We address a critical question that many firms are facing in this era of "big data'': Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers' transaction activity? We address this...

In today's turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge – with a particular focus on predicting customer churn. However, several other...

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,...

A problem that pharmaceutical companies face is how to project the persistency patterns of patients who are taking their manufactured medications into the future; i.e., how to determine the percentage of patients who will continue to refill a given prescription on a timely basis. The authors have...

Calculating customer lifetime value is complex, and the use of familiar regression-type models - which attempt to forecast future behavior based only on observable measures - is problematic and inadequate. A better approach is to perform the calculations using a probability model of buyer...

A criticism of purchase-based brand loyalty measures is that they are confounded by the marketing mix variables that affect brand choice. This paper investigates the magnitude and direction of the associations for share of category requirements (SCR), defined as each brand's share among the...

The Tuscan Lifestyles case (Mason 2003) offers a simple twist on the standard view of how to value a newly acquired customer, highlighting how standard retention-based approaches to the calculation of expected CLV are not applicable in a noncontractual setting. Using the data presented in the...

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...