Customer Life Time Value (CLTV) is the estimated future profit that will be earned from a customer. Its two primary conceptual appeals are that it shows how much extra you can spend ("invest") in a customer before you start losing money on that customer; and it shows the wide disparity in profitability in customers over their shopping lifetime which encourages management to invest extra in building and retaining those customers projected to create the most profits.
Where CLTV Works Well
The best practical use of CLTV is seen where annual transactions are few and relatively standard. Magazine subscriptions, donations for charities and causes, insurance, and telecoms are good examples. Consider the first mentioned: a magazine publisher knows his publication, distribution, and associated costs. He has three key variables to play with: the cost and frequency of soliciting subscriptions, the range of prices (cover price and its discount variations), and the various subscription periods offered. From past experience, he knows the average "customer subscription life" and how it varies by subscription source and other factors. From this simplified set of data, he can calculate the CLTV in a variety of combinations and make offers accordingly to optimize his CLTV. He may have found, for example, that readers who also read the Economist have a long subscription life with his magazine so he may position better introductory offers in that magazine.
Where CLTV Doesn't Work So Well
The answer is opposite to that above, ie, where annual transactions are many, involve many different items, and customer frequency and spending is volatile throughout the year. Food retailers are a great example. Consider a typical US food retailer with 10,000-20,000 unique customers per store, offering a range of 30,000-100,000 items. Imagine calculating the LTV for each customer and then making offers ("investments") to optimize their LTV figure. Making such a comprehensive CLTV proposal to a retail CEO would probably trigger a fairly quick exit from his or her office.
On The Other Hand...
If we consider the intent of CLTV, then optimizing customer profitability by focusing on the retention and addition of profitable customers would be in harmony with the concept but without requiring CLTV's theoretical accuracy and huge data processing requirements. Here's one approach to accomplish that objective.
Consider this store profile, drawn from a composite of food retailers' quarterly data.
$50 - $100
$25 - $50
Among the existing customer categories, the Gross Profit% and Operating Costs% do differ, but not dramatically, as customers buy from the same range of merchandise with the same prices and are processed through the same checkouts.
Looking at the table above, the question is how to best optimize overall customer (ie, company) profitability? How should resources among the customer segments be reallocated to increase profits?
Before proceeding on this quest, understand that attrition rates are the percentages of customers in each segment this quarter last year that didn't buy anything in the same quarter this year. Retained customers are, by definition, still shopping with us but are not necessarily in the same spending segment as last year: they may have increased or decreased their spending. (Such customer movement management sees in a separate report.)
Obviously, it makes intuitive sense to increase the number of Diamonds and Rubies. We want to hold them at their existing spending levels, and higher. But a number will have dropped their average weekly spending for reasons that may be internal (eg, poor quality or service) or external (family changes, new competitors). This is where grounded testing steps in. For example:
The process is a matter of trial and success as the lessons are absorbed and applied. For food retailers, it is less theoretical than what the proponents of CLTV suggest but it is pragmatic, practical ... and productive.
The Bottom Line
Sensible approximation is sometimes preferred to exquisite exactitude, even when the concept is a fabulous one.
The techniques and metrics Brian Woolf has developed have become guiding principles for those operating some of the world's most successful programs. He is the President of the Retail Strategy Center, and has consulted, and spoken at conferences, in the US, Europe, Japan, and Australasia.
Prior to his total commitment to loyalty marketing, his corporate roles included Deputy Managing Director of Progressive Enterprises, a major New Zealand retailer; and Chief Financial Officer of Food Lion, a leading US food retailer. He has an M.Com. (Economics) from the University of Auckland, New Zealand, and an MBA from the Harvard Business School.