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Handbook of Research on Customer Equity in Marketing

Handbook of Research on Customer Equity in Marketing

Research Handbooks in Business and Management series

Edited by V. Kumar and Denish Shah

Customer equity has emerged as the most important metric to manage firm performance. This Handbook covers a broad range of strategic and tactical issues related to defining, measuring, managing, and implementing the customer equity metric for maximizing firm performance. Drawing upon the wisdom of a global pool of leading scholars, the book serves as a comprehensive and authoritative guide on customer lifetime value and customer equity for marketing researchers, practitioners, and students worldwide.

Chapter 4: Incorporating dynamics in customer lifetime value models

Michael Lewis

Subjects: business and management, marketing


Over the past two decades the marketing literature has increasingly adopted the perspective that customer relationships should be viewed and managed as economically valuable assets (Blattberg and Deighton, 1996; Gupta, Lehmann, and Stuart, 2004; Rust, Lemon, and Zeithaml, 2004; Lewis, 2005a). In particular, customer equity (CE) and customer lifetime value (CLV) have been advanced as key CRM metrics related to the long-term value of customers. However, the most common CLV formulas contain significant limitations. For instance, many CLV models include assumptions of constant retention and revenue rates. Given that these inputs (retention and expected revenue) are likely to change based on consumer experiences and the firm’s marketing tactics, the lack of explicit consumer dynamics in standard CLV models is likely to result in biased predictions. CLV projections based on these traditional models also limit the usefulness of the metric. If the goal is segmentation of customers based on expected CLV, then exclusion of marketing mix variables may be acceptable. In contrast, if CLV is a viewed as a goal to be maximized by the selection of marketing tactics then CLV calculations need to be executed within a dynamic optimization model rather than via the standard net-present-value–based formulas.

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