lead scoring assignment

Lead Scoring 101: How to Use Data to Calculate a Basic Lead Score

Updated: August 23, 2022

Published: January 31, 2019

When most people start implementing inbound marketing, they're primarily worried about getting enough new leads in the funnel.

But once you have a lot of leads, you need to figure out who's really interested in your product and who's just starting to look around.

That's where lead scoring comes in.

What Is Lead Scoring?

Lead scoring is the process of assigning values, often in the form of numerical "points," to each lead you generate for the business. You can score your leads based on multiple attributes, including the professional information they've submitted to you and how they've engaged with your website and brand across the internet. This process helps sales and marketing teams prioritize leads, respond to them appropriately, and increase the rate at which those leads become customers.

Learn more about the concept of lead scoring in the video below.

Every company has a different model for assigning points to score their leads, but one of the most common ways is using data from past leads to create the value system.

How? First, you'll take a look at your contacts who became customers to see what they have in common. Next, you'll look at the attributes of your contacts who didn't become customers. Once you've looked at the historical data from both sides, you can decide which attributes should be weighted heavily based on how likely they are to indicate someone's a good fit for your product.

Lead scoring sounds easy, right? Depending on your business model and the leads in your database, this can quickly become complicated. To make this process a little easier on you , we're going to walk you through the basics of creating a lead score, including what data you should look at, how to find the most important attributes, and the process for actually calculating a basic score.

Lead Scoring Models

Lead scoring models ensure the values you assign to each lead reflect the actual compatibility they have with your product. Many lead scores are based on a point range of 0 to 100, but every lead scoring model you create will support a particular attribute of your core customer.

Here are six different lead scoring models based on the type of data you can collect from the people who engage with your business:

1. Demographic Information

Are you only selling to people of a certain demographic, like parents of young children or CIOs? Ask demographic questions in the forms on your landing pages , and you can use your leads' answers to see how well they fit in with your target audience.

One thing you can do with this information is remove outliers from your sales team's queue by subtracting points for people who fall into a category you don't sell to. For example, if you only sell to a certain geographic location, you might give a negative score to any lead who falls outside the proper city, state, zip code, country, and so on.

If some of your form fields are optional (like a phone number, for instance), then you also might award extra points to leads who provide that option information anyway.

2. Company Information

If you're a B2B organization, are you more interested in selling to organizations of a certain size, type, or industry? Are you more interested in B2B organizations or B2C organizations? You can ask questions like these on your landing page forms, too, so you can give points to leads who fit in with your target audience and take points away from leads who aren't at all what you're looking for.

3. Online Behavior

How a lead interacts with your website can tell you a lot about how interested they are in buying from you. Take a look at your leads who eventually become customers: Which offers did they download? How many offers did they download? Which pages -- and how many pages -- did they visit on your site before becoming a customer?

Both the number and types of forms and pages are important. You might give higher lead scores to leads who visited high-value pages (like pricing pages) or filled out high-value forms (like a demo request). Similarly, you might give higher scores to leads who had 30 page views on your site, as opposed to three.

What about leads who have changed their behavior over time? If a lead has stopped visiting your website or downloading your offers, they may not be interested anymore. You might take points away from leads who've stopped engaging with your website after a certain period of time. How long -- 10 days, 30 days, 90 days -- depends on your typical sales cycle.

4. Email Engagement

If someone's opted in to receive emails from your company by filling out an email popup , you're not sure how interested that person is in buying from you. Open and clickthrough rates, on the other hand, will give you a much better idea of their interest level. Your sales team will want to know who opened every email in your lead nurturing series, or who always clicked through your offer promotion emails. That way, they can focus on the ones who seem most engaged. You might also give a higher lead score to leads who click through on high-value emails, like demo offers.

5. Social Engagement

How engaged a lead is with your brand on social networks can also give you an idea of how interested they are. How many times did they click through on your company's tweets and Facebook posts? How many times did they retweet or share those posts? If your target buyers are active on social networks, then you might consider awarding points to leads with certain Klout scores or numbers of followers.

Free Lead Scoring Template

Worksheets and calculators to help you create a lead-scoring framework that makes sense for your business

• Establish criteria
• Customize your scores
• Evaluate your leads

Download Free

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You're all set!

Click this link to access this resource at any time.

6. Spam Detection

Last but not least, you might want to give negative scores to leads who filled out landing page forms in ways that could indicate they're spam. For example, were first name, last name, and/or company name not capitalized? Did the lead complete any form fields by typing four or more letters in the traditional "QWERTY" keyword side-by-side?

You might also want to think about which types of email addresses leads are using compared with the email addresses of your customer base. If you're selling to businesses, for example, you might take points away from leads who use a Gmail or Yahoo! email address.

Free Lead Scoring Calculator

Fill out the form to access the free template., how do you know what matters most.

That's a lot of data to weed through -- how do you know which data matters most? Should you find out from your sales team? Should you interview your customers? Should you dive into your analytics and run a few reports?

Actually, we recommend a combination of all three. Your sales team, your customers, and your analytics reports will all help you piece together what content is most valuable for converting leads into customers, which will help you attach certain points to certain offers, emails, and so on.

Talk to your sales team.

Sales reps are the ones on the ground, communicating directly with both leads who turned into customers and those who didn't. They tend to have a pretty good idea of which pieces of marketing material helps encourage conversion.

Which blog posts and offers do your sales reps like to send leads? You might find some of them telling you, "Every time I send people this certain piece of collateral, it's easier to close them." This is valuable information. Find out what those pieces of collateral are, and assign points accordingly.

Talk to your customers.

While your sales team might claim certain content converts customers, you might find that the people who actually went through the sales process have different opinions. That's okay: You want to hear it from both sides.

Conduct a few customer interviews to learn what they think was responsible for their decision to buy from you. Be sure you're interviewing customers who had both short and long sales cycles so you get diverse perspectives.

Turn to the analytics.

You should also complement all this in-person research with hard data from your marketing analytics .

Run an attribution report to figure out which marketing efforts lead to conversions throughout the funnel. Don't only look at the content that converts leads to customers -- what about the content people view before they become a lead? You might award a certain number of points to people who download content that's historically converted people into leads, and a higher number of points to people who download content that's historically converted people into customers. (HubSpot customers: Click here to learn more about running attribution reports in HubSpot.)

Another way to help you piece together valuable pieces of content on your site is to run a contacts report. A contacts report will show you how many contacts -- and how much revenue -- has been generated as a result of certain, specific marketing activities. Marketing activities might include certain offer downloads, email campaign clickthroughs, and so on. Take note of which activities tend to be first-touch conversions, last-touch conversions, and so on, and assign points accordingly. (HubSpot customers: Click here to learn more about creating a contacts report in HubSpot.)

Image Credit: HubSpot's Academy Blog

Is One Lead Score Enough?

If you have one core customer right now, a single score suffices. But as your company scales, you'll sell to new audiences. You might expand into new product lines, new regions, or new personas. You might even focus more on up-selling and cross-selling to existing customers, rather than pursuing new ones. If your contacts aren't "one size fits all," your scoring system shouldn't be either.

With some marketing platforms, you can create multiple lead-scoring systems, giving you the flexibility to qualify different sets of contacts in different ways. Not sure how to set up more than one score? Here are a few examples to inspire you:

Fit vs. Interest

Let's say, for instance, your sales team wants to evaluate customers on both fit (i.e. is a contact in the right region? The right industry? The right role?) and interest level (e.g. how engaged have they been with your online content?). If both of these attributes are a priority, you can create both an engagement score and a fit score, so that you can prioritize outreach to contacts whose values are high in both categories.

Multiple Personas

Say you're a software company that sells two different types of software, via different sales teams, to different types of buyers. You could create two different lead scores -- one for a buyer's fit and the other for their interest in each tool . The, you'd use these respective scores to route leads to the right sales teams.

New Business vs. Up-sell

As you grow, you might start to focus on up-sell or cross-sell as much as new business. But keep in mind the signals that indicate quality of new prospects and existing customers often look completely different.

For prospects, you might look at demographics and website engagement, whereas for existing customers, you might look at how many customer support tickets they've submitted, their engagement with an onboarding consultant, and how active they currently are with your products. If these buying signals look different for different types of sales, consider creating multiple lead scores.

(Note: HubSpot recently launched the ability to create up to twenty-five scores. Read more on HubSpot lead scoring here .)

How to Calculate a Basic Lead Score

There are many different ways to calculate a lead score. The simplest way to do it is this:

Featured Resource:  Free Lead Scoring Template

Manual Lead Scoring

1. calculate the lead-to-customer conversion rate of all of your leads..

Your lead-to-customer conversion rate is equal to the number of new customers you acquire, divided by the number of leads you generate. Use this conversion rate as your benchmark.

2. Pick and choose different attributes customers who you believe were higher quality leads.

Attributes could be customers who requested a free trial at some point, or customers in the finance industry, or customers with 10-20 employees.

There's a certain kind of art to choosing which attributes to include in your model. You'll choose attributes based on those conversations you had with your sales team, your analytics, and so on -- but overall, it's a judgment call. You could have five different people do the same exercise, and they could come up with five different models. But that's okay as long as your scoring is based on the data we mentioned previously.

3. Calculate the individual close rates of each of those attributes.

Calculating the close rates of each type of action a person takes on your website -- or the type of person taking that action -- is important because it dictates the actions you'll take in response.

So, figure out how many people become qualified leads (and ultimately, customers) based on the actions they take or who they are in relation to your core customer. You'll use these close rates to actually "score" them in the step below.

4. Compare the close rates of each attribute with your overall close rate, and assign point values accordingly.

Look for the attributes with close rates that are significantly higher than your overall close rate. Then, choose which attributes you'll assign points to, and if so, how many points. Base the point values of each attribute on the magnitude of their individual close rates.

The actual point values will be a little arbitrary, but try to be as consistent as possible. For example, if your overall close rate is 1% and your "requested demo" close rate is 20%, then the close rate of the "requested demo" attribute is 20X your overall close rate -- so you could, for example, award 20 points to leads with those attributes.

Logistic Regression Lead Scoring

The simple method, above, for calculating a lead score is a great start. However, the most mathematically sound method is one that employs a data mining technique, such as logistic regression.

Data mining techniques are more complex, and often more intuitive to your actual close rates as a result. Logistic regression involves building a formula in Excel that'll spit out the probability that a lead will close into a customer. It's more accurate than the technique we've outlined above since it's a holistic approach that takes into account how all of the customer attributes -- like industry, company size, and whether or not someone requested a trial -- interact with one another.

If you'd like to explore logistic regression in Excel, check out this resource . In the meantime, the manual approach above this section is a great way to get started.

Predictive Lead Scoring

Creating a lead score can do great things for your business: improve the lead-handoff process, increase lead conversion rate, improve rep productivity, and more. But, as you can see from the two methods above, coming up with a scoring system can be a time-consuming task when done manually.

Plus, coming up with scoring criteria isn't "set it and forget it." As you get feedback from your team and stress-test your scores, you'll need to tweak your lead-scoring system on a regular basis to ensure it remains accurate. Wouldn't it be easier if technology could take the manual setup and continuous tweaking out, leaving your team more time to build relationships with your customers?

That's where predictive scoring comes in. Predictive scoring uses machine learning to parse through thousands of data points in order to identify your best leads, so you don't have to. Predictive scoring looks at what information your customers have in common, as well as what information the leads that didn't close have in common, and comes up with a formula that sorts your contacts by importance based on their potential to become customers. This allows you and your sales team to prioritize leads so you're not harassing those who aren't (yet) interested and engaging those who are .

The best part about predictive scoring? As with any application of machine learning, your predictive score gets smarter over time, so your lead follow-up strategy will optimize itself.

Want to find out more about predictive scoring? Learn about this automated process here .

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What is Lead Scoring & How to Successfully Use Lead Scoring Models

You get on a call with a prospect. They listen intently to your sales pitch and seem interested in your product. But a few minutes later, you realize they are not a fit. You shrug off and call the next lead from your CRM, and then the next one, and so on. Only to realize that most prospects are low-value .

You wonder if there was a way to qualify your sales funnel leads better and save time.

That’s where a lead scoring system comes into the picture. It calls for your sales and marketing teams to assign a numerical value to each prospect to prioritize the ones that are the most likely to close.

In this article, you’ll learn how to get started with lead scoring. You’ll also discover the metrics and models you need to implement to qualify your prospects better .

By the end of reading the article, you’ll be able to input your basic scoring criteria in Close . Then, use Smart Views to filter the important leads.

Let’s get started with the basics.

What is Lead Scoring?

As we’ll see later in the article, there are different criteria for scoring leads. One of the most effective models is relying on your ideal customer’s attributes (or your buyer persona).

Top Benefits of Lead Scoring

Are your marketing team’s deemed good leads (MQL) not resulting in as many deals as you would like?

Then, integrating lead scoring into your lead management can help. Here are its top benefits:

• Align your sales team with marketing .

If a specific channel (such as SEO) results in leads with a higher score, you can plan better-converting marketing campaigns (publishing more content targeting high-intent keywords).

• Optimize the sales process and improve the productivity of your sales reps .

Every lead has a score, so you can create automated marketing workflows below certain values. For example, you may run leads below 50 through an email sequence to nurture them and explain what you sell before your sales team calls.

• Improve your sales conversion rate and overall revenue.

As your salespeople focus on the right leads, they are more likely to close deals.

A 2009 whitepaper by Eloqua , based on a sample set of 10 B2B organizations, found that implementing a lead scoring system improved close rates by 30 percent and revenue per company by 18 percent.

Curious about lead qualification? Our article on MQL vs. SQL provides valuable insights.

What is the Lead Scoring Process?

The lead scoring process involves assigning your lead's point values across various attributes (or data points) to arrive at a final score. The specific attributes could be related to a lead’s demographics, company size, professional role, industry, activity on your website, etc. An ideal prospect will rate highly across your most important parameters.

You can formulate scoring rules and implement them in a lead scoring tool based on your business needs. Close lets you add and bulk edit Custom Fields for any uncommon lead attributes relevant to your business. You can also quickly find your best leads, prioritize targeting them, and set your team up for optimum sales growth.

Lead Scoring Models and Examples

To implement a lead scoring model, your sales and marketing teams first need to agree on a common definition for qualified leads. Then, you find common and important qualities of your core customers and ones for the prospects that don’t tend to convert. Typically, it’s based on past leads data.

Your sales department might often end up with a lead that’s not yet ready to buy. To address such cases, you can assign a lead threshold before you mark a lead as sales-ready.

If a prospect is below the score, it may need lead nurturing. Typically, it calls for targeted marketing efforts, such as email sequences, before handing it off to your sales team.

Let’s look at a few common lead scoring models with example use cases around them.

Demographics

Do you ask for demographic information on your product’s landing page to test how well a prospect fits your target audience? It can include your prospect’s job title, geographic location, years of experience, company size, revenue, etc. Then, you can assign numerical values to the most important attributes.

This could be useful if you sell products in a specific zip code and can negatively score a lead outside of that zip code. Generally speaking, demographic information consists of static characteristics, so you may need to enrich it with active intent data .

Discover the key players in the intent data industry and how they can impact your conversion rate .

Company Size and Information

In B2B sales settings, a company’s fit for a product could depend largely on its size, revenue, and industry. You can ask for such firmographic data on your product landing page and leverage it to segment, score, and prioritize leads.

If you sell entirely to enterprise companies, spending time nurturing and pitching your product to small businesses would be futile. You can assign a negative score for it.

You can consider using B2B data providers to find additional firmographic data about your leads. Close CRM has an open API letting you automate lead enrichment with external tools like Lusha, Clearbit, and ZoomInfo through Zapier.

Potential Customers’ Behavior

Activity across your brand’s digital assets can be an effective way to gauge a lead's readiness to buy.

You can rate positive customer behaviors from your analysis of the actions that make a prospect more likely to convert into a deal. For instance, if a prospect taking a free trial of your product or watching a webinar is more likely to buy from you, award points for these activities.

Clicks on a middle-of-the-funnel blog post, visits to the product pages on your website, subscribing to your newsletter, or sharing their email in exchange for an ebook are other examples of a lead showing interest in your brand.

You can also score a potential customer negatively based on a behavior suggesting a loss of interest in your company. For instance, if a lead has unsubscribed from your email list or is no longer engaging with your emails, they may be less likely to buy from you.

Ideal Customer Profile

B2B companies that nail down their sales process and achieve sales goals have generally profiled their ideal customers . You will find much value in this lead scoring model if you're one of those.

You already know the demographic attributes, such as the type and size of organizations your best leads belong to. You understand the customer’s journey, leading them to buy from you, such as downloading a whitepaper on your website or visiting the pricing page. So you can score such characteristics positively on your lead.

For example, AdChina.io Analytics leverages the information about the customer’s current status related to advertising in China to score their leads. Their ideal customers are businesses selling and advertising in China. Hence, the prospects that meet this criterion score the highest.

Spam or Lamb

If you’re a small business owner and don’t know what your ideal customers look like, this model by Madkudu could serve you well. Based on firmographic data, you can use it to separate low-quality leads (spam) from high-quality ones (lamb).

You can recognize spam leads by their usage of personal email addresses and the absence of job titles (because it’s not worth sharing. Compare that with the high-quality “lamb” leads—which your sales team should focus on—that use corporate email addresses.

Depending on the size of the funnel and your product-market fit, such a simple model could serve you until you pass 100 qualified leads a day.

Social Media Engagement

The buying cycle for quality leads can often involve social media interactions with your company. So, based on prospects' retweets, likes, or other social media engagement on your posts, you can consider assigning them a positive score.

Here’s an example of lead scoring Act-On Connect offers in its Advanced Social Media Module. You can create rules to customize your scoring for any of these attributes:

Once you settle on a robust system that predicts your ideal customers, you can integrate the lead scoring methodology into your sales cycle . MadKudu lays down the stages you may run into in your lead scoring as you scale:

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Knowing the important attributes of a prospect can also help you plan a more informed marketing strategy.

However, if you have hundreds of new incoming leads in your CRM every week, scaling a lead scoring model with manual scoring is challenging. That’s where the marketing automation solution we’ll discuss in the next section comes into the picture.

What is Predictive Lead Scoring?

Predictive lead scoring is a data-driven model relying on machine learning algorithms to find demographic and behavioral patterns in your best past customers. These characteristics are then used to score and evaluate new incoming leads.

Leveraging predictive scoring, you can scale your lead scoring quickly and efficiently—relying on existing data about your leads in your CRM and other marketing analytics tools. You no longer need manual scoring criteria.

Further, the model works in real time, and its predictions become smarter as it processes more data. So you can follow up or create a more effective marketing strategy based on what’s relevant to your leads right now (as per the current state of your database).

You can start with under ten data points to build a predictive model. But to score contacts accurately, you may need thousands. Here’s how a bad model might look vs. a good one :

How to Score Your Leads

If you want to score your leads manually, below is a step-by-step process your sales team can follow. Before you implement it, calculate your average conversion rate from leads to customers that you can use as a benchmark.

Step #1: Identify the Key Traits of Your Best Customers

Is there a pattern in your highest-quality leads? Probably they:

• Come from enterprise companies
• Or from professionals who have managerial job titles
• Or have engaged with your lead magnets and signed up for a free trial of your products

Similarly, the leads that don’t tend to convert may also have a set of common characteristics, such as visiting your website's “careers” page.

To look for such traits, dig into your analytics, have conversations with your sales team, and talk with your customers to learn what persuaded them to buy from you. Close’s conversion rate sales report is a great way to start looking for the patterns of leads that tend to close vs. ones that don’t:

The customer churn report can also help find patterns in terms of the company's size, lead source, and the like.

‎You may already know some of these critical attributes if you have an ideal customer profile. You can also consider creating one following the tutorial below:

Consider multiple lead scoring models (if required)

Choosing the right attributes can be tricky, especially if you’re selling more than one product and targeting diverse customer segments. To address these different situations, you can create different models.

For instance, you may create a different model for upselling and cross-selling to existing customers than the one for acquiring new customers via your inbound marketing efforts.

Step #2: Weigh and Assign Values to Attributes

Once you have settled on the important attributes in the last step, it’s time to weigh them in order of importance.

