When managers and researchers encounter a dataset, they typically ask two key questions: (1) which model (from a candidate set) should I use? and (2) if I use a particular model, when is it going to likely work well for my business goal? This research addresses those two questions, and provides...

Stand-alone marketing models are well-suited to deal with different behavioral features such as variation in transaction frequency (customer heterogeneity with latent classes), recency and attrition (“buy ‘till you die” models), and more general changes in customer transaction rates...

Many datasets, from different and seemingly unrelated marketing domains, all involve "paths" - records of consumers' movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make...

We introduce a new methodology that can capture and explain differences across a series of cohorts of new customers in a repeat-transaction setting. More specifically, this new framework, which we call a vector changepoint model, exploits the underlying regime structure in a sequence of acquired...

While single-brand reward programs encourage customers to remain loyal to that one brand, coalition programs encourage customers to be “promiscuous” by offering points redeemable across partner stores. Despite the benefits of this “open relationship” with customers, store managers face...

We examine grocery shopping paths through the lens of the Traveling We examine grocery shopping paths through the lens of the quot;Traveling Salesman Problemquot; (TSP), a classic paradigm from the field of operations research. We define the quot;TSP-optimalquot; path for each shopper as the...

Multi-service providers, such as telecommunication and financial service companies, can benefit from understanding how customers’ service portfolios evolve over the course of their relationships. This can provide guidance for managerial issues such as customer valuation and predicting...

The widespread popularity and use of both the Poisson and negative binomial models for count data arises, in part, from their derivation as the number of arrivals in a given time period assuming exponenitally distributed interarrival times (without and with heterogeneity in the underlying base...

Hidden Markov Models (HMMs) have emerged as an empirical “workhorse” in the marketing literature in capturing and forecasting within-customer non-stationary behaviors. Extant research has demonstrated that HMMs typically outperform nested benchmarks when examining fit statistics aggregated...