How to Project Customer Retention

28 Pages Posted: 16 Sep 2005

See all articles by Peter Fader

Peter Fader

University of Pennsylvania - Marketing Department

Bruce Hardie

London Business School

Date Written: May 2006

Abstract

At the heart of any contractual or subscription-oriented business model is the notion of the retention rate. An important managerial task is to take a series of past retention numbers for a given group of customers and project them into the future in order to make more accurate predictions about customer tenure, lifetime value, and so on. In this paper we reanalyze data from a leading book on data mining (Berry and Linoff 2004), who drew the dire conclusion that parametric approaches do not work for such a task. As an alternative to common curve-fitting egression models, we develop and demonstrate a probability model with a well-grounded story for the churn process. We show that our basic model (known as a shifted-beta-geometric) can be implemented in a simple Microsoft Excel spreadsheet and provides remarkably accurate forecasts and other useful diagnostics about customer retention. We provide a detailed appendix covering the implementation details and offer additional pointers to other related models.

Keywords: Retention, churn, forecasting, customer base analysis, probability models, beta-geometric

Suggested Citation

Fader, Peter and Hardie, Bruce, How to Project Customer Retention (May 2006). Available at SSRN: https://ssrn.com/abstract=801145 or http://dx.doi.org/10.2139/ssrn.801145

Peter Fader (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

Bruce Hardie

London Business School ( email )

Regent's Park
London, NW1 4SA
United Kingdom

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