Most lead scoring models assign scores on a scale of 0 to 100. The higher the score, the more likely a prospect will convert into your customer.

You need a healthy mix of demographic and behavioral data (both of which we have discussed before) for lead scoring. While the first one may indicate the likelihood of a lead converting into customers, the second one can show their level of interest in your company. Here’s a quick overview of both of these:

• Data points that often don’t change, such as age, role, title, industry, company information, etc., are demographic. The basic information for user segmentation—that you can mostly scrape from LinkedIn—is of this nature.
• On the other hand, behavioral information refers to actions that the leads take, such as watching a webinar, visiting your pricing page, etc. This kind of information is richer and more useful for obtaining individualized insights on a lead.

The actual point values can be based on your discretion. However, feedback from your sales and marketing department will again help weigh the important attributes (both positive and negative). Also, note the activities that tend to result in first-touch vs. last-touch conversions.

Try assigning values to different attributes by comparing their close rates to your overall close rate. For instance, there are various behaviors a SaaS company can classify as a “lead," including free trials, demo requests, lead magnet signups, etc.

Suppose the probability of closing a lead with a ”free trial” behavioral attribute is five percent compared with your overall one percent conversion rate. So, the close rate is 5x your overall close rate. Then, you can, for example, assign such leads five points.

Depending on the number of important attributes, you can assign values between five and 20 points each.

A sample scoring system for a SaaS business selling to SMBs could look like this:

• Business size :
• Under ten employees: two points
• Ten to 29 employees: eight points
• 30 to 99 employees: 19
• 100+ employees: three points
• Email newsletter signup: two points
• Free trial : eight points
• Free trial having unlocked a specific feature: 20 points
• Not responding after a demo: -5 points

Step #3: Set a Threshold for Each Stage of Your Sales Pipeline

Once your methodology is rolled out, you want your sales to recognize the marketing-qualified leads and spend time on them instantly. However, all the leads don’t arrive red hot. Some of them will need nurturing.

You can set a threshold for each stage of your  sales pipeline (or sales funnel if you prefer that method).

If you swear by the sales pipeline, here’s how the scoring can work:

• Prospecting and qualification can get upper limits of 25 and 50, respectively
• The meeting or demo stage can have a score between 50 and 75
• Proposal and negotiation could lie between 75 and 100

Once you’re ready with your model, it’s time to implement it in your sales CRM (which is Close, right?)

Step #4: Roll the Scoring Criteria in Close

You can roll out your criteria in Close using “Bulk Edit.” You can use “Custom Fields” and “Custom Activities” if the Close fields are insufficient.

What’s more?

Smart View filtering automatically updates the  pipeline view once you roll out your scoring criteria in Close.

Step #5: Stress Test, Then Refine Your Lead Scoring System

Once you roll out your model in Close, in a few months, you’ll start to see patterns between your scores vs. the real-life buying behavior of your prospects.

• You may find a lot of activity from leads whose important attribute isn’t even in your criteria,
• Or discover that certain kinds of leads with scores above 75 are not closing!

As you see such trends emerge, you can revisit your methodology and tweak your scores based on where your initial assumptions went wrong and what the current trends indicate.

Any shift in your business strategy, such as offering a free product trial or entering a new market, will entail a change in your scoring system.

Again, you can use the “bulk edit” in Close to change your criteria at scale across hundreds of leads.

(Optional) Step #6: Get Scientific With Your Model Using Data Mining (or Leverage a Predictive Lead Scoring Software...)

If you want to be precise with your lead scoring methodology, you can employ logistic regression, a  data mining  technique.

You can build its formula in Excel or Google Sheets for a more accurate score.

ActiveCampaign —which also integrates with Close —offers a lead scoring solution if you prefer dedicated software.

You can also buy predictive software such as Infer that relies on machine learning and AI.

OK, My Leads are Scored … What’s Next?

If you’re seeing many qualified leads fall through the cracks while your sales team stays busy on calls, then a lead scoring system could benefit your company.

Reaching out to a lot of leads vs. reaching out to the right ones is equivalent to the difference between being busy vs. being effective. Being the latter is more important to grow your business because you want your sales reps to close more deals.

Assigning point values to acquisition channels helps your marketing department prioritize and launch more effective campaigns. They can also launch marketing automation to nurture the leads that are not yet ready to buy. Therefore, it can better align your marketing with sales.

Lead scoring can also prepare you better for the next steps in your sales process. You’ll feel confident you’re putting time into the most interested customers. With time, you’ll know the use cases of your products for your best customers, you may find a pattern in their objections, and you can close more deals.

There are various ways to create your lead scoring system, but your ideal customers can point you in the right direction. Roll a simple criterion, taking cues from the examples I shared above, and  input them in Close . Then, keep tweaking it using real-world results to inform your scores.

Close also lets you profile, group, and score leads based on your customized criteria. You can add custom fields to every lead for their specific attributes not present in default Close fields and implement the scoring at scale across hundreds or thousands of your leads.

The Smart View also lets your team prioritize spending time with the leads most likely to close.

Want to see how an all-in-one sales CRM with integrated lead scoring can help you build your optimal sales workflow? Watch a 10-minute demo video of Close, where our Director of Sales & Marketing walks you through our most important functionalities.

You can also sign up for the software and test the features with a 14-day free trial (credit card details are not required).

ACCESS YOUR FREE TRIAL →

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Elevate Your People

Lead Scoring: The Ultimate Guide to Understanding and Implementing it for Sales and Marketing

What's in this article?

Lead scoring is meant to simplify the sales process, and yet doing it well has felt increasingly complicated and out of reach.

We’re here to explain how lead scoring can finally be made manageable, efficient, automated, and accurate in our ultimate guide to lead scoring.

Beyond a basic understanding of what is lead scoring as well as your lead scoring tools and options, we’ll also give you the concrete information you need to implement lead scoring successfully as the options for managing it continue to grow.

Use it to actually increase conversions and boost lead scoring ROI within your industry in a way that best fits the needs of your organization.

Optimize all of your lead management data to discover missed sales opportunities. Schedule a free ProPair demo to see how to reach your full potential.

What is lead scoring?

Lead scoring is the system that sales and marketing teams use to assign value to each new prospect, which is meant to determine how important the lead, or potential customer, is to the business. By ranking each lead based on various data points, sales and marketing gain clarity on which leads to engage and when, prioritizing leads based on the probability of each one turning into a sale.

The lead scoring definition is changing

Past lead scoring was done by manually setting stagnant rules with weighted lead features and making assumptions about what qualifies a lead. New lead scoring innovations use artificial intelligence and machine learning to more accurately predict outcomes with each lead automatically and in real-time.

Fortunately, new predictive lead scoring optimizes how businesses rank, prioritize and work leads. Rather than basing lead values on best guesses set with rules that easily become stagnant, AI/ML lead scoring now provides predictive values for each lead to offer intelligent decision support. This empowers sales and marketing teams with accuracy that wasn’t previously possible. 

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Why you need new lead scoring methods to stay competitive

Pay attention to new lead scoring models. They’re advancing the first step in lead management, taking lead scoring from clunky and static and transforming it into an efficient and revenue-increasing process — all with the use of artificial intelligence and machine learning.

That may be one reason marketing and sales departments prioritize AI technology and machine learning for their success more than any other department (40%).

How predictive lead scoring applies to your business

Whether you buy thousands of leads, invest in marketing to generate them or a combination of both, you’re paying for sales opportunities and receiving thousands of rich data points.

However, if lead data is not evaluated, and instead left static, it’s going to underperform. Manually setting and updating lead scoring rules is difficult to manage accurately as the market, your campaigns and products or services evolve. Either way, you’ll close far fewer sales than expected.

Lead scoring is a concrete way to avoid the pitfalls of managing this data. Thousands of complex data points are combined into one lead scoring model, which provides intelligent decision support to increase conversions.

Data quality issues have challenged businesses for years. Now 48% of companies use data analysis, machine learning, or AI tools to address data quality issues.

Learn more about how high-quality lead scoring impacts business: The Ultimate Guide to Optimizing Lead Scoring and Growing Business .

Why is the shift in lead scoring important now?

CRM and lead management systems driven by static rules and reports, are quickly becoming obsolete and creating challenges for organizations that need improved lead scoring ROI from quality lead management.

New and improved lead scoring models are becoming the norm, allowing businesses to optimize lead scoring with lead management innovations . In 2021, 86% of CEOs said AI is mainstream technology in their office, and this is expected to grow.

Integrated, intelligent and predictive AI lead scoring boosts the performance of each lead and each salesperson.

AI is improving how we manage sales and marketing overall

According to the 2022 McKinsey Global Survey on AI , revenue increases from AI adoption are most often reported in these business functions:

• 70% of respondents reported revenue increases in marketing and sales
• 70% said product and/or service development
• 65% said strategy and corporate finance

For comparison, in 2018, manufacturing and risk were the two functions where the largest set of respondents reported value from using AI. 

In addition, optimization of service operations has taken the top spot for AI use cases, with product/service development and marketing and sales following closely behind.

Increase conversions from the start with AI lead assignment. Download our free guide to learn more.

How does lead scoring optimize sales?

Lead scoring is an important part of any good lead management system, helping marketing and sales teams quantify how qualified leads are.

This ideally informs good decisions about how to prioritize, distribute and work each lead to focus on sales-ready leads that will increase conversions.

To optimize sales with lead scoring, lead data needs to be collected, and (here comes the tricky part) it needs to be interpreted accurately to assign value to each lead as they’re generated and as they continue to engage with your organization.

These scores then translate to the next steps, like pushing a lead further down the sales funnel with a new email campaign or distributing the lead to a salesperson to reach them with a phone call or text.

Having leads is only valuable to your business if you can convert many of those leads into customers. Accurate lead scoring is one of the first steps that will optimize sales conversions.

Read on to learn the different ways to manage and optimize lead data and lead scores.

Lead scoring alignment between sales and marketing

One important piece that makes lead scoring successful is setting agreements between the marketing and sales teams within an organization. This includes clearly defining concepts like what makes a lead qualified, how to identify qualified leads and what to do next with those leads.

What is an MQL compared to an SQL?

To help organizations prioritize which leads are most likely to convert, leads are first qualified as a Marketing Qualified Lead and then ideally move on to become a Sales Qualified Lead.

• A Marketing Qualified Lead (MQL) is someone who has shown interest in an organization’s product or service as seen through their engagement with various marketing efforts. The marketing team considers leads based on certain criteria. Once a lead is qualified, it gets passed along to the sales team to engage further.
• A Sales Qualified Lead (SQL) is a lead that is a prospective customer who has shown they’re ready to buy or close a sale. Sales Qualified Leads typically have gone through a few levels of qualification before reaching the sales team and being contacted by a salesperson. They’re likely first deemed Marketing Qualified Leads (MQLs) after various engagements with marketing efforts.

So an SQL vs an MQL can show the difference between how engaged a lead is and how sales-ready they are.

How to qualify leads with lead scoring

Lead scoring is used to help sales and marketing teams determine a value or threshold that would deem a lead qualified.

The challenge is that there are many factors that impact how someone might qualify a lead. To do this well — without bias, assumption, or human error — requires the use of deeper analytics. 

Beyond the lead data itself, you also need to have an understanding of the marketing and sales team’s goals, capabilities, and capacity.

When it comes to lead scoring, organizations will have the most success if they support both their marketing and sales teams, allowing them to be aligned in their goals and have access to tools that will set them up for success.

Accurate lead scoring capabilities will ultimately help to measure the overall performance of the sales cycle for customers from start to finish.

Optimize your sales and marketing efforts. Check out our Guide to Lead Management: How to Convert More Leads into Deals .

5 reasons to use a lead scoring model

1. You waste leads without it

Without a reliable system for scoring leads, your sales team is likely making assumptions about leads that flow in.

Whether they think leads have aged out, aren’t qualified, or are just flat-out unresponsive, chances are, a deeper look at your lead data would reveal several missed sales opportunities because sales activities aren’t currently data-driven.

2. Increase revenue without a system overhaul

Leads flow in and the marketing and sales team do their best with the support they have. But what if your leads, marketing and sales teams could be optimized with one simple upgrade?

A high-quality lead scoring model increases efficiency, impacting all aspects of working leads, including what leads are generated or acquired, how they’re prioritized, and what sales actions are taken to convert them. When each of these steps is improved, ROI increases as your business closes more sales, more efficiently.

3. Optimize all your sales and lead data

You’re already collecting thousands of lead data points and you have access to performance data for your sales and marketing teams. But this data is overwhelming to manage.

A lead scoring model makes use of this data as leads flow in, prioritizing them, ranking them, and distributing the best leads to the right team members, at the right time.

4. Align sales and marketing teams using data

With data-driven lead scoring models, sales and marketing teams reach common ground based in fact, rather than assumptions, theories or static rules.

Lead scoring models rank leads, so there’s no more guessing as to what makes a qualified lead. Sales and marketing teams are empowered to move forward, doing what they each do best to engage and convert those leads.

5. Work your current leads, smarter

With a data-backed lead scoring model, your business can prioritize the right leads at the right time and engage them in the best ways possible.

Taking the guesswork out of this initial step in lead management launches the entire lead flow successfully. Customers and your sales and marketing team have an improved, more efficient and intelligent experience.

What is a lead scoring model and how do you establish one?

A lead scoring model is a structure that is used to establish and maintain a lead scoring strategy by providing the framework used to assess the value of each lead based on a variety of criteria.

With the best lead scoring models, organizations are able to quickly identify qualified leads and prioritize how to work them as they come into the organization’s CRM system.

How to use a lead scoring model to determine the value of leads

Within a lead scoring model, different criteria are established for assigning values or points to each lead to determine how qualified it is. These different criteria can clue sales teams into how ready-to-buy a lead may be.

This could include:

• Lead behavior: Assess how leads engage with you through website visits, downloads, forms filled, phone calls, emails opened, etc.
• Lead demographics: Assess who leads are by evaluating how qualified they are based on information including their location, job title, age, industry they work in, income, etc.

Beyond information from the leads, assigning value to leads could also be based on thresholds that are set to measure leads by. As we mentioned, this could include goals and metrics set by marketing and sales teams.

For example, the marketing team might launch a new campaign to attract leads for a specific product. Leads of certain demographics, showing certain behaviors within that campaign, may hit a point that the marketing and sales teams deem more ready-to-convert than other leads. Those leads would then be prioritized and contacted by salespeople.

Lead scoring models can measure and assign value to leads using different methods and criteria. Different lead scoring models will get you different results.

Each model relates to specific criteria chosen to reach certain sales and marketing objectives.

Learn more about lead scoring models: How to Develop a Lead Scoring Model, and Why it Matters

The challenge with traditional lead scoring models

There are various tools available to set the foundation for a lead scoring model.

For example, many lead scoring tools are built on the concept of assigning values based on various behaviors. With this, the goal is to prioritize the leads with higher scores or that meet other thresholds such as being sales or marketing qualified. This assumes that these values are what make a qualified lead.

Tools like this do a great job of helping sales teams make sense of lead data that flows into their systems. But how do we know the criteria they use to evaluate leads, the basis of the model, will best determine what makes a lead convert to a customer?

This is where predictive lead scoring improves these models and ensures that the data-driven criteria being measured are analyzed and acted on in the most accurate way possible, improving performance across the board.

Beyond tracking and assigning value to lead data, predictive lead scoring tools establish a lead scoring model based on criteria that are established using artificial intelligence.

Machine learning continuously learns and updates its models over time, making the criteria by which leads are scored more accurate than was ever possible before.

Get ahead of the competition. Read: Why AI Lead Scoring is Essential for Raising Conversion Rates .

Predictive lead scoring innovations

To help organizations not only make sense of their leads but actually optimize them, technology has advanced to offer innovative AI lead scoring and lead management tools.

Managing thousands of data points by hand has never been realistic. Although some tools have made it easier, nothing compares to the advances that have come with the application of artificial intelligence and machine learning.

See how AI can impact how you work your data. Try ProPair’s free data analysis.

Artificial intelligence and machine learning

When you hear about AI, you might associate the term with technology that mimics human behavior. Have you thought about how this could positively impact operations at your organization?

For businesses, AI software supports tasks like automation, engagement, and quick data analysis. All of these simplify many repeatable processes we use, making them more efficient than ever.

Machine learning is one of the many subsets of AI. It’s also one of the most practical applications of AI in business because it’s fairly straightforward in how it performs.

It uses algorithms that allow for input of data (like analysis of leads) and output of data (like predictions and decision-making to help you prioritize and take action with those leads).

So you can see how these would present an innovative solution for ongoing lead scoring needs.

Learn more with our Simple Guide to Optimizing AI/ML for Business Operations .

Keep things moving with lead management automations

You’ve also likely heard about, and may be using, various types of automations within your business that impact stages of the sales funnel.

This alone is a helpful evolution to manage repeatable sales and marketing tasks like data entry and activity logging. But it makes lead management even better with AI/ML applications.

With basic automations , as leads begin to engage, marketing teams automate steps like sending emails and tracking lead behaviors. And as leads become SQLs, sales teams automate scheduling calls, leaving voicemails, and sending emails to each of them.

AI/ML takes automation a step further , helping marketing and sales teams avoid inefficiencies and conflicts between the two teams.

With AI/ML software, sales and marketing teams get predictive insights to inform their decisions and next steps. As these predictions are based on the accuracy and intelligence of machine learning models, miscommunications, assumptions, and human error are removed from the equation.

Intelligent and predictive tools automatically score, distribute, prioritize, and communicate with leads as they flow in as MQLs and move to SQLs. And they do it with intelligent accuracy that isn’t possible when done manually.

Sales Automations backed by AI/ML software are changing the game for sales ops. Learn more: Automate Moving MQLs to Sales Qualified Leads with AI/ML Solutions .

5 features a lead scoring model should have

1. it’s predictive.

A lead scoring model without predictive artificial intelligence and machine learning is a model built on assumptions, theories, and stagnant rules no matter how strategic you are with establishing it. AI lead scoring, however, relies on analyzing thousands of lead data points to provide lead scoring that is not only accurate but actually built on complex informed predictions of what makes a lead more or less ready to close a sale. And these predictions update in real time as lead data changes.

2. It measures implicit and explicit data 

Your lead scoring model should be able to take all lead data you have and turn it into useful insights, this includes tracking and analyzing both lead behavior and facts about the lead. implicit data, and explicit data.

Implicit data is tied to behavior, showing engagement with your marketing and website, for example. Explicit data is information that leads provide or that is accessible information about the leads such as their income, job title, home value, etc.

3. It tracks and notifies as lead scores change

It’s not enough for a lead scoring model to evaluate a lead only when it first arrives in your system. High-quality lead scoring continues to track and measure your leads’ behaviors and data, providing you with ongoing information that informs whether a lead is becoming more or less sales-ready. 

4. It supports the next step in the funnel

The value a lead scoring model assigns to each lead needs to be actionable. It needs to be clear to your sales and marketing teams what various lead ranks mean. From there, when a sales agent is notified of a change to a lead’s score, for example, they will be empowered to take their next sales action with that lead and keep them well prioritized with other prospects they’re working. 

5. It benefits both marketing and sales objectives

As lead scoring involves tracking lead data that impacts both marketing and sales efforts, the lead scoring model needs to be reliable enough for both teams to trust. The more intelligent and data-driven the model is, the better informed sales and marketing will be. This also prevents any disagreements over deciding on certain thresholds or criteria used to measure leads, as it is all instead driven by concrete data.

Use direct mail to reach leads? See how AI guides your campaigns to get results with our free download.

Lead scoring tools

To optimize lead scoring in action there are many robust, production-ready lead scoring tools available.

They differ in various ways, including how they integrate with your CRM, what lead scoring model or models they use, how customizable the models are and whether lead scoring is predictive or not.

These different factors can impact which platform will help your sales team increase conversions. We’ve summarized a few of the popular tools to give you an idea of the options available.

Check out our Guide to Predictive Lead Scoring and Why Most Lead Scoring Models are NOT Predictive.

ProPair’s AI/ML software offers predictive lead scoring with decision support through its products RANK, MATCH and MIX.

Not only does ProPair use machine learning to analyze your historic lead management data and immediately begin scoring leads, but it’s also customized to your current system, your goals and your business. This allows ProPair’s clients to lift conversion rates without changing anything in their current system.

