A Bayesian multivariate Poisson regression model of cross-category store brand purchasing behavior |
| |
Authors: | Hui-ming Wang, Manohar U. Kalwani,Tolga Ak ura |
| |
Affiliation: | aCollege of Business, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94132, USA;bKrannert Graduate School of Management, Purdue University, 403 W. State St., West Lafayette, IN 47907, USA |
| |
Abstract: | The availability of cross-category transaction data in the retailing industry has enabled the investigation of interdependence in consumer purchase behavior across product categories. In this paper, we develop a multivariate count model to uncover and predict the pattern of cross-category store brand purchasing behavior. The proposed multivariate Poisson regression model, which we estimate using a Bayesian approach, provides flexibility in capturing cross-category correlations for sparse multivariate purchase data associated with infrequently purchased categories or purchasing in retail outlets such as warehouse clubs. We compare the goodness-of-fit of the proposed Poisson regression model with alternate benchmark models using customer purchase records across five product categories from a national warehouse club and find that the proposed model provides a superior fit. We also carry out a profitability analysis to illustrate the use of the model in planning cross-promotions. |
| |
Keywords: | Cross-category store brand purchasing behavior Multivariate count data Mixed Poisson models |
本文献已被 ScienceDirect 等数据库收录! |
|