Roy Cardiff runs a mail-order business that tracks sales to each customer. He recently decided to cut costs by curtailing catalogs to those customers who are least likely to buy from him in the future.
His customers break down into three categories: those who made several small purchases throughout the past year; those who made a single purchase but for a much larger amount, and those who have had a long but sporadic relationship with his firm.
Which segment of customers should Cardiff prune from his mailing list?
According to several marketing experts who have studied this issue, there is no easy answer, despite new and increasingly sophisticated efforts to measure what is called “Customer Lifetime Value” (CLV) – the present value of the likely future income stream generated by an individual purchaser.
“For many companies, their whole business revolves around trying to understand which customers are worth keeping and which aren’t,” says Wharton marketing professor Peter Fader. “This has led managers from a broad cross section of industries to seek out more refined measures of CLV, using data-intensive procedures to identify top customers in terms of their likely future purchasing patterns.”
The goal is not only to identify customers, but to reach out to them through cross-selling, up-selling, multi-channel marketing, and other tactics – all of which are tied to metrics on attrition, retention, churn, and a set of statistics known as RFM – recency, frequency, and monetary value.
“CLV is a hot area,” notes Wharton marketing professor Xavier Dreze. Although CLV is by no means new, the concept has been energized by the increasing sophistication of the Internet “which allows companies to contact people directly and inexpensively.” CLV, Dreze says, “sees customers as a resource from whom companies are trying to extract as much value as possible.”
Rolling the dice. Yet many companies are discovering that CLV – which is one component of Customer Relationship Management (CRM) – remains an elusive metric. First, it is hard to calculate with any degree of certainty; second, it is hard to use.
“The only number a manager can have much confidence in is a customer’s current profitability,” says Wharton marketing professor George Day. “And the basic question becomes, now that you have that data, what are you going to do with it? Some companies use this information to create different programs for different value segments. In the financial services industry, for example, customers get different levels of service depending on how big an account they are. But there is always the risk that by doing this you anger other customers.”
In addition, it’s hard to predict how long a customer will stay with the company or how ‘growable’ he is. “In the last analysis,” Day says, “companies don’t really know how profitable customers are.”
CLV is an intuitively appealing concept, but one that for a variety of reasons can be very hard to implement. CLV works best in industries where there is a high cost of acquiring or retaining customers, such as in financial services, airlines and hotels. “It’s also useful in situations where you have a skewed distribution of transactions – that is, where a small number of people drive most of the business, as in hotels – and where firms can offer rewards and inducements to affect customer behavior,” notes Wharton marketing professor David Bell. An example would be airlines companies that can upgrade passengers to first class – a benefit that is considered big to the passenger but whose cost to the company is small.
Collecting data on CLV can offer particular companies a number of benefits, Bell adds. For example, the individual transaction data collected by a hotel helps the company identify its best customers and cross-sell them other products. It also allows company marketers to target that group for customer feedback. Using that feedback, the company can then make smarter decisions about where to most efficiently allocate its marketing resources. Suppose the data shows that a significant percentage of the customers come from the biggest city and are in their 50s; the hotel can use that profile for more accurate outreach, he notes.
Bell points to Harrah’s Casino as a CLV success story. Based on information gleaned from its loyalty program, Harrah’s can now figure out “who is coming into the casino, where they are going once they are inside, how long they sit at different gambling tables and so forth. This allows them to optimize the range, and configuration, of their gambling games.”
Others cite the health care and credit card industries, direct marketers and online e-mail marketers as potential benefactors of CLV data, in part because they are characterized by direct customer contact and easy tracking abilities. For instance, sales forces within the pharmaceutical industry, Dreze points out, can use relevant data to decide how often they should visit doctors’ offices to pitch their companies’ drugs.
Basically, says Day, CLV is most applicable “any time you have a database with customer profile and transaction information. But if you are working through channels – using a value-added retailer, for example, or any similar situation where you don’t have a direct relationship with the customer – then it is not as easy to implement.”