Customer-Base Analysis in a Discrete-Time Noncontractual Setting

Abstract

Many businesses track repeat transactions on a discrete-time basis. These include: (1) companies where transactions can only occur at fixed regular intervals, (2) firms that frequently associate transactions with specific events (e.g., a charity that records whether or not supporters respond to a particular appeal), and (3) organizations that simply use 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 dataset on donations made by the supporters of a public radio station 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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 157 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.