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.
|Journal||International Journal of Research in Marketing|
|Publication status||Published - 2011|
van Oest, R. D., & Knox, G. A. H. (2011). Extending the BG/NBD: A simple model of purchases and complaints. International Journal of Research in Marketing, 28(2), 30-37. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8R-51S6X4G-2&_user=522558&_coverDate=03%2F31%2F2011&_rdoc=1&_fmt=high&_orig=gateway&_origin=gateway&_sort=d&_docanchor=&view=c&_searchStrId=1718219503&_rerunOrigin=google&_acct=C000026138&_version=1&_urlVersion=0&_userid=522558&md5=dd153631ae654884fec94bc7d638e9eb&searchtype=a