Abstract
Customer lifetime value (LTV) models have long been used to focus direct marketing campaigns by allocating resources to those customers who are deemed to offer the most value to the company. Traditionally, customers are rank-ordered by their lifetime value and more marketing resources are targeted towards the customers with the greatest value. In this paper, a framework for visualising customer segments, called Z-ranking, is developed, along with a method for customer portfolio management, called the Life Time Value Perturbation Technique. Rather than viewing a customer's LTV as a quantity that is immutable, this framework adopts the point of view that the LTV can be perturbed, via application of appropriate customer management strategies. Through a series of ‘What-if’ scenario analyses, a firm can focus scarce resources on those investments that lead to the greatest increase in the value provided by a customer portfolio. The LTV perturbation technique is further embedded in a four-phase procedure that forms the basis for customer portfolio management, enabling the strategic marketer to continually identify and improve customer management strategies that lead to the greatest impact on direct marketing performance.Journal of Database Marketing & Customer Strategy Management (2007) 14, 225–235. doi:10.1057/palgrave.dbm.3250050 ABSTRACT FROM AUTHOR Copyright of Journal of Database Marketing & Customer Strategy Management is the property of Palgrave Macmillan Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts)
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CITATION STYLE
Gopalan, R. (2007). Customer portfolio management using Z-ranking of customer segments and the LTV perturbation method. Journal of Database Marketing & Customer Strategy Management, 14(3), 225–235. https://doi.org/10.1057/palgrave.dbm.3250050
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