Marketing Papers

Document Type

Technical Report

Date of this Version

2017

Publication Source

Marketing Science

Volume

36

Issue

2

Start Page

195

Last Page

213

DOI

10.1287/mksc.2016.1007

Abstract

We introduce a new methodology that can capture and explain differences across a series of cohorts of new customers in a repeat-transaction setting. More specifically, this new framework, which we call a vector changepoint model, exploits the underlying regime structure in a sequence of acquired customer cohorts to make predictive statements about new cohorts for which the firm has little or no longitudinal transaction data. To accomplish this, we develop our model within a hierarchical Bayesian framework to uncover evidence of (latent) regime changes for each cohort-level parameter separately, while disentangling cross-cohort changes from calendar-time changes. Calibrating the model using multicohort donation data from a nonprofit organization, we find that holdout predictions for new cohorts using this model have greater accuracy—and greater diagnostic value—compared to a variety of strong benchmarks. Our modeling approach also highlights the perils of pooling data across cohorts without accounting for cross-cohort shifts, thus enabling managers to quantify their uncertainty about potential regime changes and avoid “old data” aggregation bias.

Copyright/Permission Statement

Originally published in Marketing Science © 2017 INFORMS

This is a pre-publication version. The final version is available at http://dx.doi.org/10.1287/mksc.2016.1007

Keywords

changepoint, cross-cohort, hierarchical Bayesian, forecasting, customer-base analysis, customer lifetime value, reversible-jump MCMC

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Date Posted: 15 June 2018

This document has been peer reviewed.