How it works

ProPair runs in the background to analyze 30+ lead dimensions as leads come in, providing  you with immediate predictive values, customized to provide strategic decision support.

With ProPair RANK you’ll see exactly which leads to focus on and when, using one data-driven lead value ranking system designed to help sales teams prioritize leads, guide follow-up activities, and revisit abandoned opportunities.

Bonus: A broader impact on overall lead management 

In addition to lead scoring, ProPair also supports other aspects of lead management to increase conversions with AI/ML tools for sales agent scoring and lead distribution.

ProPair MATCH shows exactly what sales agents to assign leads to. This turnkey solution leverages historical sales team performance data and machine learning technology to equitably get the right leads to the right sales agents and make the most of your current team as it stands today.

ProPair MIX maximizes the potential of every lead and every sales agent, bringing together the best of predictive lead scoring and predictive sales agent grading.

It allows you to optimize your entire sales operation and equitably distribute leads from top to bottom performers, maximize sales production, and reduce the need and expense of churning your salesforce.

Get more from each lead with AI-powered lead nurturing. Download our free guide here.

Hubspot Marketing Hub offers predictive lead scoring software that works with your CRM to apply machine learning, which allows for reviewing thousands of data points across your contacts to identify your best leads.

Hubspot’s lead scoring model evaluates leads based on their behavior and engagement, demographic information, relationship within Hubspot and logged interactions within the CRM. From there it uses artificial intelligence to optimize lead scoring over time.

As you collect more data, Hubspot’s model improves itself providing better-informed predictions. It categorizes leads using “Likelihood to close,” which shows the probability of a contact closing within the next 90 days. The “Contact priority” feature uses “Likelihood to close” to filter segments of your best and worst leads. 

To collect enough significant data to see Hubspot’s “Contact priority” assigned to leads, Hubspot says you’ll first need to reach 100 contacts. 

Bonus: A wide range of lead scoring possibilities

Hubspot includes features like creating different score sheets for different audiences or leads for sales and marketing teams. It allows for up to 25 unique lead scoring models to help manage various products, locations, industries, etc. And there are options to customize how leads are scored to make it work for your team.

Marketo Engage offers Lead Scoring Model as part of its marketing automation platform.

Its model categorizes users as leads, which have the potential to become customers, or prospects, which are customers who have engaged and showed interest in products.

With Marketo’s model, leads scored above 65 points are considered sales-ready. Leads below that point are either unqualified or may need to be engaged and analyzed differently to increase their lead score. This threshold can also be customized for your organization so sales and marketing will need to align on what that threshold should be.

Marketo combines explicit (demographics, basic lead information) and implicit (behavior and actions leads take to engage with your marketing) information to score leads. Note, this data needs to be kept up to date for accurate lead scoring.

Bonus: A focus on engagement to support sales and marketing

Marketo Engage’s focus is in the name — engagement. Beyond its Lead Scoring Model, Marketo Engage also makes it easy to track lead behavior in one central hub so you can optimize how you connect with leads. Push content by creating journeys that use the right channels, to reach leads at the right time, optimizing engagement.

Salesforce offers various ways to integrate lead scoring into your lead management. As a CRM it integrates with other tools like Hubspot and Marketo to take full advantage of their lead scoring capabilities within Salesforce.

There are also extensive options to customize your use of Salesforce using workflow rules to establish lead scoring methods within the platform. Because this takes time to develop and advanced functionality to execute well, Salesforce also offers a more ready-made solution for lead scoring — formerly Pardot, now called Marketing Cloud Account Engagement.

Using Account Engagement, it’s easy to attach lead scoring actions and data to each lead within Salesforce. In addition, Salesforce offers Einstein to take lead scoring further by making it predictive.

How Account Engagement (formerly Pardot) works

Account Engagement is a marketing automation platform that allows you to define the value or weight of various lead data and actions within the sales funnel.

A lead scoring model is built based on actions leads take on your website and through your email campaigns.

Based on chosen values, when a lead reaches a certain threshold, sales and marketing teams then define the lead as qualified. Once a lead is deemed qualified, the sales team can prioritize working the lead.

Account Engagement’s lead grading system compares how well each lead matches to your Ideal Customer Profile (ICP). It automatically analyzes lead’s basic data to assign a letter grade of A through F to each lead.

To determine if a lead has a high grade, and is, therefore, worth prioritizing, it assesses demographic information, such as the lead’s location, revenue, job title, industry and company size. In addition, it can also track technographics, including CRM usage, marketing automations, etc.

Account Engagement (again formerly Pardot ) also makes it possible to assign value to various actions, including web page visits, links clicked, downloads, email open rates and clicks, searches and other engagement with marketing campaigns and materials.

Bonus: Einstein adds predictive lead scoring

To take lead scoring further, Salesforce offers Einstein as an add-on to offer a predictive lead scoring approach.

Using existing lead data, Einstein’s AI software finds data points that have pointed to a lead’s successful conversion. With this information, it automatically shapes the model that leads should be scored by. The higher it scores a lead, the more likely that lead is to convert.

Einstein scores each lead by using a correlation between new lead attributes and those of historical leads.

It offers tools including Discovery to show relevant patterns in your data and AI insights, Prediction Builder to predict business outcomes and create custom AI models using fields or objects, and Next Best Action to deliver recommendations and action strategies.

Ready to start increasing sales? Check out our Guide to Implementing AI/ML for Executives in Sales Operations .

Lead scoring examples and use cases

Lead scoring is used across industries to improve the process of selling a product or service. 

Let’s dig into a few real-world examples of how lead scoring can be tailored to improve marketing and sales operations within industries that benefit most from quality lead scoring.

The universal basics that apply across industries

As we’ve mentioned, lead scoring involves collecting both implicit and explicit data on leads, from tracking their basic demographics to their behaviors in engaging with your organization. 

Across industries, there are various data points you can collect to improve how you score and manage leads once they’re generated . This is often done well through a lead-capture form on your website, or other marketing tools, that asks leads to provide information about themselves.

In addition to this, lead scoring can be supported by pushing targeted marketing campaigns to attract and nurture leads, which you can use to measure their behavior as they engage with the materials.

All that said, depending on the lead sources and lead scoring tools you use, you may not need to stress about how you’ve structured collecting lead data through these tactics, as the tools and sources might offer helpful support.

Make the most of the leads you already have. Download our free guide to maximize aged leads with AI.

Mortgage lead scoring

Mortgage lenders will want to collect data that specifically helps loan officers gauge whether a lead is ready to close a loan. This includes data like credit score, income, debt amount, home value, etc. These data points should also be considered in relation to several different loan programs the lender offers.

One aspect of scoring these leads and deciding how to prioritize and work them may be to distribute them to the loan officers who connect best with leads in that location or with certain levels of credit score, for example.

Beyond the lead data, lenders may also want to provide marketing materials and campaigns that meet a lead where they are in the process of considering a home loan and help to move them along the path to be ready to close. This could include blogs and emails aimed at educating homebuyers and homeowners about their financing options.

With intelligent, predictive lead scoring, all of these various factors — between leads, sales agents and relevant loan products — can be combined using machine learning. Rather than assess one or two dimensions, many complex dimensions can be summarized into simple predictive values that help lenders prioritize and work each lead.

Read More: Perfect Your Mortgage Lead Scoring and Sales Process with Intelligence Technology .

Financial services lead scoring

Beyond mortgage lenders specifically, various financial services organizations benefit from effective lead scoring. This includes banks and lenders providing student loans, auto loans, other personal loans, business loans and debt consolidation.

In this case, qualifying a lead may also be based on their credit score, as well as income, debt, financial needs/goals, and where they are in the process of looking for a financing solution.

All of these points allow you to prioritize a lead based on how ready they are to move forward with the solution you have to offer.

Leads can be attracted and nurtured through various marketing efforts that show the benefits of your loans compared to other options, as well as how the financing can be simple and affordable.

Predictive lead scoring provides decision support to help you know what sales activities to take with each lead depending on the above factors.

Insurance lead scoring

Whether you’re selling health insurance, Medicare, life insurance, auto insurance or any other form of insurance, you have certain criteria that leads need to meet before they’re ready to get started.

Depending on the type of insurance you offer, this could include collecting specific data such as a lead’s current state of health, their budget for insurance premiums, their current policies, if they’re the decision maker, etc.

As for helping to attract and move leads through the sales funnel, educational resources that build trust and show the need for the insurance you offer can be helpful to advance their decision to commit to your offering.

Predictive lead scoring models help you get familiar with each lead as they flow in, using the many dimensions needed, customized to your business.

Prevent customer churn and optimize relationships with AI portfolio retention. Learn more with our free download.

How to make your lead data work for you

Depending on how hands-on you want to be and other considerations of your current lead management system, there is likely a lead scoring tool available that could help you sustainably prioritize which leads are sales-ready.

The beauty of tools based specifically in AI/ML capabilities is that lead scoring becomes completely automated while also being continuously updated so that the automations are also intelligent and more accurate than a manual system could ever be.

With machine learning, lead scoring relies on the accuracy of data and the machine’s ability to analyze and learn from thousands of data points in an instant. 

With an option like ProPair specifically, your historic data and newly generated lead data don’t have to be perfect for you to see results. We help you adjust our model to best fit the needs of your organization overall, your sales team and your leads.

With our customized solution, you apply predictive lead scoring to your organization’s unique sales processes, lead buying, culture, and sales agents.

AI/ML lead management support can take what your organization already has in place to transform your efforts into creating the best version of you.

The top three most significant challenges companies face when considering the implementation of AI are staff skills (56%), the fear of the unknown (42%) and finding a starting point (26%). ProPair eliminates these concerns with its turnkey solution that runs in the background of your current system to support your sales and marketing teams.

Optimizing sales has a direct impact on revenue. Learn more in our Guide to Optimizing Your Revenue Operations .

Lead Scoring FAQ

What is a lead scoring model, why should i use lead scoring.

When you invest in buying or generating leads, you pay for sales opportunities and receive thousands of rich data points. However, if lead data is not evaluated, and instead left static, it underperforms.

Lead scoring tools help you avoid the pitfalls of managing this data. Especially now, with complex models available that measure thousands of data points and combine them into one predictive lead scoring value, such as predictive lead scoring with artificial intelligence. 

Does lead scoring improve sales?

Having leads is only valuable to your business if you can convert many of those leads into customers. Accurate lead scoring is one of the first steps to optimizing sales conversions as leads flow into your lead management system.

Lead scoring helps marketing and sales teams quantify how qualified leads are. This informs how to prioritize, distribute and work each lead to focus on sales-ready leads that increase conversions.

What criteria are used for lead scoring?

Lead scoring models use both implicit and explicit lead data to measure the probability of a sale.

Implicit data includes lead behavior such as assessing how leads engage with you through website visits, downloads, forms filled, phone calls, emails opened, etc.

Explicit data includes lead demographics. This involves assessing who leads are by evaluating how qualified they are based on information including their location, job title, age, industry they work in, income, etc.

What score makes a lead qualified?

This depends on many factors relevant to what makes a lead most likely to close a sale with your business. Scores are often weighted based on thresholds your marketing and sales teams agree to, such as what makes a sales or marketing qualified lead. These thresholds could be determined with help from predictive lead scoring tools that make sense of complex lead data.

What are the challenges of implementing lead scoring?

One big complication is relying on a lead scoring model or models that don’t serve your ongoing needs. A high-quality lead scoring system needs to measure leads readiness to buy as soon as they flow in, but also continue to measure how qualified leads are to make a sale as their behavior and demographics change over time.

This means you also need a system for ongoing tracking of lead data and a way to be alerted and take action once a lead becomes more or less valuable. With the right lead scoring system, you can wrangle complex lead data and optimize how you prioritize and work leads based on their scores, without having to manually update the model.

What are the limitations of lead scoring models?

Lead scoring models at their most basic are only as good as the data being fed into them and the criteria set for measuring that data. Using lead scoring models can be difficult when large amounts of data are managed manually and criteria are set based on assumptions or theories.

The limitations are fading however, with more intelligent models. Now through various tools, including predictive lead scoring, both data analysis and criteria measuring are done using algorithms. Artificial intelligence has transformed lead scoring into a data-driven tool that uses machine learning to continuously improve the predictive values used to score leads.

Boost ROI with strategic and intelligent lead scoring

Before reading this ultimate guide, you might have thought that lead scoring was one small part of your overall lead management system.

However, we hope you’ve come to understand that qualifying leads is actually the first step that impacts how you make use of the leads you’ve spent time and resources to generate or buy, as well as how you move those leads through to become customers.

With effective lead scoring and using the right lead management tools for your organization, you can boost lead scoring ROI and increase conversion rates.

How could your business grow with intelligent lead scoring?

Schedule your ProPair demo now to learn more.

Using machine learning to analyze your current leads and their performance, we’ll help you see where you’re missing sales opportunities.

We’ll also share the options you have for improving lead scoring, sales agent performance and overall conversion rates with our production-ready machine learning software.

How does AI increase sales?

Download our executive guide to understand the current state of AI and machine learning. We’ll show you how innovative sales and marketing organizations use it to get ahead of their competition.

Further Reading

Forecasting Success: The Impact of Predictive Sales Analytics on Future Sales Strategies

Transforming Sales Dynamics: How AI Automation Tools Are Changing the Game

Top AI Marketing Tools to Watch in 2024

Guide to Lead Scoring for B2B Sales [Models + Best Practices]

Casey O'Connor

What Is Lead Scoring?

Why is lead scoring important, how to score leads, 7 lead scoring models , lead scoring best practices, predictive lead scoring explained.

Lead scoring is the process of assigning numeric value to a predetermined set of leads’ demographic and behavioral characteristics. 

The process of lead scoring helps sales reps rank and prioritize leads according to who is most likely to make a purchase. 

Lead scoring also helps improve overall lead quality, which translates to improved pipeline and bottom-line metrics. In fact, teams that implement lead scoring have reported over 75% improvement in lead generation ROI.  

In this article, we’ll go over everything you need to know about lead scoring, including how to do it, why it’s so important for sales teams to master, a few models to consider, and some best practices. 

Here’s what we’ll cover: 

• 7 Lead Scoring Models

Lead scoring is a sales and marketing strategy in which the two teams collaborate to assign numeric values, or scores, to individual leads based on their individual or company attributes, as well as their specific behavior throughout the sales process . 

Scoring leads helps marketing and sales teams prioritize how they spend their time and resources. Generally, the higher score a lead generates, the more likely they are to ultimately (and, in many cases, imminently) purchase your product. The score lets sales reps know whether a lead is worth their time and effort. 

Leads are scored according to a variety of attributes, characteristics, and behaviors, including (but not limited to):

• Company size
• The lead’s role at the company
• Time spent by the lead on various pages of your website and/or number of website visits
• Email subscription status
• Lead source

This data, and the subsequent scoring process, helps sales reps determine who to contact first (and who, perhaps, can be disqualified entirely and isn’t worth contacting at all). 

Sales reps and marketers generally gather buyer data from two main categories : explicit lead scoring and implicit lead scoring. 

Explicit Lead Scoring

Explicit lead scoring data is objective information that comes directly from the lead, or is otherwise accessible to the public. 

This data generally includes demographic and firmographic information, such as: 

• Job title/role
• Level of seniority /experience in the industry
• Company revenue
• Geographic location 

Savvy sales teams leverage technology or sharp research skills to find this data themselves, instead of asking leads directly. This allows them to dedicate more of their conversation time to high-value, open-ended questions that help them tailor their pitch to the lead’s unique needs. 

Implicit Lead Scoring

Implicit lead scoring data can be a bit more challenging to gather and/or interpret because it’s more subjective.

When it comes to implicit lead scoring, sales reps analyze a lead’s behavior and assign values based on their observable actions. 

Implicit lead scoring is based on both active and passive buying behavior, including (but not limited to): 

• Downloading gated content like white papers or case studies
• Visiting your website (assigning different scores for visiting specific pages and/or higher scores for more frequent visits)
• Opening, clicking through, or otherwise engaging with your emails and attachments 
• Watching product videos, demos, or webinars
• Interacting on your social media profiles 
• Subscribing to your email list or newsletter
• Submitting a contact form

Each behavior earns leads a certain number of points. Visiting the pricing page, for example, is usually indicative of serious intent to purchase — this might earn a lead more points than visiting the home page. 

On the other hand, a visit to the career page usually indicates the visitor isn’t interested in buying — this behavior may actually earn them negative points, depending on your team’s specific value assignments. 

Implicit lead scoring relies on a sales team’s ability to track and interpret leads’ behavior in real-time. This sounds straightforward, but when you multiply the number of attributes each lead demonstrates times the number of leads in the pipeline , it can get complex in a hurry. Lead scoring technology can be a huge help with this. 

The lead scoring process takes a bit of legwork to get off the ground, but the benefits can be significant. 

Encourages Team Alignment

Creating a lead scoring process naturally improves communication and alignment between the sales and marketing teams ; part of the process includes the two teams coming together to mutually agree on what defines leads who are worth a sales rep’s time, and what indicates a lead is best eliminated from the funnel. 

This alignment improves overall working conditions and makes lead-handoff (i.e., the transition from MQL to SQL) a more seamless process. 

Improves Sales Performance

Lead scoring helps sales reps waste less time on poor-fit or not-ready leads, which improves sales productivity , close rates, and overall sales performance. The process enables sales reps to spend their time and resources on the leads that are most likely to convert. 

Lead scoring also helps sales reps determine which leads could eventually be a great fit but are indicating that they may need more time. Once these leads are identified, they can receive lead-nurturing content that will keep them engaged until they’re ready to buy. 

This is as important as targeting the best-fit leads who are ready to buy very soon — research shows that a solid lead-nurturing process can increase close rates by 30% .

Enhances the Buyer Experience

Lead scoring ensures that only good-fit leads move through the sales funnel ; it cannot be understated how significantly this will impact the impact of your sales funnel. 

Research shows that most sales teams only manage a 25% legitimacy rate when it comes to lead generation, and only 21% of that small number will ultimately convert to a sale. 

By avoiding poor-fit leads through effective lead scoring, sales teams can target their methodologies, strategies, and tactics to the needs of the true target buyer. 

Tracking your lead scoring efforts will also help identify which marketing and sales campaigns are most effective, so you can further refine your strategy and content around what resonates most with your well-qualified buyers. 

Boosts Financial Metrics

In addition to its many other benefits, lead scoring also improves the bottom line. Studies have shown that an effective lead scoring process can boost a company’s annual revenue by up to 50% .

It also helps optimize marketing spending and lower customer acquisition costs (CAC).

Lead scoring impacts every team and organization differently, depending on their unique goals and sales process. That being said, it can be especially beneficial for products with longer sales cycles , higher price tags, or other complexities. 

The specifics of your lead-scoring process — that is, the set of demographic, firmographic, and behavioral attributes you look for and the values you assign to each — will vary according to your unique sales process and the needs and buying behavior of your target market. 

That being said, there is a framework that most teams can follow to get their lead-scoring process off the ground. 

1. Clean Up and Analyze Sales and Marketing Data 

Lead scoring is a data-driven process. When you’re just starting to figure out how to score leads for your business, start with your historical sales data. Sales and marketing should sit down together to do this work. 

Start by calculating the current conversion rate for all leads; this will be your baseline for lead scoring. You can find this number by dividing your number of new customers by the total number of leads your team generates. 

You’ll also want to look further than the numbers. 

What traits do your most successful customers have in common? What behaviors did they exhibit right before they closed? Are they all from a common demographic? 

Look for common threads or unifying factors that can help define what a great-fit lead who ultimately goes on to become a profitable customer looks like for your company. 

2. Talk to Sales

You’ll also want to interview sales reps directly to find out their perspective on the same questions. 

How would they describe leads that turn out to be most fruitful? What types of interactions move the needle most with good-fit buyers? How many touchpoints, on average, does each sale require? What website pages indicate seriousness in a buyer? Which job titles are held by the decision-makers ? 

Sales reps can offer insight into all of these questions, which will help your team make decisions about how each behavior should be weighted and scored. 

3. Talk to Customers

The last stop on your data-mining tour should be your customers themselves. 

Much like sales reps, happy customers can be a wealth of insight into which parts of the sales process were most influential for them, so you can help interpret similar lead behavior accordingly.

