customer lifetime value case study pdf

Customer lifetime value: The customer compass

Traditional brand owners and retailers are increasingly encroaching into the e-commerce channel—and for good reason. After all, digital engagement with customers provides companies with valuable data on consumer behavior that allows them to optimize marketing and product development. In addition, by operating their own sales channel, providers retain control over user experience and brand image. Enter COVID-19, and suddenly the Internet is rapidly becoming the shopping channel of choice for more and more consumers, a trend that is likely to persist beyond the pandemic.

About the authors

This article was a collaborative effort by Max Ackermann , Karel Dörner , Fabian Frick, Marcus Keutel , and Philipp Kluge.

That said, the new e-commerce players also face a challenge: winning and retaining customers is an expensive affair. That is why it is crucial for success to invest primarily in those customers who are lucrative for the company in the long run. It is important to understand these customers intimately, to engage them with the right channels, and to tailor offers to their context and needs. This can only be achieved by drawing on customer-related metrics—of which customer lifetime value (CLV) is first among equals—and by interlinking them intelligently as the foundation for effective and efficient marketing.

“CLV is our core steering metric” Four questions for Emmanuel Thomassin, Chief Financial Officer of Delivery Hero

Just how important is clv for delivery hero.

Customer lifetime value is one of our core metrics. It’s a topic we’re driving intensively at all levels of the organization and we have set clear goals from which we can only deviate in exceptional cases. We use CLV to support our strategic and operational decisions, such as whether to enter a new market or whether to continue or end a marketing campaign.

So how do you go about that?

We have a standardized approach for all our markets. CLV monitoring is directly linked to our operational marketing systems and thus directly influences our investments. It is crucial to continuously adapt and improve the calculation of CLV and the corresponding operationalization in marketing. Initially, for example, we only calculated the cost of customer acquisition; meanwhile, we also know what it costs us to retain customers. Over time, we have learned an enormous amount, especially through monitoring in new markets and with the introduction of new business models.

Do you see a trend among your customers?

Perhaps the most important KPI-based analysis is breaking down customers into cohorts to fine-tune the targeting of marketing drives. Over the years, we have seen CLVs rising steadily across all our cohorts, even before Covid. Today, they are many times higher than the investments in customer acquisition and retention. This shows that our marketing measures, among them personalization, have a direct impact on the KPIs and thus on Delivery Hero’s success.

Do you still see untapped potential in the way you use CLV?

A major challenge is responding directly to improvements in the KPIs with measures designed for pinpoint accuracy, especially in marketing, and then evaluating these with precision. That’s something we are working on, aside from further refining our analytics and increasing their granularity to the level of geographical micro-cells.

Digitally aligned companies and start-ups have long been successfully applying and refining this approach (see sidebar, “‘CLV is our core steering metric,’ Four questions for Emmanuel Thomassin, Chief Financial Officer of Delivery Hero”). Many traditional manufacturers and retailers, on the other hand, still have some catching up to do. To make the most of the CLV approach and use it to manage their e-commerce business, they should adopt a long-term strategy and proceed systematically in three steps: collect data, determine true customer value, and target investments to the most valuable customers.

Collect data throughout the customer journey

To estimate the current and future value of customers and keeping privacy regulations in mind, companies need to collect relevant data points on as many customers and their behavior as possible over multiple years. This is because the corresponding analytical models are dependent on the availability of sufficient amounts of information to identify relevant patterns. The greater the volume of data available, the more meaningful and accurate the analyses. Three categories of data are required:

Transaction data such as shopping timeline, product information, prices, method of payment, delivery, or returns are supplied by the e-commerce platform and the connected financial systems.
Demographic data such as gender, age, occupation, and place of residence are condensed into customer profiles in order to better predict future shopping behavior and personalize marketing actions.
Marketing data such as search behavior, response to campaigns, and external online data help to flesh out the respective customer profile and, in turn, deepen customer knowledge, including as regards preferences or purchasing behavior.

Despite ample data, it is often difficult to clearly identify customers throughout the entire customer journey. This is partly due to purchases made across different channels, for instance, in the company’s own online and offline stores or perhaps through third-party suppliers such as retail partners, which often do not require registration (with an e-mail address, etc.) for identification.

Successful providers solve this problem with an integrated customer database (customer data platform) that can recognize customers even when they do not sign in. For this purpose, profiles comprising as many attributes as possible are created for visitors to the various channels (based on browser data, among other things). Then, returning visitors (including to different channels) are identified by matching them against the full array of profiles compiled. Aside from linking different data sources and formats, the customer data platform also enables the integration of suitable external systems as well as customer segmentation according to behavior and demographic data. Key steps in this context include anchoring the system’s continuous improvement, but also data use by the organization’s departments from the outset.

