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New Study Demonstrates Efficacy of CLV Measure

Everyone's looking for the optimal return on marketing investment, and it's about time. For decades now, many of us have instituted innovative practices to encourage customer "loyalty." Whether pioneers of loyalty marketing or the copycats who followed, most of us have measured the results of our efforts with share-of-wallet estimations or RFM — recency, frequency, monetary value — tracking. We've put millions into our programs only to assess their value with inadequate metrics.

In a study published by Marketing Science Institute, Rajkumar Venkatesan, assistant professor of marketing at the University of Connecticut's School of Business, and V. Kumar, the ING chair professor of marketing and executive director of the ING Center for Financial Services at the same, use data from a large multinational computer hardware and software company to apply customer lifetime value (CLV) metrics and compare them to the commonly used share-of-wallet and RFM gauges.

Smart marketers continue to reach toward better practices, recognizing that benchmarks dubbed "best practices" rarely are. And employing share of wallet and RFM has not ensured marketing spending against the right customers in the right channels. So Venkatesan and Kumar divided their research into two phases: optimal selection of customers and optimal design of marketing communication strategy.

In the first phase, they rank-ordered customers from best to worst according to CLV, share of wallet, and RFM designs, comparing the sales, costs, and profits from the top customers of each.

Turns out CLV was better at identifying profitable customers than either share of wallet or RFM. The net profits from the top 5% of CLV-ranked customers were 1.8 times the net profits from those in the share-of-wallet ranking and 1.6 times the net profits of the top 5% of RFMers. That difference in dollar figures, averaged between share of wallet and RFM, was $60,000.

In the second phase, the pair found that using CLV to target customers for resource allocation increased profits by 67%, from $28 million to $47 million. The cost to serve these customers increased through the optimization strategy, but only from $233,000 to $314,000.

Venkatesan and Kumar aren't the first to recognize the strength of CLV. The moves of airlines to reward frequent flyers based on the value of the fares they purchase rather than the miles they fly reflects a focus on CLV that can be improved by increasing the revenues from individual customers.

Dell and AOL have put CLV at their cores, too, announcing profit growth strategies that depend on optimal allocation of marketing dollars to increase the revenue from best customers and eventually decrease the cost to market to them.

This study's optimization strategy suggests removing resources from customers who are low on the backward-looking metrics — RFM and share of wallet — and shoving resources towards customers high on both backward-looking and forward-looking (CLV) metrics. In the middle ground, they recommend allocating more resources to customers high on CLV but low on the other measures rather than those low on CLV but high on the other two measures.

If you are ready to join this movement, read "Using Customer Lifetime Value in Customer Selection and Resource Allocation" by Rajkumar Venkatesan and V. Kumar, part of the Marketing Science Institute's 2003 Working Paper Series, at www.msi.org.