For this step, make sure you interview customers who completed both long and short sales cycles. 

4. Define Your Attributes and Values

Once you’ve gathered all of the hard metrics and anecdotal data, you can start to build a profile of what a great-fit buyer looks like and how they behave as they become readier to buy. 

List out these attributes as specifically and tangibly as you can. Sales and marketing should both be involved in the process of weighing and assigning value to each characteristic and behavior. 

5. Automate When You Can

Lead scoring is pretty straightforward on the surface, but it can get complex in a hurry when you consider the number of leads in the pipeline at any given time. Lead scoring software can add automation to the process to help streamline your efforts. 

Lead scoring models help ensure that the score you assign to each lead reflects the actual compatibility they have with your offer and how likely they are to buy. 

1. Purchase Intent Model

A purchase intent lead scoring model helps sales reps estimate a lead’s probability of converting. 

Purchase intent data can be gathered from first-party or third-party sources. 

This model helps sales reps pinpoint where each lead stands in the sales process and how to strategically approach each according to their behavior. 

2. Demographic Model 

A demographic model focuses heavily on the characteristics of your target buyer. 

If, for example, you sell an elementary-aged app subscription, you would assign a higher score to a parent of a seven-year-old than to a parent of a middle-schooler, and a non-parent would receive the lowest (or even a negative) score. 

A demographic lead scoring model is very effective at filtering out outliers from the sale process. 

3. Firmographic Model 

A firmographic lead scoring model revolves around company characteristics. 

4. Online Behavioral Model

Pricing pages, for example, usually carry more weight because leads who visit this page are interested enough in your offer to want to know how much it costs. Likewise, someone who visits your website 30 times is probably more likely or more ready to buy than someone who has visited only 3 times in the same time period. 

5. Email Engagement Model

Lead scoring according to email engagement is also straightforward; it assigns scores based on how each lead interacts with your emails. 

Email engagement is measured through metrics like open rate, click-through rate, and whether or not attachments were opened/downloaded and consumed. 

Yesware’s attachment tracking help sales reps identify who their hottest leads are as well as which content stands out the most with page-by-page breakdowns.

6. Social Media Engagement Model

You can also score leads according to how they interact with your various social media profiles and presence. 

Likes, comments, and shares all indicate varying levels of engagement with your brand and can be weighted and scored accordingly. 

7. Spam and Negative Scores

It’s also important to keep in mind that leads can generate negative scores as well as positive ones. 

Website visitors who land on your career page, for example, probably aren’t interested in purchasing from you; a negative score for anyone who visits that page will help indicate their intent. 

There are other behaviors and characteristics that leads may exhibit that also indicate they aren’t worth your time. Unsubscribing from your email list, using a personal email instead of a business one, writing “student” in the job title field, and using spam-like responses when filling out forms are all indicators that the “lead” isn’t a high-value one and should be deprioritized. A negative score can help balance the scales accordingly. 

Most teams use a hybrid lead scoring model, combining pieces from each of the models outlined above to get the most complete profile of their leads’ positioning.

Tip: Looking to improve your sales processes and strategies? Grab our free ebook below filled with data-backed findings.

Here are some best practices to keep in mind as you develop your lead scoring process. 

Define Entry and Exit Points for Each Stage

It’s important for sales and marketing to sit down together to define what characteristics and behaviors define leads and prospects in each stage of the sales funnel. 

Reverse-engineering a successful customer’s journey can help you pinpoint the exact behaviors that indicate potential conversion and precede a closed-won deal. 

Assign Points to Every Action and Attribute

The more behaviors and characteristics you can define and attach to values, the more effective your lead scoring process will be. 

Every behavior in the sales process is important and meaningful; it’s up to your sales team to determine just how meaningful each is in the bigger picture of your unique sales process. 

Adjust Scoring Models Over Time

The lead scoring development process is not a one-and-done occasion. Your model can and should be updated over time as you learn more and more about the ways your target buyer behaves throughout your sales process. 

On a similar note, it’s also important to use a unique lead scoring model for each offer and each target market. 

Create Lead Scoring Threshold Alerts

Sales teams that want to take their lead scoring efforts to the next level should consider setting up alerts that notify them when leads have reached a certain score threshold.

Lead scoring software can be a huge asset in this regard. Alerts like these help sales reps reach out to leads at the exact moment when they’re most likely to be engaged and receptive. 

Tip: Looking for a lead scoring tool? Vendasta’s “hot lead” scoring system shows your team which prospects are engaging with real-time notifications. Their hyper-customized reports allow salespeople to better understand buyer pain points so they can present the perfect solution.

Predictive lead scoring  is a process that uses the power of artificial intelligence (AI) and machine learning to analyze the behavior of past leads and customers. It uses the data it collects to create an algorithm for scoring leads that can accurately predict how likely a given lead is to purchase based on their current behavior. 

Predictive lead scoring is a powerful tool. Not only does it free up a ton of time for sales reps that would otherwise be spent manually scoring and sifting through leads, but it also gets more capable the longer you rely on it. The more customer data you put in, the more accurate the subsequent process becomes. 

Does your team use a lead scoring process? What characteristics or behaviors are most indicative of a best-fit lead or imminent purchase? How do you initiate and differentiate outreach based on a lead score?

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• Workflow Automation

A Complete Guide for Lead Scoring

Introduction.

Lead scoring is an essential methodology in the realm of B2B sales and marketing. At its core, it involves assigning a numerical score to each lead, typically on a scale from 1 to 100, to gauge their likelihood of making a purchase.

This process is a strategic approach to understand the potential of every lead that comes into the sales funnel. It enables sales and marketing teams to prioritize leads, ensuring they focus their efforts on high scoring leads, which are those most likely to generate revenue.

Traditionally, lead scoring has been a manual process, relying on sales and marketing professionals' intuition and experience to rank leads. However, with advancements in AI and workflow automation, manual tasks associated with lead scoring can be automated completely. We shall discuss all this is detail in our blog.

Lead Scoring Metrics

Modern lead scoring methodologies now incorporate a mix of explicit and implicit scoring metrics, and can also incorporate predictive scoring to build a framework which arrives at accurate lead scores for your leads.

• Explicit scoring involves using concrete information such as job title, company size, or industry.
• Implicit scoring is based on behavioral data like website visits, email engagement, or content downloads.
• use AI on the data around your existing customers and your accepted & rejected leads, to give a lead score.
• use LLMs to replace the subjective decision making tasks in the lead scoring workflow.

Lead Scoring Methods

Let us now discuss popular frameworks used for lead scoring in detail. You can implement any of these frameworks and integrate them into your CRM and other apps using the Nanonets Workflow Builder, which will be covered after this section.

Explicit Lead Scoring Methods

Explicit methods focus on tangible, solid data to evaluate the potential of leads. These methods are grounded in specific, often demographic, information about a lead.

1. BANT (Budget, Authority, Need, Timeframe)

Description: BANT is a classic lead scoring method where leads are assessed based on four critical criteria: Budget, Authority, Need, and Timeframe.

• Budget: Determines if the lead has the financial resources to buy.
• Authority: Assesses if the contact person can make purchasing decisions.
• Need: Identifies if the lead's needs align with the product or service offered.
• Timeframe: Checks how soon the lead intends to make a purchase.

Workflow Example:

• A lead comes in through an online form.
• The form data is enriched using a tool like Clearbit to gather more detailed information about the lead’s company and role.
• In the CRM, a scoring rule is applied where points are assigned based on how well the lead matches each BANT criterion, based on pre-set rules on the enriched data.
• For instance, if the lead has a high authority level in their company and a pressing need for the product, they score higher.
• The CRM then updates the lead's score, prioritizing them for the sales team.

2. Firmographic Scoring

Description: This method scores leads based on firmographic data such as company size, sector, location, and revenue. It’s particularly useful in B2B scenarios where such factors significantly impact the likelihood of a sale.

• A lead is identified via LinkedIn.
• Company information is enriched using a tool like Clearbit to gather more detailed information about the lead’s company and role.
• The CRM scores the lead based on predefined firmographic criteria. For example, a large enterprise in a target sector may receive a higher score.
• This score helps in segmenting leads for tailored marketing strategies.

3. ANUM (Authority, Need, Urgency, Money)

Description: ANUM is another variant that prioritizes the authority and need of a lead, along with urgency and budget considerations.

• A potential lead interacts with a webinar hosted by the company.
• Post-webinar, their engagement and queries are analyzed for urgency and need based on the interaction.
• Their role and company are checked for authority and budget, typically done manually or via a lead enrichment tool.
• The CRM then assigns scores based on these criteria, fast-tracking leads with immediate needs and high purchasing power.

Automate lead enrichment, qualification and scoring workflows with our AI-driven workflow builder, designed by Nanonets for you and your teams.

Implicit Lead Scoring Methods

Implicit lead scoring focuses on the prospective customer's behavior and engagement to gauge their interest and potential to convert. These methods assess how leads interact with your brand, website, or content, offering insights that aren't always apparent through explicit data.

1. Engagement Scoring

Description: Engagement (or behavorial) scoring examines the actions leads take, like the type of content they consume, the duration of their website visits, and their responses to marketing campaigns.

• A lead regularly opens marketing emails and spends time on high-value pages like product demos or pricing.
• Each action (page visit, download, email opens) is tracked and points are assigned based on the level of engagement.
• The CRM, integrated with website analytics using workflow automation, updates the lead’s score automatically.
• High engagement leads are flagged for follow-up by the sales team.

2. Content Interaction Scoring

Description: Leads are scored based on the type and depth of content they interact with, such as blog articles, whitepapers, or videos. More in-depth interactions with technical or advanced content may indicate a higher level of interest.

• A lead spends time reading advanced technical blogs and viewing tutorial videos.
• Content management systems track these interactions, assigning higher scores for deeper engagement with complex content.
• This information is integrated into the CRM, raising the lead’s score.
• Leads engaging with advanced content are flagged as high-potential leads for the sales team.

Predictive Lead Scoring Methods

Predictive methods use AI with traditional methods to automate or increase accuracy.

1. LLM based Lead Scoring (Used with Explicit Lead Scoring)

This approach uses LLMs to gauge subjective parameters in explicit scoring such as Budget, Authority, Need, Timeframe in the BANT framework. This removes the manual task where a salesperson needs to fill the BANT form for a lead based on their personal interaction and available company information.

2. Machine Learning-Based Scoring (Used with Implicit Lead Scoring)

This approach uses machine learning algorithms to analyze past lead data, identifying patterns and characteristics of leads that successfully converted. The system then scores new leads based on how closely they match these success profiles.

We shall learn how this works in detail in the next section with the help of an example.

Automated Lead Scoring using Nanonets

Let's take the example of a BANT workflow and automate it using Nanonets Workflows. The existing manual workflow looks like this -

• Lead enters a form and provides email and a convenient time for a sales call.
• Salesperson creates a new record in Hubspot CRM.
• Salesperson creates the call event in Google Calendar based on the specified time indicated by the lead.
• Once the call is over, the salesperson uses his subjective memory of the call discussion and the sales call transcript fetched from Gong to fill the BANT form with Budget, Authority, Need, Timeframe fields.
• The lead score is thus calculated by the sales person using the filled BANT form and a pre-set formula with weights to each field.
• The lead score is updated manually in the corresponding Hubspot CRM record.

Now let us take a look at how we can automate this using Nanonets by creating an automated workflow that does all the tasks of the above workflow for us.

We feed the description of the workflow we wrote above as a prompt in the workflow generator, and an automated workflow spins up for us based on our description.

We move on and authenticate our Google, Hubspot and Gong accounts to provide the Nanonets workflow with access to the apps in order to facilitate the workflow to fetch data and perform actions directly within your apps.

The workflow runs as follows -

• Google Forms - Triggers a workflow run when the sales call Google Form is submitted.
• Hubspot - New Hubspot record is created with the email submitted by the lead.
• Google Calendar - New calendar event is created between the lead and the salesperson based on the time indicated.
• Gong - The workflow is delayed till the call happens. Once the call is done, the sales call transcript is fetched from Gong
• Nanonets AI - Nanonets AI reads the transcript and populates the BANT fields in a structured fashion.
• Nanonets AI - Nanonets AI uses self selected (default) weights for arriving at a lead score, from the BANT data extracted from the call transcript in the previous step. You can specify the lead score formula and the weights manually in the prompt as well.
• Hubspot - The Hubspot record created in the second step is populated with this lead score.

Here is a demo of the workflow in action.

Let's take a look at the results of automated lead scoring compared to manual lead scoring now.

Lead Scoring Case Study

Challenge: Sales teams often struggle with lead scoring, spending substantial time on manual processes that are prone to incomplete information and subjectivity. The BANT (Budget, Authority, Need, Timeline) framework, while effective, traditionally required time-consuming efforts and could result in biased lead scoring​​.

Solution: Created a Nanonets Workflow - integrating AI to transform the lead qualification process. This tool automates the extraction and analysis of BANT criteria from sales calls, offering a streamlined, efficient approach to lead scoring​​.

Results & Impact:

• Enhanced Precision: In a study comparing over 1500 sales calls, the workflow matched or outperformed AEs in identifying leads likely to close. Notably, its recall rate was 81%, significantly higher than the manual review's 41%, while the precision rate was similar.

• Reduced Cycle Times: Leads scored 80+ by the AI tool showed 5-10% shorter closure cycle times, enhancing sales team efficiency.

• Flexible Scoring: Unlike binary AE assessments, AI provides a nuanced 1-100 scoring scale, allowing more tailored sales approaches.
• Efficiency Gains: Sales teams reported faster BANT qualification, elimination of incomplete data issues, and more time for customer engagement and product development​​.

Conclusion: Workflow automation of lead scoring marked a significant leap in sales efficiency, combining human intuition with AI precision for more effective, customer-centric strategies​​.

Nanonets for Workflow Automation

In today's fast-paced business environment, workflow automation stands out as a crucial innovation, offering a competitive edge to companies of all sizes. The integration of automated workflows into daily business operations is not just a trend; it's a strategic necessity. In addition to this, the advent of LLMs has opened even more opportunities for automation of manual tasks and processes.

Welcome to Nanonets Workflow Automation, where AI-driven technology empowers you and your team to automate manual tasks and construct efficient workflows in minutes. Utilize natural language to effortlessly create and manage workflows that seamlessly integrate with all your documents, apps, and databases.

Our platform offers not only seamless app integrations for unified workflows but also the ability to build and utilize custom Large Language Models Apps for sophisticated text writing and response posting within your apps. All the while ensuring data security remains our top priority, with strict adherence to GDPR, SOC 2, and HIPAA compliance standards​.

To better understand the practical applications of Nanonets workflow automation, let's delve into some real-world examples.

• Automated Customer Support and Engagement Process

• Ticket Creation – Zendesk : The workflow is triggered when a customer submits a new support ticket in Zendesk, indicating they need assistance with a product or service.
• Ticket Update – Zendesk : After the ticket is created, an automated update is immediately logged in Zendesk to indicate that the ticket has been received and is being processed, providing the customer with a ticket number for reference.
• Information Retrieval – Nanonets Browsing : Concurrently, the Nanonets Browsing feature searches through all the knowledge base pages to find relevant information and possible solutions related to the customer's issue.
• Customer History Access – HubSpot : Simultaneously, HubSpot is queried to retrieve the customer's previous interaction records, purchase history, and any past tickets to provide context to the support team.
• Ticket Processing – Nanonets AI : With the relevant information and customer history at hand, Nanonets AI processes the ticket, categorizing the issue and suggesting potential solutions based on similar past cases.
• Notification – Slack : Finally, the responsible support team or individual is notified through Slack with a message containing the ticket details, customer history, and suggested solutions, prompting a swift and informed response.
• Automated Issue Resolution Process
• Initial Trigger – Slack Message : The workflow begins when a customer service representative receives a new message in a dedicated channel on Slack, signaling a customer issue that needs to be addressed.
• Classification – Nanonets AI : Once the message is detected, Nanonets AI steps in to classify the message based on its content and past classification data (from Airtable records). Using LLMs, it classifies it as a bug along with determining urgency.
• Record Creation – Airtable : After classification, the workflow automatically creates a new record in Airtable, a cloud collaboration service. This record includes all relevant details from the customer's message, such as customer ID, issue category, and urgency level.
• Team Assignment – Airtable : With the record created, the Airtable system then assigns a team to handle the issue. Based on the classification done by Nanonets AI, the system selects the most appropriate team – tech support, billing, customer success, etc. – to take over the issue.
• Notification – Slack : Finally, the assigned team is notified through Slack. An automated message is sent to the team's channel, alerting them of the new issue, providing a direct link to the Airtable record, and prompting a timely response.
• Automated Meeting Scheduling Process
• Initial Contact – LinkedIn : The workflow is initiated when a professional connection sends a new message on LinkedIn expressing interest in scheduling a meeting. An LLM parses incoming messages and triggers the workflow if it deems the message as a request for a meeting from a potential job candidate.
• Document Retrieval – Google Drive : Following the initial contact, the workflow automation system retrieves a pre-prepared document from Google Drive that contains information about the meeting agenda, company overview, or any relevant briefing materials.
• Scheduling – Google Calendar : Next, the system interacts with Google Calendar to get available times for the meeting. It checks the calendar for open slots that align with business hours (based on the location parsed from LinkedIn profile) and previously set preferences for meetings.
• Confirmation Message as Reply – LinkedIn : Once a suitable time slot is found, the workflow automation system sends a message back through LinkedIn. This message includes the proposed time for the meeting, access to the document retrieved from Google Drive, and a request for confirmation or alternative suggestions.
• Invoice Processing in Accounts Payable

• Receipt of Invoice - Gmail : An invoice is received via email or uploaded to the system.
• Data Extraction - Nanonets OCR : The system automatically extracts relevant data (like vendor details, amounts, due dates).
• Data Verification - Quickbooks: The Nanonets workflow verifies the extracted data against purchase orders and receipts.
• Approval Routing - Slack : The invoice is routed to the appropriate manager for approval based on predefined thresholds and rules.
• Payment Processing - Brex : Once approved, the system schedules the payment according to the vendor's terms and updates the finance records.
• Archiving - Quickbooks : The completed transaction is archived for future reference and audit trails.
• Internal Knowledge Base Assistance
• Initial Inquiry – Slack : A team member, Smith, inquires in the #chat-with-data Slack channel about customers experiencing issues with QuickBooks integration.
• Ticket Lookup - Zendesk : The Zendesk app in Slack automatically provides a summary of today's tickets, indicating that there are issues with exporting invoice data to QuickBooks for some customers.
• Slack Search - Slack: Simultaneously, the Slack app notifies the channel that team members Patrick and Rachel are actively discussing the resolution of the QuickBooks export bug in another channel, with a fix scheduled to go live at 4 PM.
• Ticket Tracking – JIRA : The JIRA app updates the channel about a ticket created by Emily titled "QuickBooks export failing for QB Desktop integrations," which helps track the status and resolution progress of the issue.
• Reference Documentation – Google Drive : The Drive app mentions the existence of a runbook for fixing bugs related to QuickBooks integrations, which can be referenced to understand the steps for troubleshooting and resolution.
• Ongoing Communication and Resolution Confirmation – Slack : As the conversation progresses, the Slack channel serves as a real-time forum for discussing updates, sharing findings from the runbook, and confirming the deployment of the bug fix. Team members use the channel to collaborate, share insights, and ask follow-up questions to ensure a comprehensive understanding of the issue and its resolution.
• Resolution Documentation and Knowledge Sharing : After the fix is implemented, team members update the internal documentation in Google Drive with new findings and any additional steps taken to resolve the issue. A summary of the incident, resolution, and any lessons learned are already shared in the Slack channel. Thus, the team’s internal knowledge base is automatically enhanced for future use.

The Future of Business Efficiency

Nanonets Workflows is a secure, multi-purpose workflow automation platform that automates your manual tasks and workflows. It offers an easy-to-use user interface, making it accessible for both individuals and organizations.

To get started, you can schedule a call with one of our AI experts, who can provide a personalized demo and trial of Nanonets Workflows tailored to your specific use case. 

Once set up, you can use natural language to design and execute complex applications and workflows powered by LLMs, integrating seamlessly with your apps and data.

