A Primer: Here’s How To Calculate Customer Lifetime Value


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Economic debate used to rage around the question, What is the ideal rate of savings? Economics Nobel Prize winner Franco Modigliani offered a key insight: It depends on the stage of the person’s lifetime.

A child’s savings rate is negative, the same as when that same person retires. During the income-earning years, the savings rate is positive, increasing to a peak before declining.

Physical product costs are becoming a smaller part of the value of the overall purchase that a customer makes. The cost of providing services is an increasingly larger part of the overall cost. Generally, the quantity of service required will change at different stages of the lifecycle of the relationship between a customer and a business.

How are businesses going to accurately predict their cash flow? Profitability? How will decisions be made? Customer lifetime value (CLV), a concept devised by Northwestern University Professor Paul Wang, takes into account the changing dynamics of a customer relationship. It provides insight through objectively valuing the customer relationship based on net cash flow. To provide a comparable value in “today’s dollars,” net cash flow from future periods is discounted at the cost of capital to the business.

Activity-based costing/management

Historically, average cost or standard cost has routinely been calculated for the components of products. Where activities now make up the bulk of the cost, it is necessary to determine standard costs for repeated activities.

The accounting profession developed activity-based costing/management or ABC/M to address this need. ABC/M takes historical data from the standard accounting general ledger and uses it to calculate the average cost of servicing activities. Using these average service costs, business managers can project in a systematic way the costs at different levels of service activity.

ABC/M is the critical component of the cost side of calculating CLV. Similar techniques to those used on the cost side are applied to activities to generate revenue, with one important difference.

When looking at activity-based revenue projections, the revenue is not 100 percent guaranteed. When several activities are undertaken to interest prospective customers in a product or service, it is not guaranteed that the customer will actually make a purchase. CLV requires the introduction of “expected revenue.” The formula for expected revenue is:

projected revenue x probability of winning the revenue

You must make an estimate of the probability of winning the revenue. The estimate must be tested and reviewed in order to improve the accuracy. You need to learn about activities systematically, and their outcomes must occur to accelerate improvement of the revenue forecast and its probability.
When working at Intel, my job was to forecast the next half year’s monthly demand for specific products.

At first, I didn’t know where to start, so I did an initial forecast using the average of the last three months’ sales—sometimes called the three-month rolling average. The managers in my department asked me questions about what major customers were planning and told me about inventory shortages and over-stocks. I revised the forecast to take into account this information. After 36 months of forecasting, I had become so knowledgeable about the customers, products and seasonal variations in demand that my judgment on what to forecast improved, improving the forecast accuracy over time.

Calculating customer lifetime value

Can customer retention be converted to financial value?

net present value (NPV) = valuing cash flow over time in today’s dollars
expected value = probability of event x outcome of event

By measuring the expected financial benefits from retention and referrals, you can make a sustained investment in customer care, moving beyond lip service. By quantifying the expected results, you obtain metrics you can use to gauge the impact of your customer care programs and actions.

Here’s an example. Let’s say there’s a company called ACME that does business with the federal government. Below is a summary of ACME’s business.

ACME’s Federal Government Business
Average sale: $2,000 per customer per year
Product margin: 50% of revenue (after selling costs)
Customer retention rate: 40% in the year after initial purchase

The next two examples will demonstrate the calculation of expected value and net present value for this segment of federal government customers. Then we will look at how CLV can be used to create different activity paths for different segments of customers.

Calculate expected revenue

Let’s look at the two-year history of revenue from customers. In the second year, 40 percent of customers come back and make a purchase; 60 percent of new customers never return. On average, how much revenue do you get from 1,000 new customers over two years?

ACME’s expected revenue
  Year 1 Year 2
New customers 1,000
Expected number of customers
= 1000 x retention rate of 40%
Average size of sale for each customer $ 2,000 $ 2,000
Revenue $2,000,000 $ 800,000
Expected revenue from 1,000 new customers (cumulative) $2,800,000
Average revenue from each of the 1,000 initial customers = Expected 2-year revenue divided by 1,000 = $2.8M / 1,000 $ 2,800

With a 60-percent retention rate, the two-year revenue from each new customer would be $3,200.

Let’s calculate the profitability to ACME of their customers, assuming that their profit-margin is 50 percent of revenue. Of 100 new customers in Year 1, only 40 percent come back in Year 2 to make a purchase.

Increasing customer retention

The customer value model can be used to value customer retention. Customer lifetime value is the profit you earn from a customer over the customer’s lifetime. It is calculated as the net present value of the expected value of the profits you earn on sales to that customer in each of the years the customer remains a purchaser.

In our example, each customer provides ACME with $1,000 in profit each year that he or she remains a customer. The net present value takes into account the “cost of capital” to discount future years’ profits and restate them in today’s dollars. In this example, the cost of capital is 25 percent.

ACME’s expected profit
  Year 1 Year 2
New customers 1,000
Expected number of customers
= 1000 x retention rate of 40%
Average profit for each customer $ 1,000 $ 1,000
Total profit $1,000,000 $ 400,000
Discount rate 25% factor 100% 80%
NPV profit $1,000,000 $ 320,000
Expected profit from 1,000 new customers (cumulative) $1,320,000
Average profit from each of the 1,000 initial customers = expected profit by Year 2 divided by 1,000 = $1.32M / 1,000 $ 1,320

If the retention rate is 60 percent, the expected profit per new customer would be $1,480.

One of the great advantages of customer lifetime value is the ability to incorporate probability in the future years. Unlike cost analysis, which is determinate—in that if you do an activity, you will incur a cost—revenue analysis is probabilistic. You can do an activity, but you cannot guarantee that the revenue will eventuate. You can increase the chances of a customer purchasing, but there are factors outside of control of the selling entity that can affect whether or not the purchase will occur. With CLV, you can take these into account.

Final note: CLV indices

A valuable use of CLV is to differentiate customer segments with high CLV from those with low values. The relative CLV for different customer segments may be calculated based on different costs of acquisition and retention. These measures are a CLV index that can range from zero for the customer segments of the lowest asset value to one for the customer segments with the highest asset value. Companies ranging from financial institutions, like banks and insurance companies, to retail and technology companies use CLV indices to distinguish between segments of customers, empowering front-line sales to provide offers appropriate to customers of differing CLV, to maximize potential value of their portfolio of customer assets.

A note of caution: All calculation of customer lifetime value requires a working model or a hypothesis of what the customer relationship lifecycle looks like. This can be tested in a predictive model, with the goal of revising the model regularly to improve predictions over time. Be wary of CLV indices based on analysis done once some time ago, with relationship lifecycles not explicit. Circumstances change, and relationship lifecycle activities and responses must change with them. Customer Indices must be used within a culture of customer appreciation. As I note in my article, Use, But Don’t Misuse, Customer Lifetime Value, there is no substitute for a real understanding of customer needs and behavior. The numbers should be used to help expand on that understanding.


  1. Hi Mei,
    Found the article very interesting. I really understand that the results of CLV should be used with cautions and the methodology for calculaing CLV changes with industry. Few questions I would like to ask:
    1. How we can create the segments without use of predictive tool and segments should be based on which measures.
    2. Can we use this model in scenario where customer life cycle is not known or in never ending.


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