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Determining the true value of customers.

What happens to the data collected? Here, in the second step, is where customer lifetime value (CLV) comes into play. This is because it can be used to measure a customer’s value, in the long term, over their entire time as a customer of the company. This value is compared with the customer acquisition/ retention costs (CAC), i.e. the marketing investments made or planned that are necessary to acquire and retain the customer. Finally, both indicators are linked to derive recommendations for action with regard to strategic and operational decisions (Exhibit 1).

A distinction is made between three levels of complexity when modeling CLV and CAC:

The descriptive model calculates CLV using historical consumer data and identifies behavioral patterns of customer groups mostly through simple manual analysis. This comparatively simple method yields rapid results, but they are merely hypotheses and therefore of limited value; they can only serve as an initial indicator of CLV for potential decisions.

The predictive model uses historical data patterns to determine future CLV. Consequently, the results are more accurate and meaningful as the customer’s individual profile is factored into the equation along with their remaining time as a customer. Backed by this knowledge, CLV managers can make more effective decisions. However, this model requires more comprehensive advanced analytics capabilities, such as customer identification across multiple channels. For a 360-degree view, it is worth having complete historical customer data as well as regular updates of sales and cost data.

The operative model goes one step further: it automatically predicts CLVs using machine learning and makes initial recommendations for decisions, amplifying the CLV effect. In addition, predictive accuracy and decision making improve with each update. For the operational teams, this means that rather than elaborating decision recommendations, their primary job is to review and continuously monitor them. Yet, creating such models is a much more complex endeavor that can take months, if not years.

For all three models, continuous updating data and calculations is indispensable. For example, CLV must be adjusted after each customer purchase, but the CAC value must also be increased if, for instance, a marketing campaign is launched for a specific customer group. This is essential so that the data and the associated analytics results can be used for future campaigns.

Targeted investment in high-value customers

The last and most important step is to evaluate the CLV and CAC computations in such a way that the company can derive strategic and operational recommendations for action and decisions from them. It is essential to consistently measure the impact of the respective decisions, for example, the increase in CLV as a result of certain marketing measures.

With regard to the depth of evaluation, a distinction can be made between three levels. At the first level, only the average of all customers is considered, although this can already be very helpful when making decisions about expansion into new markets, channels or brands, for example. As a rule of thumb, expansion is advisable as soon as the estimated CLV exceeds the CAC by a multiple—even if profitability has not yet been reached. In practice, mature digital business models should display CLV-to-CAC ratios ranging between at least 2:1 and up to 8:1 or more. At this level, CLV can also be used as a metric to measure and improve the performance of organizational units such as country branches, or to gain a more customer-focused perspective on the business (rather than a purely sales- and profit-centric view).

The next analytical level focuses on cohorts of customers clustered on the basis of their CLV and CAC values in order to improve operational decisions in particular. Demographic data, such as gender, age, place of residence, but also behavioral data such as purchase frequency, brand loyalty, and returns are typically taken into account.

These data sets help marketing and sales teams identify indicators of high CLV and low CAC respectively, and tailor marketing campaigns to individual cohorts. Exhibit 2 illustrates an example of such a cohort analysis: in addition to the distribution of customers among the various CLV levels, it shows the characteristics of less lucrative and particularly valuable customers, as well as the recommended actions that can be derived from them.

Finally, at the third level with maximum analytical depth, the model targets individual customers. However, this only makes sense if the company is operating advanced, automated marketing platforms. Otherwise, individual marketing efforts would be prohibitively expensive and the cost of acquiring and retaining customers would skyrocket.

In a nutshell, CLV offers both established and new players in the e-commerce space the opportunity to better understand, target, and serve their customers in order to engage them in an effective and efficient manner, and create value for them. However, the operationalization of CLV and CAC can also set in motion entirely new developments. For example, successful e-commerce companies are moving to build a network of physical stores to operate in tandem with their online business. Take, for instance, Mr. Spex, Europe’s largest online eyewear retailer, which is opening more and more stores in German cities to attract new customers and create an omnichannel experience. A response to the rising cost of customer acquisition and retention in the online channel in recent years, this trend capitalizes on the fact that it is often cheaper and more efficient to strike the desired CLV-to-CAC ratio in conjunction with offline channels.

Max Ackermann is an associate partner in McKinsey’s Berlin office; Karel Dörner is a senior partner in the Munich office, where Dr. Philipp Kluge is an associate partner; Marcus Keutel is a partner in the Cologne Office; and Fabian Frick is a project manager in the Frankfurt office.

