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By Dominique M. Hanssens, Julian Villanueva

Purchaser fairness may also help administration: ·allocate advertising spending for long term profitability, ·understand the relationship among budgets, metrics and fiscal functionality, ·provide a shopper targeted strategy for measuring company worth, ·improve the productiveness of CRM systems by means of supplying frameworks, instruments and metrics consumer fairness stories present types, deals a typology, and examines the elemental query of no matter if a purchaser fairness orientation can positioned an organization in a aggressive virtue to different companies. The authors overview versions which may bring up client fairness through optimizing every one of its drivers - client acquisition, shopper retention, and add-on promoting. buyer fairness is critical analyzing for advertising and marketing managers, advertising researchers, students and scholars.

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Additional info for Customer Equity: Measurement, Management and Research Opportunities (Foundations and Trends in Marketing)

Example text

2 Normative models of customer acquisition Managers need models that help them find the optimal acquisition spending, and the optimal allocation among acquisition channels or among individual customers. Firms use a combination of acquisition channels without a clear understanding of the long-run consequences of their allocations. , Advertising or TV advertising). Finally, in the field of direct marketing, more research is needed on how to target individual customers in order to maximize CE, not simply immediate marketshare, as most current models do.

The margin multiple is high when the discount rate is low or the retention rate is high. This multiple provides an easy way to estimate the lifetime value of an average customer if the margin is known and the assumptions of the model hold. Despite the necessary assumptions that models using these data should have, they are very interesting because of their likelihood to impact the business community. 4 Panel data The use of panel data in CE models offers a great challenge for researchers and opportunities for future work.

Most of the previous literature on customer retention has been focused on understanding the determinants of customer retention, and the consequences of higher retention rates. We first review current literature on the determinants and consequences of customer retention, to later review modeling efforts in this area. We conclude with recommendations for future research. Specifically, we want to address the following questions: • What are the determinants of customer retention? • What is the relationship between customer retention and profitability?

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