Identify Value Segments

Based on Augmented Customer Value

We know that optimal allocation of resources is the most effective means by which to manage a customer portfolio, such that maximum value potential is realized. In order to identify the most profitable customer segments, a decile analysis must be conducted that factors in both baseline CLV (net profits) and augmented CLV (net profits in addition to value added by the ‘Wheel of Fortune’ strategies). How, then, are resources to be optimally allocated? Firms must identify not only the most profitable customers, but also those customers most receptive to marketing efforts. Furthermore, the right communication channels must also be determined. It is here that a customer contact strategy is developed. Consequently, resources can be effectively leveraged toward the implementation of this strategy.

A good way to understand the impact of optimal allocation of resources is through the firm’s usage of marketing communication channels (Venkatesan, Rajkumar, V. Kumar, and Timothy Bohling (2007)). In order to optimally allocate their resources, firms must first identify their most profitable customers and those who are the most responsive to marketing efforts. Performed at the individual customer level, the selection of the best mix of communication channels is determined based on the responsiveness of each customer. The cost-effectiveness of these channels is also considered to measure the customers’ potential revenue contribution based on the contacts made. The frequency of contacts through these channels, and at what interval, is then decided by the firm in order to develop a customer contact strategy. Prior to developing the contact strategy, firms needed to analyze the various factors that affect customer behavior, such as upgrading to a higher product category and cross-buying in a different category. Analyzing customer behavior in relation to these factors provides firms with valuable information about the preferences and attitudes of its customers. By carefully monitoring the purchase frequency of customers, the time elapsed between purchases, and the contribution margin, managers can determine the frequency of firm initiatives to maximize CLV through an optimal contact strategy.

Even in a complex business setting, the benefits of optimal resource allocation using CLV as the metric can be observed. When IBM chose to harness the advantages of the CLV metric in order to measure customer profitability and decide on the allocation of resources, they used CLV to determine the level of contact and outreach efforts through telesales, e-mail, direct mail, and catalogs on an individual customer basis. At the conclusion of this program (based on about 35,000 customers), IBM was able to effectively re-allocate resources for about 14 percent of customers as well as increase revenues by about USD 20 million. All of this was done without increasing the investment, and was accomplished through abandoning ineffective techniques based on the spending history in favor of the CLV metric (Kumar et al. 2008).

Venkatesan, Rajkumar, V. Kumar, and Timothy Bohling (2007), “Optimal customer relationship management using Bayesian decision theory: An application for customer selection,” Journal of Marketing Research, 44 (4), 579-94.
Kumar, V., Rajkumar Venkatesan, Tim Bohling, and Denise Beckmann (2008), “The Power of CLV: Managing Customer Lifetime Value at IBM,” Marketing Science, 27 (4), 585-99.