Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences

A primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers’ needs. A customer’s purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer’s probability of a next purchase and target the customers most prone to purchasing a particular product. The bias– variance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias–variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions

Term of Contract and Portfolio Aware Churn Modeling in Telecommunication Campaigns

Preventing customer churn is an important task in customer relationship management (CRM), in which the identification of customers with an intention to terminate one or more contracts plays a pivotal role. Today, typically survival analysis is used for this purpose. These approaches, in their standard configuration, assume a proportional, time-invariant influence of covariates. In telecommunications, for instance, these assumptions are questionable because of existing fixed-term contracts and term of notice clauses. These can be expected to result in nonmonotonous cancellation probabilities over time, with increased frequencies of cancellation in time periods before minimum subscription periods end. In this paper, we consider customerspecific contract duration dates within established methods of survival analysis. We introduce a novel, non-standard feature generation procedure for this purpose. In addition, we study the impact of product variety in a customer’s portfolio on his churn probability, as there is evidence both from theory and practical experiences in other industries that product variety can be related to loyalty. In the empirical part of the paper, we evaluate the proposed extended model using data provided by one of the largest telecommunication companies in Europe. Results show that both model extensions significantly increase churn prediction performance in out-of-sample tests