Add time:08/14/2019         Source:sciencedirect.com

Extant customer-base models like the beta geometric/negative binomial distribution (BG/NBD) predict future purchasing based on customers' observed purchase history. We extend the BG/NBD by adding an important non-transactional element that also drives future purchases: complaint history. Our model retains several desirable properties of the BG/NBD: it can be implemented in readily available software, and estimation requires only customer-specific statistics, rather than detailed transaction-sequence data. The likelihood function is closed-form, and managerially relevant metrics are obtained by drawing from beta and gamma densities and transforming these draws to a sample average. Based on more than two years of individual-level data from a major U.S. internet and catalog retailer, our model with complaints outperforms both the original BG/NBD and a modified version. Even though complaints are rare and non-transactional events, they lead to different substantive insights about customer purchasing and drop-out: customers purchase faster but also drop out much faster. Furthermore, there is more heterogeneity in drop-out rates following a purchase than a complaint.

► We incorporate complaints into the well-known purchase-only BG/NBD model. ► The proposed model is easy to implement and leads to improved forecasts. ► Though being rare and non-transactional, complaints change inference on purchasing. ► Complaints increase drop-out after purchase more than they increase purchasing. ► There is more heterogeneity in drop-out following a purchase than a complaint.

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