Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model

Today's managers are very interested in predicting the future purchasing patterns of their customers, which can then serve as an input into "lifetime value" calculations. Among the models that provide such capabilities, the Pareto/NBD "counting your customers" framework proposed by Schmittlein et al. 1987 is highly regarded. However, despite the respect it has earned, it has proven to be a difficult model to implement, particularly because of computational challenges associated with parameter estimation. We develop a new model, the beta-geometric/NBD BG/NBD, which represents a slight variation in the behavioral "story" associated with the Pareto/NBD but is vastly easier to implement. We show, for instance, how its parameters can be obtained quite easily in Microsoft Excel. The two models yield very similar results in a wide variety of purchasing environments, leading us to suggest that the BG/NBD could be viewed as an attractive alternative to the Pareto/NBD in most applications.

[1]  Francis J. Mulhern,et al.  Customer Profitability Analysis: Measurement, Concentration, and Research Directions , 1999 .

[2]  Walter Gautschi,et al.  Mathematics of computation, 1943-1993 : a half-century of computational mathematics : Mathematics of Computation 50th Anniversary Symposium, August 9-13, 1993, Vancouver, British Columbia , 1994 .

[3]  Richard Colombo,et al.  A stochastic RFM model , 1999 .

[4]  Glen L. Urban,et al.  Evolutionary Model Building , 1971 .

[5]  David C. Schmittlein,et al.  Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort? , 1988 .

[6]  Robert A. Peterson,et al.  Customer Base Analysis: An Industrial Purchase Process Application , 1994 .

[7]  Walter Gautschi,et al.  NUMERICAL EVALUATION OF SPECIAL FUNCTIONS , 2001 .

[8]  Couchen Wu,et al.  Counting your customers: Compounding customer's in-store decisions, interpurchase time and repurchasing behavior , 2000, Eur. J. Oper. Res..

[9]  Arnd Huchzermeier,et al.  The 2003 ISMS Practice Prize Winner: Optimizing Rhenania's Direct Marketing Business Through Dynamic Multilevel Modeling (DMLM) in a Multicatalog-Brand Environment , 2004 .

[10]  Donald G. Morrison,et al.  Estimating Heterogeneity in Consumers' Purchase Rates , 1991 .

[11]  David C. Schmittlein,et al.  Counting Your Customers: Who-Are They and What Will They Do Next? , 1987 .

[12]  Sönke Albers,et al.  Impact of types of functional relationships, decisions, and solutions on the applicability of marketing models , 2000 .

[13]  W. Reinartz,et al.  The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration , 2003 .

[14]  Arnd Huchzermeier,et al.  Optimizing Rhenania´s Direct Marketing Business through Dynamic Multi-Level Modeling (DMLM) in a Multi-Catalog-Brand Environment , 2004 .

[15]  Peter S. Fader,et al.  Forecasting Repeat Sales at CDNOW: A Case Study , 2001, Interfaces.

[16]  Sunil Gupta,et al.  Stochastic Models of Interpurchase Time with Time-Dependent Covariates , 1991 .

[17]  Steven M. Shugan Endogeneity in Marketing Decision Models , 2004 .

[18]  W. Reinartz,et al.  On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical Investigation and Implications for Marketing , 2000 .

[19]  C SchmittleinDavid,et al.  Counting Your Customers , 1987 .

[20]  C. Narasimhan,et al.  Customer Profitability in a Supply Chain , 2001 .

[21]  D. Jain,et al.  Customer lifetime value research in marketing: A review and future directions , 2002 .

[22]  Michel Wedel,et al.  Modeling large datasets in marketing , 1998 .

[23]  Michel Wedel,et al.  Modeling large data sets in marketing , 2001 .