Supercharge your teams with Nanonets Workflows allowing them to focus on what truly matters.

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Lead Scoring Made Simple: A Step-by-Step Framework for Success

Table of Contents

Finding leads can be a challenge - even more so when you are trying to qualify high-potential leads that will likely convert. In fact, generating hot leads is one of the top challenges marketers face today. 

More often than not, sales teams spend their time trying to nurture poor leads instead of leads that hold high-conversion capacity.

Maybe your lead qualifying cycle is too long or ambiguous, or perhaps you are getting conversions but don't know which sales prospects to prioritize - it all comes down to lead scoring. So it is essential to know what is lead scoring and why it is important.

What Is Lead Scoring?

Lead scoring is a method to classify and qualify leads based on their probability of conversion . It is mainly performed by assigning numerical values to prospects, thereby helping marketers in identifying which leads are most likely to convert. 

Lead scoring can streamline and reduce the conversion timeline for sales teams. They can now pay attention to the right people, close more deals , and understand the hierarchy of leads they are receiving. 

The process takes several factors into account. For instance, some of the components that can affect a lead score are -

• Customer Persona Fit

Tracking user behavior on your website can be another key indicator in identifying a hot lead. A visitor who compares product prices is a stronger lead and will have a higher lead score than a visitor who browses reading resources on your site.

Understanding the different parameters that affect conversion is a focus determinant for effective lead scoring.

Why Is Lead Scoring Important?

Lead scoring aims to make the job easier for marketers and sales teams. Prior customer data can be used to form impactful lead scoring strategies that can accelerate the conversion cycle for sales teams. Let's understand why it is important:

Streamline Lead Evaluation Process

Manually processing leads is not only time-consuming, but it also opens up room for human error. Using an automated lead scoring system can simplify the evaluation as the leads are now ranked using a pre-determined criterion.

Understand Your Prospects Better

Lead scoring can help you assign your prospects according to different aspects like their past interactions with your brand. By understanding your user's pain points and how well you can cater to them, your sales teams can foster better professional relationships and accelerate the time to close.

Increase Your Sale Conversions

Not all visitors on your website are looking to make a purchase right away. It is important to know which stage of the sales cycle a prospect is on. For instance, you might lose out on revenue because you bombard a potential customer with sales pitches when they are just in the exploration stage. Lead scoring can help you avoid such a mismatch.

With the help of scored leads, you can find out which sales prospects are genuinely interested in converting at the earliest. With automated lead screening, sales teams can focus on primary opportunities first.

Align Your Sales and Marketing Teams

Marketers do their job so that salespersons can onboard more clients. With manual lead scavenging, the operational bridge between marketing and sales teams can widen. Lead scoring not only provides you with better leads but also ensures synergy between the two teams in a company.

Read more: Top 20 Sales KPI for SaaS growth teams to track in 2022

5 Steps to Effective Lead Scoring

Lead scoring is often touted as a great way to make your cold calls more effective. How often does your sales team call a prospect, only to learn they were not ready to convert yet? 

Here are the lead scoring steps you can follow to build a powerful tool: 

Understanding Your Ideal Lead

The most important part of scoring leads is researching your target audience and identifying what makes the ideal lead.  

You must consider two primary factors that go into making the ideal lead:

• Demographics
• Behavioral patterns

First, identify key points such as age group, industry, location, and behavior on your website. Then, use this data to create a profile of your ideal lead to give you a picture of who is most likely to convert and become a customer. 

Defining Positive and Negative Criteria

Based on all the research you have compiled, you can choose a scale of 10 or 100. The goal is to make the scale as balanced as possible so that you get a realistic score for each lead, allowing your sales team to filter through them better.

There can be positive and negative criteria:

• If a potential buyer fits into the demographic of the ideal lead in terms of age or industry, that would be a positive criterion.
• However, if the visitor is a student and only wanted to apply for a job on your site or is from an unserviceable location, those would be negative criteria.

Assigning Points Based on Weightage

Now that you know which factors make a good lead, you can distribute the points fairly among the criteria. For this, you can calculate your overall lead-to-customer conversion rate. Identify criteria that contributed the most and least to closing a deal and assign points to each of them accordingly.

For instance, you may find that leads who signed up for your newsletter are more likely to convert into customers than leads who looked through your Careers page.

The Line in The Sand: Setting the Threshold

Having made a scale to score your leads, you can now set the threshold to determine which prospects should be called. Not every person will fit the bill of the ideal lead, so setting a threshold is important to make your scoring effective.

Note that this part of the process gets better with refinement. You could list circumstances that led to successful leads converting and add those points to figure out roughly how high your threshold to be. You can refine this with feedback from your sales team.

Evolution and Refinement to Improve Efficiency

Your lead scoring tool is now ready for action, but it’s not perfect yet.

Your tool must be refined over time to be more efficient. Be flexible and open to changing scoring criteria and the weightage of points assigned. Make any changes that might be required and configure your tool to identify prospects on your website.

You have now come up with your own lead scoring tool to help you filter out the noise and only call prospective customers that are most likely to convert. This will help save your sales team precious time by prioritizing on the right group of leads.

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Decoding Lead Scoring: A Comprehensive Guide with Real-World Lead Scoring Examples

Prabhat Gupta

In sales and marketing, where businesses engage with a large pool of potential customers, identifying and prioritizing leads is crucial for business success. By analyzing existing lead scoring examples, you stay ahead of the curve, learning to optimize your lead scoring strategies to enhance your business sales.

You can assign points for positive actions (e.g., visiting pricing pages), deduct for negative actions (e.g., unsubscribing), and when a lead reaches a set total, they become a qualified, or "hot," lead. By assigning numerical values to leads based on specific criteria, companies distinguish between merely curious prospects and those who exhibit genuine interest and engagement.

Effective Lead Scoring Boosts the Success of Your Business by,

• Ensuring sales teams prioritize leads with a higher likelihood of conversion, allowing sales representatives to optimize their time and resources.
• Enabling marketing teams to focus their campaigns on leads more likely to convert, resulting in a targeted approach that reduces marketing spend on less promising leads.
• Aligning sales and marketing by qualifying leads with clear criteria, ensuring smoother handovers, reduced friction, and improved efficiency.

Whether you are an experienced professional looking to enhance your lead management approach or a newcomer eager to grasp the fundamentals, this exploration into lead scoring will provide valuable insights and actionable knowledge.

Common Lead Scoring Models

This table shows lead scoring models at a glance.

To gain a deeper understanding of the practical implementation of lead scoring across various businesses, check out the following real-world lead scoring examples.

Effectively prioritizing and evaluating potential leads allows businesses to focus their resources on prospects with the highest likelihood of conversion, ultimately maximizing efficiency and driving revenue growth. Let's analyze and understand various lead scoring examples implemented by different businesses to enhance their customer engagement. ‍

An eCommerce Platform’s Lead Scoring Case Study  

Let’s take an example of an implementation of lead scoring strategies in an eCommerce platform to enhance conversion rates and optimize resource allocation and boost their business.

An eCommerce platform, experiencing a high volume of incoming leads, faced challenges in prioritizing and converting them efficiently. To address this, the company decided to integrate a lead scoring system to identify and focus on high-potential leads.

Key Lead Scoring Objectives

• Improve conversion rates by prioritizing leads with a higher likelihood of purchase.
• Optimize resource allocation for sales and marketing efforts.
• To proceed with the task, you need the best lead scoring platform.

So what is the go to tool for marketers to generate potential leads? There are different tools available among which Nected lead scoring tool stands out.

With Nected, you can set your personalized lead scoring formulas.

Implement Step by Step Lead Scoring easily in Nected now.

With Nected's Rule Engine , you can effortlessly construct a sophisticated and highly adaptable lead scoring system. This tool empowers you to align your sales and marketing efforts more precisely towards the leads that truly matter.

When you explore the capabilities of Nected's Rule Engine for personalized lead scoring tailored to your unique requirements, you gain a different perspective on how easy and seamless this process can be. The tool offers versatile segmentation for maximum effectiveness of your strategy.

For instance, let's take Miss Smith as an example to understand the lead scoring concept. Suppose she wants to buy a new jacket, and she is already a customer of the eCommerce platform. Based on her past interactions and present actions, the lead scoring tool will assign a lead score using predefined formulas, terming her as a hot lead or not. This information helps the company understand her purchasing intent, allowing them to provide personalized recommendations or other aspects of assistance. The following table provides examples of online behavioral criteria and how they contribute to lead scoring:

Educational Website Lead Scoring Case Study

For an educational coaching center seeking to expand its reach and increase course registrations, assessing how potential students engage with the website is crucial. By analyzing lead behaviors, the center can personalize marketing strategies and improve user experience to effectively attract and register more students.

Key Objectives of Lead Scoring

• Prioritizing leads exhibiting behaviors indicative of a higher likelihood to enroll or engage with educational content.
• Implementing a unified lead scoring system that aligns both teams towards common objectives, ensuring a cohesive approach in attracting and converting prospective students.

Implementing a lead scoring system is essential for prioritizing and nurturing potential customers effectively. Behavioral criteria are crucial for assessing the impact of different interactions and play a vital role in the lead scoring process.

You can assign different scores to the leads for engaging with your SaaS platform using the Nected tool. For instance, the tools’ easy interface lets you set personalized formulas.

For example,

• Webpage visits are the starting point, and you assign +5 points for checking out high-quality pages.
• Downloading content shows even more interest, earning +10 points for recognizing valuable resources.
• Expressing interest in product demos is a big deal, worth +20 points. It shows they're seriously considering our product.
• Filling out forms gets +25 points, indicating a willingness to share info and actively engage with us.
• For those signing up for webinars, it's a strong commitment, earning +30 points.

These scores will help you focus on leads showing the most interest and engagement.

‍ A Lead Scoring Case Study in SaaS Company

Faced with the challenge of effectively identifying and prioritizing potential customers in a highly competitive SaaS landscape, a SaaS product company decided to leverage lead scoring. The primary focus was on understanding trial usage, feature exploration, and user engagement with support documentation.

Key Objectives

• Prioritize leads actively engaged in trial versions and feature exploration.
• Develop a lead scoring model based on trial depth, time spent, and login frequency.

You can assign different scores for doing some specific things related to your website using the Nected tool. 

For instance, Nected’s easy interface lets you set personalized formulas.

• For website visitors, diving into the product's key features gets them +10 points.
• Signing up for a free trial shows serious interest in experiencing the product firsthand, earning them +20 points.
• Expressing interest in a product demo is worth +25 points, indicating their keenness to understand our product in detail.
• Actively using knowledge base gets them +15 points, highlighting the value of users seeking information on their own.
• Submitting a support ticket is a significant move, earning them +30 points.

Key Improvements

• Leads engaged in meaningful trial usage and feature exploration received higher scores, allowing for a more targeted sales approach.
• Leveraged lead scoring insights to tailor communication strategies for different segments, increasing the effectiveness of outreach efforts.
• User engagement with support documentation reflected a commitment to understanding the product, resulting in increased lead scores and a higher likelihood of conversion.

Enabling Lead Scoring in B2B Marketing

A B2B company faced challenges in effectively prioritizing and converting leads due to the diverse nature of its client base. The decision was made to implement a lead scoring system to better identify high-value leads based on company attributes and engagement from key decision-makers.

• Prioritize leads based on company size and industry to maximize potential business value.
• Improve overall conversion rates and the efficiency of the sales process.

Implementation of the lead scoring strategy

1. scoring based on company size and industry.

2. Scoring Decision-Maker Engagement

Implementing lead scoring in Business-to-Business (B2B) marketing involves assessing how potential clients interact with your products or services. You can assign different scores to the leads for engaging with your business. For instance, the tools’ easy interface lets you set personalized formulas.

• Engaging with whitepapers earns a lead a +15 point on their scoring, indicating a higher score for downloading or interacting with informative whitepapers.
• Expressing interest in a product demo has a significant +30 point, resulting in a higher score for those keen on a detailed product demonstration. 
• Actively participating in webinars carries a +25 point, translating to a higher score for attendees. 
• In cases where multiple decision-makers are involved, there's a +20 point, signifying a higher score for such collaborative interactions.
• Requesting proposals or quotes has a substantial +35 point, reflecting a higher score for leads showing a strong interest in these detailed aspects of our offerings.
• Leads from larger enterprises and specific industries received higher scores, allowing sales teams to prioritize efforts on high-potential leads.
• Overall conversion rates improved as a result of prioritizing high-value leads and engaging effectively with key decision-makers.

Why Choose Nected for Lead Scoring?

You must explore the capabilities of Nected Rule Engine for Lead Scoring , personalized  to your unique needs, that offers versatile segmentation for effective lead scoring.

• Effortless Segment-Based Lead Scoring: You can personalize  your lead scoring plans effortlessly with Nected's rule engine. It helps you prioritize users based on specific actions, such as those visiting pricing pages over blog visitors.

• Real-Time Lead Scoring: You will experience the power of real-time lead scoring with Nected by instantly scoring leads based on their actions on your platform, allowing you to adapt and respond dynamically to changing scenarios. ‍
• Negative Scoring for Precision: Not every prospect action is beneficial, and Nected understands that. You can build rules for negative scoring, disqualifying leads based on undesirable actions, such as email unsubscribes. ‍
• Adaptive Scoring for Market/Season Cycles: Nected's highly iterative lead scoring system enables instant adjustments to your scoring models, ensuring adaptability to changing market conditions or seasons.

Implement Lead Scoring to Transform Your Lead Generation Game

As businesses evolve, the ability to decipher customer behaviors and prioritize leads becomes a competitive advantage. Lead scoring serves as a guiding light in this journey, leading to increased efficiency, higher conversion rates, and sustained growth.

You can revamp your business's lead generation game by implementing effective lead scoring. With this approach, you gain the ability to precisely navigate potential customers, allocating resources to the most promising opportunities.

The benefits of effective lead scoring, including improved sales productivity, enhanced marketing ROI, and better alignment between sales and marketing teams, underscore its significance in driving business success.

To implement these strategies, consider acquiring the best lead scoring platform like Nected's lead scoring system , delivering a significant impact for businesses. The platform offers a 3X faster time to market and 10X faster iterations through experiments, with a setup process requiring less than 1 hour of development for a streamlined implementation.

Prabhat Gupta is the Co-founder of Nected and an IITG CSE 2008 graduate. While before Nected he Co-founded TravelTriangle, where he scaled the team to 800+, achieving 8M+ monthly traffic and $150M+ annual sales, establishing it as a leading holiday marketplace in India. Prabhat led business operations and product development, managing a 100+ product & tech team and developing secure, scalable systems. He also implemented experimentation processes to run 80+ parallel experiments monthly with a lean team.

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Lead Scoring 101

Lead scoring is an effective way to track contacts’ engagements, creating a temperature gauge to plan future messaging and targeted sales outreach.

Similar to playing any game, scores make sense only if you have rules and goals to determine how points are scored. Think of lead scoring as gamifying your marketing and sales process. The points will help determine who your best prospects are and close deals quicker.

What is lead scoring and how does it work?

Lead scoring is a widely used methodology by marketing and sales teams to determine the likelihood of a lead making a purchase. The process involves assigning a score to each lead based on how they engage with your brand.

The lead score indicates the probability of a lead making a purchase, with higher scores indicating a greater likelihood of purchase. Scoring typically ranges from 1-100. Before setting up a lead scoring model it’s important to understand common attributes that help set the baseline for your score.

Explicit vs. implicit scoring attributes

Lead scores are calculated based on various attributes, which can generally be split between explicit and implicit attributes.

Explicit lead scoring

Explicit lead scoring is the information given explicitly by the lead, such as demographic data. Examples of explicit attributes include job title, company size, industry, and location.

Implicit lead scoring

Implicit lead scoring involves analyzing a lead’s behavior to gauge their level of interest. This includes behavioral scoring, active and passive buying behavior, and interactions in the sales and marketing funnel. Implicit attributes include white paper or gated content downloads, website visits, email engagement, watching product demos and videos, and attending webinars.

Each attribute that applies to a lead earns them points toward their lead score. For example, visiting the pricing page may indicate a high level of interest and therefore add points to their lead score. Conversely, unsubscribing from emails may subtract points from their lead score.

Once a lead reaches a certain point threshold, such as a score of 50, a sales rep will reach out to close the deal. Many companies have  sales automation  to identify qualified leads and alert sales reps accordingly.

Why is lead scoring important for your business?

Lead scoring is essential for removing guesswork and enabling you to focus on the most promising leads that are likely to convert, something every business can benefit from.

The impact of lead scoring

For example, let’s take the case of TechSolutions, a software development company. Before implementing a lead scoring system, they faced difficulties identifying qualified leads and wasted resources on low-quality leads that did not convert. This led to wasting time and leaving money on the table.

However, after implementing lead scoring, the company identified the most promising leads and prioritized them for sales follow-up, sending those who needed more lead nurturing to the marketing team. By focusing their efforts on high-quality leads, they improved their conversion rates and achieved significant revenue growth.

As a result, TechSolutions expanded its customer base and invested in additional marketing resources to generate even more leads. In short, lead scoring helps you identify where your leads are in the sales funnel, enabling you to focus on the most promising leads and take appropriate action to turn them into customers.

The benefits of lead scoring to your business

Now that we understand the importance of lead scoring in the buying cycle let’s explore the real world benefits it can offer to both the marketing and sales teams:

Lower marketing and acquisition costs

Appcues, a B2B SaaS company, reduced customer acquisition costs by 80% through lead scoring. By prioritizing leads based on their engagement level and fit with Appcues’ ideal customer profile, the company could focus their marketing efforts on the most promising prospects.

Higher conversion rates (CVR) and time saved

LearnUpon, an e-learning software company, increased their MQL to SQL conversion rate by 30% after implementing lead scoring. By prioritizing leads based on their engagement level and fit with LearnUpon’s ideal customer profile, the sales team could focus on the most promising prospects and close deals more quickly.

Improved sales and marketing alignment

Hootsuite, a social media management platform, improved alignment between its sales and marketing teams by implementing lead scoring. By providing sales with higher quality leads and more detailed lead intelligence, the company was able to close deals more quickly and with greater success.

Increased revenue

ZoomInfo, a B2B data and intelligence provider, saw a 45% increase in sales conversions after implementing lead scoring. By focusing its sales efforts on leads that had the highest scores, the company was able to improve conversion rates and drive more revenue.

The evidence speaks for itself. Lead scoring drives revenue growth and provides a range of benefits for your business. The question now is how?

Resources on lead scoring

Want to dive deeper into lead management and lead scoring?

Take the next step with Lead scoring 102

Learn about lead management

Learn about lead routing

Learn about lead nurturing

Creating a lead scoring system

Let’s explore how to enhance your marketing efforts by creating a lead-scoring system. By effectively utilizing lead scoring, you’ll be able to prioritize your leads, nurture them efficiently, and ultimately boost your conversion rates. 

Understanding lead scoring

You can award points for a variety of actions. Points can be awarded or subtracted and can expire after a set amount of time. In ActiveCampaign, lead scoring is found in the “ Contacts ” tab, and then in “ Scoring ”: 

When you click “ Add New Score ,” you denote if the score is for a contact or a deal. 

For our example, we are creating contact scoring rules relating to engagement, let’s make sure we are editing the name of our scoring rules to be ‘Engagement’.

Then, after we ‘Add a New Rule’, the familiar conditions screen will pop up, allowing you to input your conditions. We want to award five points to someone who signs up for a  newsletter  through our Newsletter form. When complete, be sure to set your Lead Score Rule to Active in the upper right corner.

Note : A contact either meets conditions or does not. Meeting a condition adds or subtracts a value only once; the points are not cumulative. In our example, the only rules we’ll put in our Engagement score are actions people will take only once. Examples include subscribing to a newsletter, downloading a specific report, or attending a particular event.

Now that we have an Engagement score, we can add points to the Engagement score in our automations. For example, if we send an email to our new newsletter subscribers, who have just earned five points by completing our form, we may also want to give them points for actually opening the email.

Because opening an email is a smaller commitment than completing a form, we’ll award two points for the act. Additionally, points can be set to expire. If you want a good read of how hot or cold your leads are, expiring points will help you keep track.

In this example, we used an If/Else statement to award points to contacts who open the email. The points are set to expire after three months.

