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.