Last week, one of my clients asked me a question. “Should I measure lifetime customer value of each of my customers and then evaluate the stores based on their performance?”
I paused before answering. In fact, Customer Lifetime Value (CLV) can be very useful in certain situations, but I have found that in many organizations the measurement of CLV becomes so complex that it is rarely successful. The answer always has to do with the objectives of the analysis, and what you intend to do with the results.
To review, Customer Lifetime Value is a marketing measurement that combines (1) anticipated length of relationship with (2) anticipated customer financial value, to create a predicted measure of how profitable that customer will be. For example, if a customer was forecasted to have a length of relationship of five years with an average spending of $1,000 per year, then their total value would be $5,000. If you add up the calculation for all customers in the customer base, you would then be able to value the business as a whole, since the value of a business is really just the anticipated future profit from their customer base. Such a metric can be used by Wall Street, investors and senior management for their incentives.
Given how far-reaching the impact of Customer Lifetime Value could be, it is not surprising to discover the calculation frequently debated.
- Should an average length of relationship be used, or should the company spend the time (and money) to do predictive modeling on each individual customer?
- Should you use a customer’s past performance, or use predictive modeling to forecast future revenue and margin instead?
- Do you use gross profit or net profit and if it’s net profit, what are the allocations of corporate expenses? And so on…
You can imagine how political the discussion gets. Rarely does a company come to a solution that makes everybody happy. As a result, when Customer Lifetime Value is expected to be widely used, what you find is that it is rarely implemented at all. There are just too many vested interests involved.
The one place that I have seen CLV used successfully is in Marketing. If you can keep the metric isolated within the marketing department, you actually have a chance of being able to pull it off.
CLV is frequently used differently in Marketing. For example, the total amount of Customer Lifetime Value isn’t very important. What is important is how CLV changes after marketing is applied. You see, what Marketing wants is increase the depth of customer relationship (increase in number of products purchased and length of relationship). If everything works well, CLV should increase over time.
Since Customer Lifetime Value is composed of retention and customer value, what you will find is that CLV is highly sensitive to improvements in purchase frequency. In other words, if you are successful at driving a customer to the store to make incremental purchases, you will improve that individual customer’s CLV. The change in CLV from before the marketing effort to afterwards is the true value of that marketing effort.
Using CLV in Marketing is the only place I have seen the metric work. Even then, it requires training of marketing management before it is accepted. CLV is more complex than traditional marketing metrics. As a result, acceptance requires both time and additional explanation before you get buy-in.
So what did I tell my client? Well, first I asked him what he wanted to accomplish. He said that he wanted to measure change in retention due to operational improvement. For his particular industry, a subscription-based organization, monthly customer revenue does not vary significantly. As a result, retention rate is really the only metric that influences long-term value of customers. So, as you might imagine, I suggested that he use retention as his primary metric.
By doing some simple calculations of monthly revenue times retention, it was possible to create a rough value of the customer base and show the differences. Net of everything, he got all of the value that he sought out of Customer Lifetime Value without 1) dealing with territorial and political issues around allocating corporate costs; or 2) creating a metric so complex that no one in the organization could understand it.
Customer Lifetime Value is a classic example of a theme that I have been focusing on recently. The value of a metric is driven by how understandable that metric is, and what you can do with the results of the analysis. If it’s not understood and you can’t figure out what the action is, I recommend that you not do it. For all organizations except the most sophisticated, I am afraid that Customer Lifetime Value falls in that category — for now. That may change in the future, as predictive modeling and other statistical techniques become more widely accepted, but for now basic metrics achieve over 80 percent of the value with less than 20 percent of the work.
What have been your experiences with Customer Lifetime Value? Have you found organizations where it has been successfully used? Please let me know.