Now that we know where and how to add points let’s think about overall planning.

Strategy first

In order to determine how your organization should score your contacts, you have to understand what’s important to your organization. What kind of actions do you hope your audience will take? What communications do you send, or what events do you plan? How do you determine if a contact is hot or cold?

There are several different actions that could be important for a contact:

• Subscribing to email updates
• Requesting a free informational download
• Requesting a consultation
• Registering for an event

There are also actions that, in the aggregate, may add up to an engaged contact. While these actions may seem small on their own, the aggregate can show a bigger picture. For example:

• Opening an email
• Clicking a link
• Visiting a particular URL more than once

Understanding all the different actions your contacts can take, and learning their tendencies, will help you along the way.

Data-driven planning

Look at your data and get a feel for any similarities between your contacts that ultimately convert.

For example, if a large percentage of conversions occur following a free consultation, awarding a higher number of points for a consultation makes sense. Those leads are historically more likely to convert than others.

Monitoring cold leads can also help you make strategic decisions. A contact may open a few emails but never take any additional actions. A small action like opening a single email doesn’t call for another email; you want to avoid being considered spam!

Awarding points

Now that you have a handle on what’s most important, you can add to your Engagement Lead Score rules.

Remember : Contacts only trigger rules in the Scoring section once, and the points are NOT cumulative. You don’t want to put repetitive actions here as a best practice.

• Subscribed with Newsletter form submission = 5 points
• Requested a report through Request free report form = 10 points
• Has achieved goal Requested Consultation = 20 points

We’ve determined that these actions mean a lot to our organization and signify a greater level of interest from our contacts. They’ll also take these specific actions only one time. We’ve scored them to give additional insight into the engagement level of our audience.

You can also set up  automations  that award points. As noted earlier, not all points are created equal. Some points may need to expire because smaller actions may not be a meaningful way to determine engagement. 

For example:

Opening an email = 2 points, expire after 3 months

Clicking a link = 5 points, expire after 3 months

Simply opening an email doesn’t require much effort. That is why those points expire: if someone just opens emails and racks up points but never takes any additional actions, there’s no need to identify them as a particularly hot lead. (Additionally, if many people open your emails and take no additional actions, you may want to revisit your messaging.)

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Deliver gated content to new contacts and create a deal in your CRM — all with one automation.

Take lead scoring to the next level

Our next guide,  Lead Scoring 102 , covers additional examples of when lead scoring can empower your marketing automation and next steps for dividing your leads by engagement. Lead scoring empowers you to automate re-engagement campaigns, create entries in the  Deals CRM , and many other actions based on lead score criteria you create.

Do even more with lead scoring

Lead scoring is a powerful way to individualize marketing automation across all types of businesses. If you’re looking to improve an existing lead-scoring system, check out our post on  creating an effective lead scoring system .

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What is lead scoring—and how do you get started?

My most recent buying experience looked less like a straight line and more like a plate of spaghetti. Here's how it went down: while listening to a podcast, I came across the founder of a product and ended up following him on Twitter. Later, I joined his product's email list; eventually, I signed up for a free trial. Then I stopped using the product for a while, before realizing I needed it after all. Finally—a full year after first hearing about it—I made the purchase.

My long-and-winding customer journey isn't unique. Many businesses manage dozens, hundreds, or thousands of leads like this at any given time, ranging from low-quality spam to ready-to-buy referrals. Lead scoring identifies the leads that are most likely to convert into sales, which lets you focus your efforts where they're most likely to pay off.

In this article, I'll break down how lead scoring works, highlight tools that make it more efficient, and explain how to get started, so you can save your sales team time and boost revenue.

What is lead scoring?

Lead scoring involves rating each sales prospect based on a combination of demographic and behavioral data, resulting in a numeric value between 1 and 100. The higher the value, the better match they are to your ideal customer profile (ICP)—and the more likely they are to convert.

Here's why lead scoring matters:

Your sales team spends less time on dead-end leads, and more time on those that have a good chance of becoming a customer.

Customers get a better experience, since lead scoring makes it easier to nurture them with relevant content.

By focusing your team's attention on the highest-potential deals, your conversion rate and average deal size are likely to go up.

Put simply: qualified prospects = higher conversion rate = more revenue.

How a lead scoring model works

Let's look at two hypothetical prospects:

Prospect A is a sales manager from Oklahoma working in the manufacturing industry at a company with 1,000 employees. They view three pages of your website, sign up for your email list, open three emails once on your email list, and submit an inquiry.

Prospect B is a project manager from Seattle working in the software industry at a company with 100 employees. They view five pages of your website, download an eBook, join a webinar, and sign up for a free trial.

While the traditional method of lead scoring was labor-intensive—especially collecting data for each lead—today's sales CRMs increasingly automate data collection, while using AI to generate predictive lead scores. Still, you'll need to pay attention to the customer traits and behaviors you value, so you can tweak your settings and make your lead score more accurate.

Lead scoring best practices: How to get started

To illustrate how lead scoring software works, I'll be using screenshots from a few different CRMs.

1. Pick your lead scoring software

Here are some best-of lists to help you pick the right lead scoring software:

2. Collect data

For your lead scoring tools to work, you'll need data—lots of it. There are three main layers to this data.

Actions within your CRM. Things like moving a lead to a different deal stage can impact your lead score dramatically. Once your sales reps have spoken with a prospect, the game changes: after assessing their needs and potential, your salesperson might disqualify the prospect entirely—or catapult them to the top of the list.

3. Decide what data to emphasize

The tricky thing about designing a lead scoring system is that there's no single playbook that works for every business. You'll need to decide for yourself which attributes make a prospect more or less valuable.

Once you've decided which customer traits to focus on, it's pretty straightforward to emphasize some (and deemphasize others) within your CRM. Here's how it looks to adjust your lead scoring model in Freshsales, for example.

Your ideal customer profile, if you have one, is a good way to choose the customer attributes that will form the core of your lead scoring model.

Example: Let's say you're in the solar panel business. Your ideal customer is a homeowner, so homeowners would be assigned a higher point value than renters.

Example: If you're a marketing agency that focuses on helping small businesses with SEO, you'll assign more points to businesses with fewer than 250 employees.

If all this feels overwhelming, you can always rely on AI to do the job for you. It won't be quite as nuanced—nor will it understand your ideal customer as well as you do—but AI has large-scale number-crunching on its side. AI-powered predictive lead scoring takes into account all the raw data you're collecting in your CRM, your prospect's behavior and social data streams, and the attributes of past deals that turned into sales. Then, it automatically provides a lead score and assesses customer fit; advanced predictive models like Salesforce Einstein can also coach you on when to reach out to a prospect, based on typical industry buying cycles.

HubSpot and Salesforce both offer predictive lead scoring, as do many of the sales CRMs tailored to mid-sized businesses. Here's what the Freshsales predictive AI insights look like.

4. Refine your lead scoring model

Don't expect your lead scoring model to perform perfectly when exposed to the real world. You'll want to tweak it regularly over time, especially in the weeks and months after you launch it.

To refine your model, there are a few things you can do:

Talk to your sales reps. Ask them to flag a few examples of leads they assumed would be high-quality because of the lead score, but which ended up being poor-fit prospects. Break down those examples to figure out what variables might be overweight or underweight in your lead scoring model. Keep an eye on spam leads too, and devalue any indicators they have in common.

Lean on automated settings . Check to see what features your CRM has to keep your lead scores relevant over time. For example, some CRMs have a "time decay" feature: if your prospect has no engagement, their lead score will automatically decline over time.

Use automation to boost your lead scoring system

Analyze new typeform entries with openai and send to a google sheet.

Enrich new Facebook Lead Ad leads with Lead Score by Zapier and log them in Google Sheets

Add new Typeform form entries to Salesforce as leads

Add new Shopify customers to EngageBay as contacts

Related reading:

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Ryan Kane is a writer and marketer based in Mérida, Mexico. He writes about SaaS, AI, and marketing while building fun Internet side projects. Learn more: ryankane.co.

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Lead Scoring: Definition, Criteria, and Strategy

Table of contents

To see what Databox can do for you, including how it helps you track and visualize your performance data in real-time, check out our home page. Click here .

You’ve got hundreds of leads making their way through your pipeline…

Your team is following up with every single one…

Your sales reps are trained professionals and your offer is great…

Yet, your conversion to paid is still low.

What’s going wrong?

Well, in most cases, the answer is that you’re following up (and wasting time) with poor-quality leads – the people who submit an inquiry but have no real motivation to purchase from you.

There’s only one antidote in this situation – a lead scoring model .

Lead scoring is the process of giving your leads a score (usually out of 100), with those towards the higher end being your highest-quality leads.

Businesses typically develop a list of criteria, with each box ticked increasing their score.

In this report, we’ll help you better understand what lead scoring is and how it works, what kind of criteria you can assign, and how to develop a lead scoring strategy.

Let’s dive in.

What Is Lead Scoring?

What is predictive lead scoring.

• Why Is Lead Scoring Important?

How to Create a Lead Scoring Strategy?

5 lead scoring criteria for evaluating your leads.

• How to Score a Lead: 8 Lead Scoring Tips For Identifying Your Highest Quality Leads

What is a Good Lead Score?

What are the best lead scoring tools, stay on top of your lead data with databox.

Lead scoring is a technique that sales and marketing teams use to rank potential customers ( leads ) based on their likelihood of becoming customers.

The goal of lead scoring is to know which leads have the highest chance to convert, which allows you to focus your resources on them.

In the process of lead scoring, we assign numerical values or scores to leads based on various criteria that indicate their level of interest with our product.

Here’s what that might look like:

• Lead A : They’ve visited your pricing page (5 points), submitted an inquiry (15 points), and attended a sales demo (25 points.) Their lead score is 45.
• Lead B : They’ve submitted an inquiry (15 points) but did nothing else aside from that. Their lead score is 15.

…Which lead should you prioritize?

Naturally, it’s Lead A – the one with a higher score and the contact who seems more keen on purchasing from you.

Predictive lead scoring is a more advanced form of lead scoring that leverages machine learning and predictive analytics to assess the likelihood of a lead converting into a customer.

Instead of relying only on manually defined criteria, predictive lead scoring uses these algorithms to analyze vast amounts of data and identify patterns that correlate with conversions.

Here’s how predictive lead scoring typically works:

• The business collects data related to leads and their interactions with the company (e.g. demographic data, online behavior, email interactions, etc.).
• The algorithm identifies the most relevant attributes that indicate stronger lead quality.
• The predictive lead scoring model uses your historical data on leads and outcomes (converted or not) to recognize patterns between the chosen features and conversions.
• When a new lead enters the system, the predictive model applies the learned patterns and assigns a predictive score.

Predictive lead scoring models often improve over time as more data becomes available and as the organization gains more insights into the performance of the model.

However, it’s important to note that predictive lead scoring is not a one-size-fits-all solution.

The effectiveness of a predictive model depends on the quality and relevance of the data it’s trained on, and you need to regularly refine the model to ensure ongoing accuracy.

Why is Lead Scoring Important?

Lead scoring is important for several reasons and implementing it can significantly benefit both your sales and marketing teams.

Here are some key reasons why lead scoring is considered crucial:

• Better resource allocation : Lead scoring helps prioritize leads based on their chances to convert, which ensures that company resources are concentrated on those leads that are more likely to become customers.
• Improved sales productivity : Since sales teams can focus their efforts on leads with higher scores, it allows sales representatives to spend their time more effectively and close more customers.
• Faster sales cycles : Once you know which leads are “hot”, the sales team can engage them at the right time in their buying journey, which can accelerate the sales cycle and reduce the time it takes to get a conversion.
• Data-driven decision making : Lead scoring relies on data and analytics, which means you also get direct insight into your customers’ behavior and preferences. With these insights, you can fine-tune your other sales and marketing strategies as well.
• Stronger customer retention : Beyond the initial sale, you can also use lead scoring to identify leads that are more likely to become long-term customers. With this type of data, you can tailor your post-sale engagement and develop better retention strategies.

Now that you know what lead scoring is and just how important it can be, let’s check out the exact step-by-step process you can follow to create your own lead-scoring strategy.

• Define Your Ideal Customer Profile (ICP)
• Identify Key Buying Signals
• Assign Point Values
• Negative Scoring
• Integrate with Your CRM
• Continuous Monitoring and Adjustment
• Documentation and Training

Step 1: Define Your Ideal Customer Profile (ICP)

Start by analyzing your existing customer base.

You should look for common characteristics such as industry, company size, geographical location, and other relevant factors. And don’t forget to consider psychographic factors like common values and challenges.

This detailed profile will serve as the basis for scoring leads based on their similarity to your most valuable customers.

Step 2: Identify Key Buying Signals

Monitor buying signals . to pinpoint the actions that indicate a lead is progressing through the buying journey.

This could include visiting specific product pages, engaging with pricing information, signing up for a trial, or attending events.

You should collaborate with both sales and marketing teams to gather insights into the behaviors that historically correlate with successful conversions.

Expert Insight: Are you gauging your website’s engagement to identify buying signals? If so, you should check out our free Website Engagement Overview Dashboard . Instead of scrambling through endless reports in your Google Analytics and GSC tools, you can combine all of your most relevant data in a single dashboard and track it in real-time.

Step 3: Assign Point Values

Assign a numeric value to each buying signal based on its relative importance (we’ll dive deeper into this later). 

For instance, a direct product inquiry might be worth more points than a standalone webinar attendance.

You need to make sure that your point system reflects the varying levels of interest that the lead exhibits throughout the sales funnel.

Step 4: Negative Scoring

Don’t forget to incorporate negative scoring.

You need it to account for behaviors that may indicate a lack of fit or interest.

Assign negative points for actions such as repeated visits to the careers page, unsubscribing from emails, or prolonged periods of inactivity. This prevents wasting resources on leads unlikely to convert.

Expert Insight: Tracking your unsubscribes is just one of the metrics you need to pay attention to when it comes to email marketing. Open rates, CTR, and conversion rate… these are just some of the additional metrics you need to keep an eye on. And with our free Mailchimp Campaign Performance Dashboard , you can do it in real time, in one place. If you use another ESP, don’t worry – we have 100+ integrations you can choose from.

Step 5: Integrate with Your CRM

Have you integrated your lead scoring system with your CRM platform?

This integration ensures that both sales and marketing teams have access to real-time lead scores and can use this information to prioritize their outreach efforts.

CRM automation helps streamline the entire lead scoring process and keeps the data up-to-date.

Expert Insight: Do you use Pipedrive for your CRM needs? If so, you might’ve had some trouble with navigating through the various dashboards and finding the data you need. Not anymore. Download our free Pipedrive CRM Account Overview and connect your most relevant metrics in one place – no need to juggle through dozens of reports just to get the insight you need.

Step 6: Continuous Monitoring and Adjustment

Regularly review and analyze the performance of your lead scoring model.

As a best practice, compare the lead scores of converted leads against those that didn’t convert to identify new patterns and trends.

Use this data to fine-tune your scoring criteria. For starters, you can do this by adjusting point values and adding/removing behaviors as necessary to improve the accuracy of your predictions.

In this step, it’s also crucial that you start A/B testing your process.

You can compare the outcomes of leads scored with the new model against those scored with the previous method.

Also, remember to regularly revisit your lead scoring strategy in light of evolving market conditions, changes in customer behavior, and shifts in your product or service offerings.

Step 7: Documentation and Training

All that’s left to do now is create comprehensive documentation for your lead scoring strategy, including the rationale behind point assignments and the criteria for positive and negative scoring.

This, alongside training sessions, is what your sales and marketing teams need so they can better understand your company’s lead scoring model and how it fits into the overall strategy.

If you’re just starting to build your lead scoring system, one of the biggest headaches is usually deciding which criteria you’ll use for evaluation.

That’s why we asked industry experts to chime in and give us their insight.

Here are the 5 lead scoring criteria they recommend for evaluating your leads:

Where a Lead Comes From

Email responses, content engagement, the lead’s attitude.

Are your leads coming primarily from your social media channels? Did you generate them through paid advertising? Maybe organic search?

Where your lead comes from provides a lot of insight into its potential.

For example, a lead that originates from a targeted webinar registration may be more valuable than a randomly acquired one.

When you understand the source, you can tailor your approach to capitalize on the most productive channels.

Joe Arko of G2 argues that “the most important thing about a lead is where it came from.”

“The close rate for different lead sources varies from 2%-50%. Word of mouth closes at an insanely high rate, whereas a one-click web form isn’t going to be in the same ballpark. All other variables are interesting, but there’s no better indicator of ‘likely to buy’ than how they reached out to begin with.”

Srish Agrawal of A1 Future Technologies agrees with this approach:

“Always check the source of your leads and have a tracking system in place like a  lead generation dashboard  to know where the best leads are coming from. This way, you can remove the lower quality traffic/lead sources, and focus on the ones working best.”

Pushkar Gaikwad of AeroLeads also added that “a user searching for our software name from Google should have a high score as compared to someone who is coming from different sources.”

And according to our research, content visited through a search engine is reported to be the best source of high-quality leads.

Expert Insight: If you have multiple traffic sources to keep an eye on, things can easily get complicated in Google Analytics. But not if you download the Website Acquisition Overview Dashboard . Track where your leads are coming from, their demographic data, and much more – all in one place.

Next up, you should be looking at what role within an organization your lead holds.

Different job roles have varying levels of decision-making power and influence.

For instance, a lead in a managerial or executive position usually has a more significant impact on the purchasing decision compared to someone who just started working for the company.

Eric Melillo of COFORGE explains that, as with most sales processes, a deal “is best influenced by decision-makers.”

“I’ve found that by segmenting our leads by job roles that fit our buyer personas best, we can have better conversations with the right people. This also helps sales efficiency as I’ll stop chasing poor-fit leads that have lower lead scores. This allows me to be much more effective while nurturing prospects.”

How often do you analyze your lead’s responses to emails?

Metrics such as open rates, click-through rates, and response times can help you assess the lead’s responsiveness and enthusiasm.

Positive responses, such as requesting more information or expressing interest in a product, are strong indicators of a hot lead.

On the other hand, consistent disengagement typically suggests that you need to re-evaluate your lead nurturing strategy for that particular segment.

As Tom Buchok of Mailchart explains, “smart email marketers have a process for sending their emails. The first may merely build anticipation, working up to the actual introduction of the product or service.”

Buchok thinks the responses to these emails are a great way to score your leads:

“When you begin to see hits to your website from these emails, or even responses to the email, those customers are ready to make a purchase. They should be contacted immediately, so you can answer any questions and finalize the deal.”

“Most email service providers report which page the person was on when they filled out a form, so you can use that to put your leads into different categories based on which product may be the best fit for them.”

Measuring a lead’s interaction with various types of content, such as blog posts, whitepapers, or videos, gives you a comprehensive understanding of what they’re interested in.

And naturally, a lead that consistently engages with in-depth content relevant to your offerings demonstrates a higher likelihood of conversion.

James Pollard of The Advisor Coach says that using content engagement for scoring leads “isn’t only one of the most effective ways to do it, but it’s also the simplest”.

“In fact, the whole concept of remarketing and retargeting is based on this. If someone has already interacted with your content, that person is more likely to convert.

For that reason, a good lead scoring system will assign points based on how many times someone has downloaded your content, watched your webinars, liked your pages, and so on. The rule of thumb is that the higher the score, the better the prospect.”

And it’s not just the pages they’re visiting. The frequency of each engagement is the most popular criteria our experts consider when scoring a lead:

HealthMarkets ‘ David Peterson also recommends an interesting strategy:

“Incorporate content-based scoring that segment based on where the lead is in the marketing funnel.

There is an important difference when scoring digital behaviors related to informational content versus transactional content.

For instance, a lead viewing an article titled “What is Medicare?” should be engaged differently than someone who is looking at an article about “How to buy Medicare Advantage in Texas”.

Expert Insight: Do you use Facebook as a part of your content marketing strategy? While your Facebook page is a great way to get more users in your funnel and boost engagement, the reports you can find in the main interface can often be too confusing. But you can simplify it with our free Facebook Page Insights Dashboard . Download it for free and start tracking your most relevant metrics like reach, visits, and likes in just a few minutes (all in one dashboard).

Aside from explicit actions, you should also analyze a lead’s overall attitude during interactions with your company.

