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Journal of the Academy of Marketing Science

Erschienen in:

01.05.2013 | Original Empirical Research

Overcoming the “recency trap” in customer relationship management

verfasst von: Scott A. Neslin, Gail Ayala Taylor, Kimberly D. Grantham, Kimberly R. McNeil

Erschienen in: Journal of the Academy of Marketing Science | Ausgabe 3/2013

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Abstract

Purchase likelihood typically declines as the length of time since the customer’s previous purchase (“recency”) increases. As a result, firms face a “recency trap,” whereby recency increases for customers who do not purchase in a given period, making it even less likely they will purchase in the next period. Eventually the customer is effectively lost to the firm. We develop and illustrate a modeling approach to target a firm’s marketing efforts, keeping in mind the customer’s recency state. This requires an empirical model that predicts purchase likelihood as a function of recency and marketing, and a dynamic optimization that prescribes the most profitable way to target customers. In our application we find that customers’ purchase likelihoods as well as response to marketing depend on recency. These results are used to show that the targeting of email and direct mail should depend on the customer’s recency and that the optimal decision policy enables the average high recency customer, who currently is virtually worthless to the firm, to become profitable.

Vorheriger Artikel Competence resource specialization, causal ambiguity, and the creation and decay of competitiveness: the role of marketing strategy in new product performance and shareholder value

Nächster Artikel Customer positivity and participation in services: an empirical test in a health care context

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Note that given the definition of recency as time since previous purchase, “higher recency” or “increased recency” means a longer time period has elapsed since the customer last purchased.

A customer’s recency state is assigned at the end of the period. As in all CLV models, since we start the calculation from the time the customer makes her or his first purchase, we know the customer is in recency state 1 at the end of period 1. So the probabilities in Table 1 start off with a probability of 1 (the customer buys in period 1) by definition. The customer then has a 0.23 probability of purchase in period 2, because ProbPurchase(1) = 0.23. The subsequent purchase probabilities, and hence the states, are determined by the migration probabilities at the top of Table 1.

Note that if as in Table 1, the highest recency state is ≥20, once ≥20 becomes that customer’s current state and he or she does not buy, he or she “migrates” to state ≥20, since we are collapsing states 20, 21, 22, etc. into one state, ≥20. That is, S+1 is state ≥20 if the current state is ≥20.

Note the exception that since we have 20 recency states, if the customer is in recency state ≥ 20 and doesn’t purchase, he or she remains in state ≥20.

In fact, discussion with the firm’s management suggested that the company was not currently targeting email or direct mail efforts in any way, i.e., they were not using previous purchase, etc., to target marketing. If they had been, this would have created an endogeneity that we would have had to handle in our estimation of the logistic customer response function (see Rhee and McIntyre 2008).

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Titel: Overcoming the “recency trap” in customer relationship management
verfasst von: Scott A. Neslin
Gail Ayala Taylor
Kimberly D. Grantham
Kimberly R. McNeil
Publikationsdatum: 01.05.2013
Verlag: Springer US
Erschienen in: Journal of the Academy of Marketing Science / Ausgabe 3/2013
Print ISSN: 0092-0703
Elektronische ISSN: 1552-7824
DOI: https://doi.org/10.1007/s11747-012-0312-7

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