Edited by V. Kumar and Denish Shah
Chapter 4: Incorporating dynamics in customer lifetime value models
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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