Factors such as responsiveness, communication style, and receptiveness to outreach efforts can tell you how open they may be to your offers.

Depending on what channels you use, the strategies you incorporate will differ.

Heather Baker of TopLine Explainers shared her method:

“This might be a little unconventional, but as a service business, our leads need to be qualified over the phone. If the person is rude or unpleasant to deal with, we immediately (and politely) qualify out, no questions asked. That’s because we know from experience that no amount of budget can compensate for a client that makes your team miserable.”

PRO TIP: Create a Leads By Source Dashboard for HubSpot Marketing in Just A Few Steps

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. Which sources generate the most traffic and leads? Creating a comprehensive report takes time and a lot of data combining, but with our free dashboard, you can have it ready in just a few clicks.

Our HubSpot Leads by Source Dashboard includes data from HubSpot Marketing with key performance metrics like:

• New Contacts vs Visits by Source – Discover how many new contacts and visits each source generates within the pre-set time period. 
• Visitor to Lead Conversion Rate – Gain insight into the number of visitors that convert into leads in a pre-set time period. 
• New Contacts by Referral –  Track the number of new contacts generated through referrals from your existing contacts. Is your word-of-mouth marketing a success?
• New Contact Sources Referral Traffic – Are your link-building efforts paying off? Discover which sources drive most contacts through referral traffic. 
• New Contacts by Organic Socia Source – Gain insights into your effectiveness of social media marketing by analyzing the number of brand-new leads generated by social media campaigns.
• Contacts by Social Source – Discover the number of brand-new leads generated by social media campaigns and determine the effectiveness of your social media marketing efforts.

New Contacts by Source – identify your most effective lead generation channels by digging into the number of new contacts acquired from various sources within a defined period of time with this plug-and-play free HubSpot Leads by Source Dashboard .

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot account with Databox. 

Step 3: Watch your dashboard populate in seconds.

How to Score a Lead: 8 Lead Scoring Tips for Identifying Your Highest Quality Leads

Ok – so now you have lead scoring criteria and you developed a proper scoring model.

As a cherry on top, start using these 8 lead scoring tips to find (and convert) your highest quality contacts:

Clean and Verify Your Lead

Define a sql, build buyer personas, look at top-converting website pages, check conversion attributions, use the bant approach, know your limits, think about the bigger picture.

For accurate and up-to-date information about your potential customers, you need to clean and verify your leads.

First, you need a robust data cleansing tool to regularly scrub your lead database. Most of these tools can identify and rectify inaccuracies, inconsistencies, and duplications, which gives you with a more accurate foundation for lead scoring.

Also, make audits a routine in your organization. This involves verifying contact details, company information, and other relevant data points.

Jonathan Greene of Scribe suggests “using an outside vendor to clean and verify your leads.”

“There are tons of companies out there, some of which can verify that your lead is a real person, with a working email, and a LinkedIn profile, for as little as $.50 per record. Then, have them output the leads into buckets and score them according to how accurate the information is and how workable the lead is.”

Sean Dudayev of  Frootful Marketing  advises doing this with qualifying questions:

“When you ask for a name and email address, especially in something like a Facebook ad, it’s very easy for people to fall into the funnel that has no intention of taking action. Qualifying questions make it so that the potential lead is forced to assess their own situation and provide that information.

This does two things: on one end it filters out the “tire kickers” and on the other end it helps the salespeople do their job better. The marketers can use the questions as a way to score those leads from top to low priority based on the answer.”

A  Sales Qualified Lead (SQL)  is a contact that a sales rep thinks has a good chance of converting.

They usually differ from Marketing Qualified Leads (MQLs), which can sometimes be of poorer quality as many marketing departments don’t usually vet them before passing them through to sales.

That’s why defining what constitutes a Sales Qualified Lead (SQL) for your offer is another pivotal aspect.

This way, you make sure that your sales team is focusing their efforts on the leads that are most likely to convert.

One of the ways you can do this is by leveraging the criteria we talked about in our previous subheading.

Steve Cross of iSynergy adds that it’s also important to “work directly with the sales staff because they are the hardest graders of sales qualified vs. marketing qualified leads. They can provide the best insight into the type of lead they want.”

You need to understand the distinct characteristics, preferences, and behaviors of your target audience, so you can tailor your lead scoring criteria to align with the needs of potential customers.

One way you can build your buyer personas is through surveys and interviews, where you’ll collect direct insight into their pain points and goals.

Another good strategy is to analyze your current lead database for common characteristics.

Group leads with similar attributes into segments to identify trends that can make a difference in your model.

But whatever method you use to build your buyer persona, you need to keep in mind that “most leads don’t buy” as David Green of LeadCrunch says.

Green continues: “For that reason, you need to see which leads look like your best customers. What are the most important attributes? What correlations can you find between types of engagement and closed-won deals?”

Andrea Moxham of  Horseshoe+co  adds that “if you know who is currently using your products/services and understand the process in which they search for solutions, the types of content that resonate with them, the channels they use, the terms they use, and the keywords they search, you can better segment your database into good-fit leads, prospects that need more nurturing, and contacts you can refer to another vendor.”

There’s a reason your top-converting pages are doing so well – have you found it?

One of the first things you should do is see the traffic source for your page – is organic, social media, email, or something else?

Then, analyze the types and formats of content on your pages. Identify whether specific content formats (e.g., videos, infographics, and case studies) or topics consistently drive conversions.

Another best practice is to map the user journey throughout your website, focusing on the sequence of pages leading to conversions.

You should be able to recognize the common paths your high-converting leads take and adjust your scoring criteria to prioritize following similar journeys.

Dan Moyle of Impulse Creative adds that “when marketing and sales communicate and look at data from current clients (do they visit the website, which pages, how often, do they watch videos, how often do they open/click emails), and sales can tell you exactly what you’re looking for, you can find what qualifies YOUR leads the best.”

“One business may say it’s critical to know when a lead visits the “about” page. Another may say the most important factor is when someone visits a pricing page. It’s different for everyone. But when sales and marketing collaborate and look through the data, finding the most important lead score factors will happen.”

Expert Insight: Semrush is one of the best tools you can use to assess your web content efforts – but navigating through the reports can be confusing, especially if you’re a beginner. That’s why we have the free Semrush Site Audit Dashboard . Connect your Semrush account, add your most relevant content metrics, and visualize them in just a few clicks of a button. In a few minutes, you can have your dashboard up and running and ready for proper analysis.  

To refine your lead scoring strategy properly, you also need to understand the attribution of your conversions.

You should analyze how leads interact with various touchpoints before converting, so you can assign appropriate scores based on the most influential actions.

For this purpose, it’s a good idea to include multi-touch attribution models to analyze the entire customer journey.

Models like linear, time decay, or U-shaped attribution provide insights into the touchpoints that contribute most to conversions. Then, adjust lead scoring based on the importance of each touchpoint.

If you followed the previous step and found the pages your leads are enquiring through, Kris Gunnars of Search Facts says that “if the page they landed on pre-conversion is about something that your product is an excellent fit for, then that indicates that the individual is more likely to convert.”

However,  Persist Communications ‘ Grace Montealegre takes this a step further and advises to “run an attribution report to find out where they convert in the funnel.”

“Hopefully, you’ve been segmenting your marketing to see which type converts best (video is known to convert well, same with whitepapers via best practices). Is it a trial period, is it the e-book, etc.?”

The BANT approach— B udget, A uthority, N eed, and T imeline —is a classic method for qualifying leads and a great way to guide your team in focusing on prospects with the highest chances of conversion.

Using the BANT approach in your scoring strategy allows you to get more precise when evaluating the key criteria that are essential for successful sales engagement.

Samuel Wheeler of Inseev Interactive explains that “the BANT system is perfect for understanding if someone is a good lead, but the quality of good leads can change depending on the service or the products involved.”

“Creating a simple “probability” calculation based on budget and timeline will not only help score leads but will also give your pipeline a more accurate valuation.”

Andy Hoek’s team at  Invalshoek  also uses the same framework:

“So basically, does the lead have the budget to buy your solution, decision-making authority to buy it, does the company the lead works for a need for the product you’re selling and is there a timeframe for selling it? If three out of four criteria are met, then it’s a good lead.”

When scoring leads, it’s not unusual to get a bit ahead of ourselves.

But for a scoring system to work, we must understand our limitations and approach qualification with a more realistic and nuanced strategy. 

Mark Hughes of Inspired B2B Marketing says this is one of the biggest tips he can give on scoring leads.

He also adds that “the score of a lead shouldn’t be determined solely by the value it can bring to your business, but also by the value that you can provide to the prospect.”

“When scoring a lead, you need to consider whether you can provide them with the same quality of service that you provide to all of your clients. Although ambition is important when it comes to business, it should never result in a compromise on the quality of your product or service.

Biting off more than you can chew when it comes to the size of an opportunity, can result in damage to your business’ professional reputation and integrity. If you are unsure as to what your limits are, analyze your business’ resources and your processes.

These analyses should be used to make a judgment as to whether, at your current size, you have enough resources to take on the opportunity, e.g. staff and hardware, enough time to take on the opportunity, and the right level of expertise and capability that will be needed to provide a quality product or service to the client.”

While lead scoring is one of the most powerful tools for identifying and prioritizing potential customers, it’s important to view it within the broader context of your overall business strategy.

As a best practice, you should make sure that your lead scoring efforts are in line with the overarching goals of your business and that they can contribute to long-term success.

Jeff Stanislow of Chief Internet Marketer puts it like this – “Don’t just score for the sale or transaction.”

Stanislow thinks that the “lifetime value of that customer needs to be considered and applied against the cost per acquisition.”

Andrew McLoughlin of  Colibri Digital Marketing says that the “most important leads have some sort of a personal connection. Many are referrals, for example.”

That’s why McLoughlin thinks that “when it comes to scoring leads, our best advice is to consider the personal element and imagine whether your business would be a good fit. If the partnership feels like C+ compatibility, then the lead itself is probably going to score similarly.”

There’s no one-size-fits-all answer here.

In general, a good lead score is typically one that aligns with your organization’s specific criteria for identifying potential customers.

And what constitutes a good lead score depends on your business, industry, and the factors you’ve identified as important for predicting customer conversion.

That said, our survey found the majority fall somewhere between a score of 41-60, with less than 10% of a company’s overall leads reaching the 81 score mark.

However, Jay Atcheson of  R2i says that they “only see approximately 20% of our leads in the B2B space with a/any score, however, the ones that do attain a combined engagement and profile score, almost 50% lead to a CRM opportunity.”

It’s tricky (and time-consuming) to run through each lead in your CRM manually and give them an individual lead score, which is why we use tools to help us out.

Here are some of the best lead-scoring tools you can implement in your business:

HubSpot is an all-in-one inbound marketing, sales, and customer service platform and its lead scoring is based on a combination of explicit and implicit factors.

Explicit factors include information provided by leads (e.g., job title, company size), while implicit factors include behavioral data (e.g., website visits, email opens).

HubSpot’s scoring system assigns values to these factors, allowing users to prioritize leads based on their likelihood to convert.

One of its strengths lies in its seamless integration of marketing and sales tools. Plus, it provides a comprehensive view of lead interactions, making it easier to create accurate lead scoring models.

The platform also allows for lead nurturing based on the score, ensuring that sales teams focus on the most promising leads.

Expert Insight: HubSpot is undoubtedly one of the best CRM tools you can use for lead scoring – but with so many different features and capabilities available, you can easily get lost during data extraction and analysis. But with our free HubSpot CRM Overview Performance Dashboard , that’s not an issue anymore. Simply connect your HubSpot account and drag and drop the metrics you want to keep an eye on. You can have your dashboard ready in just several minutes.

Salesforce is another leading CRM platform that offers a range of cloud-based applications for sales, service, marketing, and more.

Salesforce uses a rules-based lead scoring system that allows users to define criteria for assigning scores to leads.

This can include demographic information, lead source, and engagement history. Users can customize and adjust scoring rules based on their specific business requirements.

Furthermore, lead scoring is highly customizable, making it suitable for businesses with unique lead evaluation criteria.

The platform has recently introduced AI features as well which can provide more insight into lead behavior and preferences.

Expert Insight: Similar to HubSpot, Salesforce also has a variety of reports that can make data tracking confusing even for seasoned users. If that’s an issue for you as well, you can simplify things by tracking your data through our free Salesforce Leads Overview Dashboard . With all of your key metrics in one place, you can monitor your performance in real-time and see what needs to be optimized immediately.

Pardot’s lead scoring can incorporate a wide range of data and you can use it to track engagement with marketing assets, website interactions, and email responses.

Pardot is tailored for B2B marketing, making it well-suited for businesses with complex sales cycles. The platform’s lead scoring features enable marketers to prioritize high-value leads and align marketing efforts with sales goals effectively.

Expert Insight: Want to make lead data tracking easier in Pardot? You can do that by downloading our free Pardot Opportunities and Prospects Overview Dashboard . Get a holistic view of your growth by connecting all of your key lead data insights into one place. With your entire performance overview on one screen, analysis becomes that much easier.

Marketo’s lead scoring system combines behavioral and demographic data.

It tracks interactions across various channels, such as emails, social media, and webinars, assigning scores based on predefined criteria. Users can fine-tune scoring models to reflect the unique characteristics of their target audience.

The platform’s integration with CRM systems, including Salesforce, facilitates seamless data flow between marketing and sales teams, enhancing collaboration and improving the overall lead management process.

Expert Insight: Using Marketo for your email marketing and tired of having to juggle around multiple reports each time you want to check on performance? Here’s a free Marketo Email Marketing Dashboard you can download for free to simplify things. Track everything from your open rates to conversion rates on a single screen and in real-time.

Overall, lead scoring is one of the best ways a business can assess and qualify the leads in its pipeline and determine which ones to prioritize.

But to build your lead scoring system, your data tracking and analysis have to be on point.

Depending on your specific criteria, you’re likely going to monitor traffic sources, email metrics, previous interactions, and content engagement… and you’ll do it manually, in separate spreadsheets, making the entire process that much harder.

Fortunately, there’s a much easier way you can do this – using Databox.

With Databox Dashboards , you can streamline your tracking and analysis process by compiling all of your key metrics and KPIs in a single dashboard, where you can monitor performance in real-time.

We even have dozens of Sales Leads Dashboards you can download for free and build in just a few minutes – all you need to do is choose a template, connect your data, and add the metrics you want to include. And with 100+ integrations available, you’ll have no problem finding your specific data source.

Once you wrap up the analysis, you can use Databox Reports to report your insights to other teams involved with the lead scoring process.

There’s one more cherry on top – Benchmark Groups .

The data you’re tracking and using to build your lead scoring model… how will you know whether the numbers you see are objectively good?

Unfortunately, relying on industry reports can only get you so far.

But with Benchmark Groups, you can instantly see how you compare against similar-sized businesses in your industry when it comes to performance.

And like all of our products – you can join Benchmark Groups free of charge.

Sign up for a free trial and take advantage of all of these things so you can build the perfect lead scoring model for your business.

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Filip Stojanovic is a content writer who studies Business and Political Sciences. Also, I am a huge tennis enthusiast. Although my dream is to win a Grand Slam, working as a content writer is also interesting.

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How to Calculate a Lead Score (With Examples)

• May 13, 2021

Lead scoring is a key strategy for any business that wants to efficiently handle leads at scale.

There comes a point when your focus shifts from getting enough leads to figuring out what to do with them. If you spend too much time on the wrong leads, you’ll push the wrong ones through your sales funnel and miss out on more valuable potential customers

There are plenty of services that help you calculate lead score, and many teams have taken lead scoring to a new level through AI-powered conversational lead nurturing , but no matter what specific method or service you use, understanding how to do lead scoring and how to build lead scoring models on your own empowers you to implement a proper system that will boost your sales.

Related:  Top 50 Lead Generation Tools in 2023, Ranked & Rated

What is lead scoring and how does it work?

Lead scoring is the process of assessing lead quality through a quantitative system. While the models and data you use will differ based on your business, most lead scoring involves assigning points (or a numerical value) to each lead based on their behavior or data, giving them a lead rating. This lets you decide who your sales team should spend time on, and who might need more of a push through your marketing and sales funnel through lead nurturing.

In this piece, we will provide guidelines and processes for manually building a lead scoring model, and the criteria for calculating a simpler lead score.

Collect important lead data

To create a lead scoring model, your first step is to make sure you’re collecting enough valuable data. This all goes hand-in-hand with the next step, but it’s important to start with a healthy baseline of information about your contacts. If you’re not already tracking this information , you should be.

The data you should collect fall into two sets of overlapping categories: implicit and explicit data, and this data can be behavioral or demographic .

• Explicit data is information that is factual and confirmed about your contact.
• Implicit data is data that you infer based on the data you have.
• Behavior data includes the actions your prospect takes online (e.g. whitepaper download) or in real life (e.g. brokers up to $50M in loans per year)
• Demographic data includes information like title, industry, company size.

Pick the right data and create buyer personas

Your next step is determining what constitutes your ideal customer profile (ICP) based on the data you have. Essentially, you are setting up your lead scoring criteria for your model. You can create one or more buyer personas – these are hypothetical customers who are ideal in every possible way. This is who you are looking for, and you will eventually score leads based on how closely they match this ideal customer.

To come up with the ICP, you’ll need to not only dig into your analytics, but you’ll also need to talk to both your marketing and sales team. You want to find out what qualities and actions are most common/important in your current customers, and what qualities and actions contacts took prior to becoming customers.

For example, if your sales rep is saying “Half of my recent sales had watched our last two webinars,” then your lead scoring model may weigh webinar sign-ups much more highly than expected.

Once you have these insights, rooted in the data you’ve collected, you can start to work on your model.

Determine the lead score point structure for the model

There are various ways to calculate a lead score, but we will focus on building a manual lead scoring model. While this is the most time-intensive system, the elements of doing it manually are the basis for any lead scoring model that may use more complex calculations or lead scoring analysis software.

Manual scoring means you will assign positive or negative points based on the data you have on each lead. You can break these point assignments into four buckets: demographics, behavior, deductions, and relationship.

How to determine a score for each metric in your model

You want your lead score to be rooted in the data, which means it’s time to do some math. To see how a specific metric impacts the total close rate, you must first come up with the baseline close rate by dividing the total number of leads by the total that became sales.

Once you have this rate (let’s say 2%, for example), this is the number you’re looking to beat when comparing subsections of your leads based on the data you’ve collected. In other words, if you’re assigning positive points, this reflects an improvement on that 2% close rate, and the more points you assign, the higher the increase on the close rate.

Let’s say that you find that your close rate for leads who followed you on social media is 5%, and for those who attended a webinar it’s 15%. You could assign a point value accordingly (perhaps five and 15 points). Perhaps it turns out that any lead who hasn’t opened any of the last 10 newsletters has a close rate of .05% on average. You could assign them a negative point value.

These points are the basis of your lead scoring model. You can calculate these points based on increments or multiples of improvement from your baseline, or, if you have the resources, you can work with data scientists on your team to come up with a logistical regression analysis.

Not everything will work out perfectly in one-to-one multiples, and you may not always have enough data to know with full certainty how much a specific metric might affect the close rate. Thus, feel free to adjust your points system in a way that works best for you, and trust your team when figuring out what’s important and what’s not.

In the beginning stages, it’s best to start with a simple lead scoring model and work your way toward something more comprehensive. Don’t try to build Rome in a day.

Demographic lead scoring examples and criteria

Once you have your demographic data and have determined which pieces of data matter, it’s time to sort them into a hierarchy: critical, important, and influencing.

You’ll then assign more points the higher up the data point is. Here is a sample scoring framework:

Notice that the same property can appear in multiple levels of importance. For example, there are hundreds of titles that leads could have, so you need to find a way of breaking them into groups that you can score accordingly.

While we did not list any negative point assignments in this section, these are also important, and we discuss them in the deductions section.

Lead scoring template: behavior data

Just like with demographics, you can take your lead behavior and rank them in the critical, important, and influencing hierarchy.

Remember not to go haywire by assuming that any online engagement means a lead is sales-qualified. Consider all of the ways in which a specific behavior could be a red herring, and remember to rely on your sales and marketing teams when deciding what behaviors are most important.