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9 inspiring case studies of Customer Lifetime Value (CLV)

Marketing: How much should I spend to acquire a customer?
Product: How can I offer products and services tailored for my best customers?
Customer Support: How much should I spend to service and retain a customer?
Sales: What types of customers should sales reps spend the most time on trying to acquire?
Design: What type of graphic designs are required to attract customers? Which tool is used to design logos, Color palettes, and Icons?

To calculate Customer Lifetime Value, here is how to do it. If you want examples of brands that are making the most of it, here are 9 inspiring case studies of Customer Lifetime Value (CLV).

AMAZON : Consumer Intelligence Research Partners estimates that Amazon Kindle owners spend approximately $1,233 per year buying stuff from Amazon, compared to $790 per year for other customers. So Amazon pays close attention to Customer Lifetime Value (CLV). Amazon Prime has been developed to enable Amazon to efficiently compete on price and to increase customer lifetime value. According to a 2013 study by the Consumer Intelligence Research Partners, Amazon Prime members spend $1,340 annually. And that was 3 year ago. It’s more now. By applying Customer Lifestyle Value (CLV) to the development of Amazon Prime, Amazon knows how to get the most out of their most profitable customer segments.
BONOBOS : Is a leading e-commerce driven men’s apparel brand focused on delivering great fit, a fun approach to style, and superb customer experience . Bonobos has always been a data-driven, customer-focused retailer. With Guideshops, Bonobos has service-oriented e-commerce stores that enable men to try on Bonobos clothing in person before ordering online. Bonobos discovers that Guideshops bring in customers with the highest lifetime value across all of its marketing channels. Insights into which channels are attracting Bonobos’ highest-value shoppers has helped Bonobos increase the predicted lifetime value of its new customers by 20% .
CROCS : has always had a data-driven, customer-centric approach to marketing. When the marketing team is given a mandate to transform Crocs’ online business by becoming less reliant on promotions and discounts, the team is excited by the opportunity to improve Crocs’ profitability. The team tests to optimize promotions aimed at customers who are predicted to churn, and expands programs to coordinate a “no discount” experience across site, email and display for customers with the lowest price sensitivity. Crocs realizes 10X and 2X lift in revenue.
HEAR AND PLAY MUSIC : A provider of music lesson products, uses automated lead nurturing and scoring to turn prospects into customers and repeat customers. Many of the company’s products cost less than $100. With automated messages that have a personalized tone to high value prospect, the company has seen: 1) 416% increase in Customer Lifetime Value, 2) 67% increase in click through rate from the best prospects (increased from 24.73% to 41.28% for subscribers with the highest lead scores) and 3) 18.4% improvement in lead-to-purchase time.
KIMBERLY-CLARK : According to Nielsen, the typical family spends over $1,000 on diapers and baby wipes during the two-and-a-half years their children are in diapers. A Nielsen study was able to quantify the dollar value of key consumer segments, the critical nature of brand selection at various points in the consumer lifecycle and distinct differences in channel choices through key points in the baby care lifecycle. Kimberly-Clark has a clearer picture of its target market and where its greatest marketing and promotional opportunities exist to extend and expand their market share. “Nielsen’s lifetime.
NETFLIX : An average Netflix subscriber stays on board for 25 months. According to Netflix, the lifetime value of a Netflix customer is $291.25. Netflix knows that customers are impatient and some customers cancel because they don’t like waiting for movies to arrive in the mail. Due to this they’ve added a feature where you can stream movies on the web, which not only satisfies your movie urge, but it keeps you busy while you are waiting. By tracking these stats and behavior, Netflix has reduced their churn to 4%.
STARBUCKS : One of the most effective ways to boost Customer Lifetime Value (CLV) is to increase customer satisfaction. Bain & Co has found a 5% increase in customer satisfaction can increase by 25% to 95%. The same study shows it costs 6 to 7 times more to acquire a new customer than keep an existing one. Starbucks’ customer satisfaction has been reported as high as 89%. Due to high customer satisfaction, Starbucks’ Customer Lifetime Value has been calculated at $14,099.
U.S. AUTO PARTS : Realizes the competitive advantage of loyalty and decided to invest. The company debuted the Auto Parts Warehouse loyalty program, known as APW Rewards. U.S. Auto Parts began to leverage capabilities such as increased rewards for high-margin products, personalized post-purchase enrollment offers, a status tier, and triggered email campaigns based off of a person’s repurchase history to maximize customer lifetime value. U.S. Auto parts increased its spend per member by 20%, its repurchase rate by 14%, and its enrollment rate by 45% after updating the loyalty program of its flagship brand,
ZAPPOS : Has found people who regularly return items can be some of your best customers. It says that clients buying its most expensive shoes have a 50% return rate. Placing a priority on Customer Lifetime Value, Zappos has identified their best customers have the highest returns rates. They are also the ones that spend the most money and their most profitable customers. That’s why Zappos has a 365-day returns policy, free two-way shipping and doesn’t charge for returns.

