Beteiligte: | , , |
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In: | Marketing Science, 29, 2010, 6, S. 1086-1108 |
veröffentlicht: |
Institute for Operations Research and the Management Sciences (INFORMS)
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Medientyp: | Artikel, E-Artikel |
Umfang: | 1086-1108 |
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ISSN: |
0732-2399
1526-548X |
veröffentlicht in: | Marketing Science |
Sprache: | Englisch |
Kollektion: | sid-55-col-jstoras4 sid-55-col-jstorbusiness1archive sid-55-col-jstorbusiness JSTOR Arts & Sciences IV Archive JSTOR Business I Archive JSTOR Business & Economics |
<p>Many businesses track repeat transactions on a discrete-time basis. These include (1) companies for whom transactions can only occur at fixed regular intervals, (2) firms that frequently associate transactions with specific events (e.g., a charity that records whether supporters respond to a particular appeal), and (3) organizations that choose to utilize discrete reporting periods even though the transactions can occur at any time. Furthermore, many of these businesses operate in a noncontractual setting, so they have a difficult time differentiating between those customers who have ended their relationship with the firm versus those who are in the midst of a long hiatus between transactions. We develop a model to predict future purchasing patterns for a customer base that can be described by these structural characteristics. Our beta-geometric/beta-Bernoulli (BG/BB) model captures both of the underlying behavioral processes (i.e., customers' purchasing while "alive" and time until each customer permanently "dies"). The model is easy to implement in a standard spreadsheet environment and yields relatively simple closed-form expressions for the expected number of future transactions conditional on past observed behavior (and other quantities of managerial interest). We apply this discrete-time analog of the well-known Pareto/NBD model to a data set on donations made by the supporters of a nonprofit organization located in the midwestern United States. Our analysis demonstrates the excellent ability of the BG/BB model to describe and predict the future behavior of a customer base.</p> |