Here is a sample behavior scoring sheet:

There are hundreds and hundreds of behaviors that may be worth tracking, so don’t be afraid to really dig in here. Just make sure that the behaviors you’re tracking each are telling you something new about the lead.

Deductions to your lead rating

Not all of your points will be positive. An unsubscribe, for instance, is not a positive indicator, but it is still valuable information. Similarly, an entry level job title (or title in the wrong department) wouldn’t be ideal for most companies. Here are some common deductions:

• Unsubscribe, unfollow, or add to do-not-call list
• Wrong job title
• Wrong country/location
• Company size not the right fit
• Completely inappropriate estimated budget
• Non-product website visit (eg, careers page)
• Event no show
• Not replying to one-on-one communications

Remember, these aren’t always dealbreakers. Make sure to include relevant deductions, but don’t assume that one deduction means the contact should be dropped.

One area that is most important for deductions is relationship, but this can be more nuanced than a simple negative point assignment, which is why we’ve given it its own section below.

Measuring relationship through rules-based lead scoring

The relationship with the contact is extremely important, and often overlooked. Imagine someone downloads three whitepapers, attends two webinars, and interacts with over 80% of newsletter blasts. They may seem like an ideal lead based on these behaviors, but actually be a student, researcher, or even blogger, and would then certainly not be worth moving through the sales pipeline.

Depending on your business, the types of relationships that matter will differ, but are some main relationships to consider.

Unlike the point scores for the other buckets, relationships may be more complicated. Rather than assigning points, you could use relationships as a classifier to decide on your action plan. For example, previous customers might go through a different funnel, or even be paired with a more senior sales rep when they become sales qualified again.

Make sure that you aren’t just assigning a zero value and getting rid of students, bloggers, etc., just because they are unlikely to become customers. They may help spread the word about your company (for free) through research, talks/presentations, articles/blogs, or social media.

Create a sales and marketing action plan

Now that you have a robust lead scoring point framework, it’s time to put everything together and make an action plan. This is arguably the most important part of your lead scoring system, because even if you have the most accurate scores imaginable, you might be wasting leads if you aren’t taking the right actions with them.

The action plan is what you will actually do with the lead at each score (send to sales, nurturing, etc). Without this in place, your lead score is just a number with no effect on your sales or marketing efforts.

The main thing to consider is when leads should go to sales, and when they should receive more nurturing. You’ll want to use a combination of the behavior score and demographics score to determine how to move forward, as a high score in both represents both lead fit (similarity to your ICP) and lead intent (interest and ability to become a customer).

Make sure your demographics score is at or below 50% of the total score makeup—even if someone seems like a perfect lead based on demographic data, if they have no discernable interest in becoming a customer, they need more nurturing.

Additionally, it’s important to consider how you’re going to nurture your prospects into leads. If you have the capacity to determine not only how strong a lead’s score is, but also what they need to move further through your funnel, you have a winning strategy. Therefore, your funnel should try to push leads to increase their score, not stay stagnant.

This most comes into play with lead relationships. If you have this data, you may want to implement different action plans (marketing funnels) based on the relationship. Additionally, you don’t want to send irrelevant nurturing campaigns to people with incompatible roles at their respective companies. This is an advanced strategy, but worth considering as you develop your lead scoring action plan.

Iterate, expand, explore on your lead scoring model

A lead scoring strategy is not a “one and done” undertaking. Your model should be an adaptable machine that grows with your company. Run regular check-ins to make sure it’s as effective as possible, and consider ways you can improve on your lead scoring process.

One of the best ways to implement a lead scoring system is through technology, as clearly the more data you try to score, the harder this gets. That’s why many companies choose to use predictive lead scoring, which harnesses the power of machine learning to calculate these numbers for you. This lets you analyze copious amounts of data much faster than even your best data scientists could.

Many of these AI-powered lead scoring systems also help automate lead nurturing efforts. While these save heaps of time and the stress of calculating this all yourself, it’s important to understand how manual lead scoring works, particularly as you decide what data to track and feed to the predictive lead scoring service, and how to best integrate them as part of your workflow.

A winning lead scoring model: the big picture

Ultimately, successful implementation of lead scoring can improve your campaigns and marketing efforts and maximize the efficiency of your funnel. Plus, it aligns your marketing and sales teams by turning them into a cohesive machine that is more than the sum of its parts.

While the specific formula and methodology may differ based on your company and ideal customer profile, lead scoring is a key element to any scalable sales and marketing strategy.

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Problem Statement ¶

An education company named X Education sells online courses to industry professionals. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead.

Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’ . If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone. A typical lead conversion process can be represented using the following funnel:

"The company requires a model that will assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance."

Data Cleaning ¶

Issues by column: ¶.

By going through the above unique entires of every column, we have observed the following inconsistencies:

lead_source -> WeLearn and WeLearnblog_home are one and the same. Facebook and social media are two different categories.

country -> "Australia", the asian Countries and "Asia/Pacific Region" are two different categories.

tags -> "invalid number" and "wrong number given" are one and the same.

last_activity and last_notable_activity are highly correlated. One of them should be dropped to avoid multi-collinearity.

Other potential issues: ¶

• Many columns exist with only a single category (Redundant columns).
• same category percieved as different due to not matching case. (ex. Google -> google)
• The value Select is equivalent to NaN.
• Possible overlaps may exist in City column.

Dealing with NaN values ¶

Some features have hierarchial categories, which requires ordinal encoding. But then the features will not be scale invariant. It is thus better to one-hot encode all categorical variables.

5 rows × 56 columns

Correlations ¶

Univariate and outlier analysis ¶, bivariate analysis ¶, building the predictive model ¶, splitting into train and test ¶.

We will perform stratified split to prevent the effect of class imbalance of target variable, thus preventing bias.

Scaling (Normalisation) ¶

We will perform Min Max scaling on the Continuous numerical variables

Recurssive Feature Elimination and Cross Validation ¶

By iterating through number of features we will see which model gives highest result.

Here we'll plot how accuracy changes with number of features considered.

The VIF of total_time_spent is too high. We will eliminate the feature and check the VIF again.

The factors for the columns are now in check, we can proceed further with our analysis.

Assesing the model with statsmodel ¶

Fitting the model ¶

Measuring model performance ¶.

The data available at hand has class imbalance and therefore accuracy is not a good enough metric to measure if model is good enough.

Sensitivity (Recall) tells us what percentage of leads that were converted, were correctly identified as converted .

Specificity tells is what percentage of leads that were NOT converted were correctly identified.

Precision is, given a positive test result, the sample is positive.

If correctly identifying positives is important for us, then we should choose a model with higher Sensitivity. However, if correctly identifying negatives is more important, then we should choose specificity as the measurement metric.

F1 score is the weighted average of the precision and recall, and is a good metric to hold the model against.

Finding Optimal Cut-Off ¶

Measuring performance on test set ¶.

Finally, we have an overall accuracy of about 0.85 on our Logistic Regression model. That is, there is 85% chance that our predicted leads will be converted. This meets the CEO's target of atleast 80% lead conversion.

Lead Scoring ¶

We will perform lead-scoring on the test set.

Pardot Lead Scoring: Best Practices for Enterprises

Pardot lead scoring is a powerful way for enterprise marketers to track prospects, segment leads by level of interest in order to deliver tailored content, and qualify leads so they can be assigned to the appropriate sales team. If you’re still not sure about using Pardot for the enterprise, we’ve written about Pardot and why it’s enterprise-ready in a previous post so you can get up to speed on the product’s latest features and developments.

While Pardot’s robust B2B marketing automation solution can increase your organization’s sales pipeline and help your sales teams close more deals, one of the challenges many marketers face is setting up a successful lead scoring model. 

A strategic approach to scoring leads in Pardot is important for sales productivity and the health and effectiveness of your marketing funnel. At CloudKettle, we use a Pardot lead scoring framework and some best practices to help enterprises overcome this challenge–and we’re sharing it all with you in this blog post.

“A strategic approach to scoring leads in Pardot is important for sales productivity and the health and effectiveness of your marketing funnel.”

How Does Pardot Lead Scoring Work?

First, let’s look at how Pardot lead scoring works. Since all leads are not created equal, implementing a lead scoring system ensures that qualified leads are passed to the sales team while remaining leads stay with the marketing team to be further nurtured.

With Pardot, there are two main factors that determine how a prospect is qualified:

• The Score, which represent activities taken or not taken by a prospect, and
• The Grade, which represents how closely aligned that particular prospect is with your organization’s “ideal customer profile.”

When it comes to calculating the lead score, there are a couple of layers to consider. You can think about it like LEGO blocks.

“…implementing a lead scoring system ensures that qualified leads are passed to the sales team while remaining leads stay with the marketing team to be further nurtured.”

In the initial layer, you have your standard default scores. For example, the number of points assigned for every web page visited, emails opened, links clicked, and so on. Default scores treat everything the same.

However, additional layers of points can be added to the score based on a number of additional factors you can set up. Examples include: adding points when a specific form is filled out or when a particular web page is visited. This allows you to identify high-value items and grant additional points to qualify leads faster.

The third layer of scoring involves “scoring categories.” These allow for specific categories to be created–by product line or industry, for instance–and automation rules can then be used to associate actions and scores with those categories. Let’s look at an example.

Here at CloudKettle, if we have a prospect with a score of 100, we might see 75 points come from “marketing cloud” content, 10 points from “sales cloud” content, and the remaining 15 points from untagged items (i.e., items we have not associated with a custom category).

Scoring categories provide valuable insights about our prospects. In this example, we know the prospect is most interested in “marketing cloud” topics, so we can deliver more relevant content or have follow-up conversations.

Configuring Scoring Points

Scoring points are configured in “Automation Settings.” You can create a default or baseline scoring system, then layer on additional scoring criteria via Automation rules. These rules can be customized based on an enterprise’s needs and goals and include things like: automatically tagging prospects who meet certain criteria, moving leads through a custom nurture sequence based on their actions, and countless other automations.

Here’s what the lead scoring process looks like in a nutshell:

• A prospect enters your organization’s database via an opt-in form or another source, such as via the Salesforce sync, the Pardot API, or list uploads
• Each prospect accumulates points based on a variety of factors, such as opening emails, clicking links, and so on
• Prospects are nudged along in a variety of customized lead nurturing campaigns
• Once a prospect has accumulated enough points to be classified as “Marketing Qualified,” the prospect is assigned to a sales representative or business development specialist for follow-up

Keep in mind that prospects can also generate points even if they don’t opt into an email list through actions such as website visits. For example, filling out a web form would register the prospect and points would be assigned.

It’s also important to keep in mind that negative scoring does exist. Let’s look at that next.

What Are Decaying Lead Scores in Pardot?

One common mistake marketers make in lead scoring is not utilizing negative scoring. For example, if a prospect has been inactive in your Pardot database for several months, they may not be a truly sales-ready lead, despite a good score overall. Similarly, prospects that take years to reach a high score aren’t necessarily strong leads, either.

“One common mistake marketers make in lead scoring is not utilizing negative scoring.”

In such instances, we recommend assigning prospects negative points in order to ensure Pardot database accuracy. One way to do this is to assign periods of inactivity to your prospects’ various positive scores. Accurate lead scoring–which includes negative scoring–ensures a healthy sales funnel, optimized sales efforts, and team productivity.

Without setting up your Pardot lead scoring system to take into account the decay of a prospect’s value to the enterprise, you risk inflated scores that could lead to wasted time and marketing efforts.

Bottom line: Reduce your prospects’ lead scores if they haven’t interacted with your marketing recently or if they have a low level of engagement over a long period of time.

“…we recommend assigning prospects negative points in order to ensure Pardot database accuracy.”

Pardot Scores vs. Grades

Before we get into the lead scoring framework, let’s dig deeper into “scores” versus “grades” as this is an important distinction.

As mentioned earlier in this post, a lead’s score–or accumulation of points–should not be confused with a prospect’s grade. In Pardot’s scoring framework, the better the grade, the better the fit. This also means that a prospect with a high grade can have a lower score (i.e., fewer points) and still be considered a qualified lead.

“…a lead’s score–or accumulation of points–should not be confused with a prospect’s grade.”

In other words, the higher the grade, the more likely the lead will get passed on to the appropriate sales team. But how are grades assigned? And what does the sales hand-off look like?

In the next section, we’ll introduce you to the lead scoring framework and lead assignment process we use here at CloudKettle.

“…the higher the grade, the more likely the lead will get passed on to the appropriate sales team.”

Lead Scoring Framework

By default, Pardot assigns points to various prospect actions, all of which can be customized. These include actions like form submissions and landing page opt-ins, among a lengthy list of other actions. Pardot’s full default scoring system can be found in the Salesforce Trailblazer Community .

When we work with enterprises on their Pardot databases, we often help them set up a custom scoring approach. This means points might be weighted differently from company to company. Still, the lead scoring framework below includes a grading “rubric” and can be used to qualify leads regardless of how points are assigned.

“When we work with enterprises on their Pardot databases, we often help them set up a custom scoring approach.”

Weighted Lead Scoring in Pardot

Now that the grade and score have been calculated, a weighted lead scoring model is used to determine what constitutes a qualified lead.

The next section illustrates how you would have a weighted system. That is, the lower the grade, the higher the score needs to be in order to be considered “qualified.”

In addition to default or customized scoring based on points, CloudKettle recommends taking other grading criteria into consideration as well. Weighted grading criteria adds a layer of valuable insights into the particular interests that drive up a prospect’s score.

Grading and Lead Assignment

Again, lead scoring is used to keep prospects within the marketing department until they have been deemed “sales-ready.” But how do you know when your leads are ready to be assigned to sales?

Let’s assume a fictional company, EnterpriseCo, uses a lead scoring model like the one below to determine how well a prospect fits EnterpriseCo’s “ideal buyer profile.” There are two components that make up the prospect’s grade: the overall weight for that specific criteria as well as the values that relate to that criteria.

With a solid scoring foundation in place, Pardot can automatically assign leads once they reach a certain threshold. Remember, the better the grade, the lower the score (i.e., points) can be. Conversely, this also means the lower the prospect’s grade, the higher their activity score needs to be.

“With a solid scoring foundation in place, Pardot can automatically assign leads once they reach a certain threshold.”

Below is a sample grading rubric from EnterpriseCo:

As with the scoring model, we recommend regularly reviewing the grading rubric so that it always reflects current business needs and practices.

Pardot is a powerful marketing automation platform for the enterprise. It provides marketers an effective way to track, segment, and qualify leads so they can be assigned to the appropriate sales team.

Yet, setting up a successful lead scoring model is often a marketer’s biggest challenge. In this post, we provided an overview of Pardot lead scoring best practices as well as introduced you to a lead scoring framework so you can start building and maintaining healthier, more productive sales funnels.

We hope you find the actionable insights provided here to be helpful. If you have questions about this blog or how to improve your Pardot database, reach out today . We love helping enterprise companies succeed with Salesforce.

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Lead Scoring

Whether your business is growing or you work with a high volume of leads, lead scoring will help you address the most important leads quickly and prioritize the rest. Check out our Ultimate Guide to Lead Scoring here on the Close blog for an even more in-depth look at how to score leads, regardless of the CRM you’re using. For learning about lead scoring in Close, fear not—you’re still in the right place. In this article, we’ll walk you through lead scoring in Close by using Custom Fields, Custom Activities, and Smart View filtering.

• Step 1: Find your ideal customer

The way we score our leads at Close will likely differ from how you want to score yours. The first step in understanding which leads you should prioritize is to find your ideal customer. We wrote a full post and created this template for you if you don't already have an ideal customer.

Need more inspiration? Our Blog has you covered: Lead Scoring Nurture Your Leads Convert More Leads

• Step 2: Determine your ideal customer criteria

Compile a list of criteria that your ideal customers have in common. Here are some examples:

Company size

Funding or financial situation

They opted-in for an event or a newsletter, or they filled out a form on your website

They signed up for a trial

They came from a specific ad

• Step 3: Track criteria in Close

Track the criteria from Step 2 using Custom Fields , Custom Activities , or any other Close data points.

You might need to add new Custom Fields to your Close setup .

If you need to bulk update or add Custom Fields for hundreds or thousands of leads, we recommend these options:

If you can easily find the leads you need to update using another data point such as a lead status, an address in a given area, an Opportunity, etc., use Search to filter the leads you need and bulk update the Custom Field by clicking the three dots at the top-right corner of your list, choose Bulk Edit , then Update a Custom Field . Select the Custom Field and value you want to add.

If you can't easily search and filter the leads you want to update, export your leads as a CSV and add one column per Custom Field. When you re-import your leads, make sure you check for duplicates to add the new value in that column to your existing leads rather than creating brand-new duplicate leads.

Make sure you are using the right Custom Field types for each individual piece of data. Examples:

Company size could be a multiple choice if you store it as a range or a number if you want to create a range using Smart Views.

Industry could be a multiple choice .

Job title could be a multiple choice .

Source could be a multiple choice .

Funding could be a number .

Even opt-in , trial signup , or ad source could be a multiple choice .

Lead enrichment third-party tools Bring important criteria into Close from external tools such as LeadFuze , Clearbit , Lusha , and ZoomInfo . Automate lead enrichment with most of these tools using Zapier, a native integration, or our open API . LeadFuze: Native integration Clearbit: Zapier Lusha: Zapier ZoomInfo: CSV (spreadsheet)

• Step 4: Create Smart Views

Use the criteria you've stored in Close to create a Smart View for each score or prioritization level. Here are some examples.

High-Priority Leads :

Company size: 100+ employees

Industry: Technology

Job title: Manager or Director

Funding: $500,000+

They have an opt-in or signed up for a trial

Medium Priority Leads :

Company size: 50-99 employees

Funding: $250,000+

They have an opt-in

Low Priority Leads :

Company size: 1-49 employees

It doesn't show up in High or Medium Priority Leads Smart View

After you create these Smart Views, access them from your left sidebar. Get into a daily or weekly routine checking these Smart Views; your next opportunity could show up.

Step 5: What's next?

Once you've scored and prioritized your leads, you can use these lists in a multitude of scenarios. Here are a few examples:

Lead assignment : You can assign higher priority leads to senior reps or keep low priority leads for onboarding reps.

Task prioritization : Your sales reps can use these lists to prioritize their day. For example, if you use the "Lead Owner" Custom Field to assign leads to your reps, they can add "custom.lead owner" in ("Me") to any of the above Smart Views to see their high, medium, and low priority leads.

Processes and workflows : If your follow-up processes differ depending on a lead score or priority, use your new scoring lists to set up a Workflow for your reps and automate your sales process.

What's Next

• Simplify Your Workflows with Automated Senders

Table of contents

© Elastic Inc

Lead Score Case Study

Problem statement.

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

There are a lot of leads generated in the initial stage, but only a few of them come out as paying customers. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating etc. ) in order to get a higher lead conversion.

X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

Goals of the Case Study

Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.

Reading Dataset

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Lead scoring assignment upgrad

swapnilranjan/Lead-Scoring-Assignment

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Problem Statement An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone. A typical lead conversion process can be represented using the following funnel:

Lead Conversion Process - Demonstrated as a funnel Lead Conversion Process - Demonstrated as a funnel As you can see, there are a lot of leads generated in the initial stage (top) but only a few of them come out as paying customers from the bottom. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating etc. ) in order to get a higher lead conversion.

X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

Data You have been provided with a leads dataset from the past with around 9000 data points. This dataset consists of various attributes such as Lead Source, Total Time Spent on Website, Total Visits, Last Activity, etc. which may or may not be useful in ultimately deciding whether a lead will be converted or not. The target variable, in this case, is the column ‘Converted’ which tells whether a past lead was converted or not wherein 1 means it was converted and 0 means it wasn’t converted. You can learn more about the dataset from the data dictionary provided in the zip folder at the end of the page. Another thing that you also need to check out for are the levels present in the categorical variables. Many of the categorical variables have a level called 'Select' which needs to be handled because it is as good as a null value (think why?).

Goals of the Case Study There are quite a few goals for this case study.

Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted. There are some more problems presented by the company which your model should be able to adjust to if the company's requirement changes in the future so you will need to handle these as well. These problems are provided in a separate doc file. Please fill it based on the logistic regression model you got in the first step. Also, make sure you include this in your final PPT where you'll make recommendations.

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