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Including the effects of prior and recent contact effort in a customer scoring model for database marketing

Including the effects of prior and recent contact effort in a customer scoring model for database... Database marketers often use a scoring model to predict the likely value of contacting customers based on their purchase histories and demographics. However, when purchase history has been a partial result of the firm’s own contacting efforts, these contacts should also be accounted for in the scoring model. The current work extends the existing literature to account for the firm’s contacts by focusing on each customer’s most recent purchase. Contacts prior to that purchase are designated “prior contacts” and those after that purchase “recent contacts.” A new latent variables formulation of the customer’s propensity to respond is used to predict the likelihood and time of response as well as the relationship to the independent variables. The methodology also addresses the statistical problems of “selection bias” and “endogeneity,” which have been largely ignored in most customer scoring models. An application to the database of a charitable organization confirms that, in this case: (1) the effect of the firm’s customer contact efforts is associated with a stronger propensity to respond than is the case for the included demographics; (2) the firm’s “recent contact” efforts are associated with larger returns in customers’ propensity to respond than the “prior contact” efforts; and (3) the “recent contact” efforts are associated with an at-first increasing but then diminishing propensity to respond up to a point beyond which actual decreasing returns are observed with further contacts. Clearly, too much contacting can alienate would-be donors. The proposed model is general enough to calibrate such impacts in other database marketing applications where the relative effects might be different. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Academy of Marketing Science Springer Journals

Including the effects of prior and recent contact effort in a customer scoring model for database marketing

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Academy of Marketing Science
Subject
Business and Management; Business and Management, general; Marketing; Social Sciences, general
ISSN
0092-0703
eISSN
1552-7824
DOI
10.1007/s11747-008-0086-0
Publisher site
See Article on Publisher Site

Abstract

Database marketers often use a scoring model to predict the likely value of contacting customers based on their purchase histories and demographics. However, when purchase history has been a partial result of the firm’s own contacting efforts, these contacts should also be accounted for in the scoring model. The current work extends the existing literature to account for the firm’s contacts by focusing on each customer’s most recent purchase. Contacts prior to that purchase are designated “prior contacts” and those after that purchase “recent contacts.” A new latent variables formulation of the customer’s propensity to respond is used to predict the likelihood and time of response as well as the relationship to the independent variables. The methodology also addresses the statistical problems of “selection bias” and “endogeneity,” which have been largely ignored in most customer scoring models. An application to the database of a charitable organization confirms that, in this case: (1) the effect of the firm’s customer contact efforts is associated with a stronger propensity to respond than is the case for the included demographics; (2) the firm’s “recent contact” efforts are associated with larger returns in customers’ propensity to respond than the “prior contact” efforts; and (3) the “recent contact” efforts are associated with an at-first increasing but then diminishing propensity to respond up to a point beyond which actual decreasing returns are observed with further contacts. Clearly, too much contacting can alienate would-be donors. The proposed model is general enough to calibrate such impacts in other database marketing applications where the relative effects might be different.

Journal

Journal of the Academy of Marketing Science – Springer Journals

Published: Apr 8, 2008

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