Do the way these companies pay attention to Customer Lifetime Value inspire you with ideas for your company. Do you want to learn more about making the most of out of CLV.

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Calculating Customer Lifetime Value (CLV): Theory and Practice

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Paul Andon 2 ,
Jane Baxter 2 &
Graham Bradley 2

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This paper examines the calculation of customer lifetime value (CLV). Two case studies from Australasian practice are used to describe how CLV is calculated. These case studies reveal that customer retention rates, customer acquisition costs and the present value of future expected base profits are incorporated into the calculation of CLV. Other drivers of CLV, such as revenue growth, cost savings, referrals and price premiums, were not significant in this examination of practice. Practitioners indicated that these other drivers were not used for two reasons: first, they were unable to readily quantify them; and second, they expressed doubts as to their significance. In brief, it appears that practitioners are developing simple and feasible representations of CLV to use in business decision-making.

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Andon, P. (1997): Customer Valuation & the Role of Accounting in the Loyalty Context, Honours Thesis, University of New South Wales, Australia.

Google Scholar

Atkinson, A.A./Banker, R.D./Kaplan, R.S./Young, S.M. (1997): Management Accounting, 2nd ed., New Jersey.

Bellis-Jones, R. (1989): Customer Profitability Analysis, in: Management Accounting, February, pp. 26-28.

Berry, S./ Britney, K. (1996): Market Segmentation: Key to Growth in Small-Business Marketing, in: Bank Management, Vol. 72, No. 1, pp. 36–41.

Costanzo, C. (1995): Getting Serious About Customer Profitability, in: United States Banker, Vol. 105, No. 5, pp. 79–80.

Gloy, B./ Akridge, A./ Preckel, P.V. (1997): Customer Lifetime Value: An Application in the Rural Petroleum Market, in: Agribusiness, Vol. 13, No. 3, pp. 335–347.

Article Google Scholar

Henry, J. (1994): It Pays to Rate Retention, in: Advertising Age, Vol. 65, No. 13, p. S32.

Horngren, C.T./Foster, G./Datar, S.M. (1997): Cost Accounting: A Managerial Emphasis, 9th ed., New Jersey.

Jackson, D. (1989): Insurance Marketing: Determining A Customer’s Lifetime Value (Part 1), in: Direct Marketing, Vol. 51, No. 11, pp. 60–62, 123.

Jackson, D. (1989): Insurance Marketing: Determining A Customer’s Lifetime Value (Part 2), in: Direct Marketing, Vol. 51, No. 13, pp. 25–32.

Jackson, D. (1989): Insurance Marketing: Determining A Customer’s Lifetime Value (Part 3), in: Direct Marketing, Vol. 51, No. 15, pp. 28–30.

Keane, T.J./ Wang, P. (1995): Applications for the Lifetime Value Model in Modern Newspaper Publishing, in: Journal of Direct Marketing, Vol. 9, No. 7, pp. 59–66.

Kidder, L./Judd, C. (1986): Research Methods in Social Relations, 5th ed., New York.

Pearson, S. (1996): Building Brands Directly: Creating Business Value from Customer Relationships, New York.

Peppers, D./Rogers, M. (1997): The One to One Future: Building Relationships one Customer at a Time, New York.

Pritchard, H.O., Jr. (1991): A Member’s Lifetime Value: Seeing Member Acquisition as an Investment in Your Organization’s Future, in: Association Management, Vol. 36, No. 6, pp. 35–39.

Reichheld, F.F. (1996): The Loyalty Effect: The Hidden Force Behind Growth, Profits and Lasting Value, Boston.

Reichheld, F.F. (1993): Loyalty-Based Management, in: Harvard Business Review, Vol. 71, No. 2, pp. 64–73.

Reichheld, F.F./ Sasser, W.E., Jr. (1990): Zero Defections: Quality Comes to Services, in: Harvard Business Review, Vol. 68, No. 5, pp. 105–111.

Smith, M./ Dikolli, J. (1995): Customer Profitability Analysis: An Activity-Based Costing Approach, in: Managerial Auditing Journal, Vol. 10, No. 7, pp. 3–7.

Stauss, B. (1997): Regaining Service Customers-Costs and Benefits of Regain Management, paper from the EIASM workshop (Quality Management Services VII), Agder College Kristiansand, Norway.

Tauhert, C. (1996): First United Raises Customer Profitability with Database System, in: Bank Systems & Technology, Vol. 33, No. 5, p. 67.

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Andon, P., Baxter, J., Bradley, G. (2003). Calculating Customer Lifetime Value (CLV): Theory and Practice. In: Günter, B., Helm, S. (eds) Kundenwert. Gabler Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-99328-1_12

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