Predicting Market Basket Additions as a Way to Enhance Customer Service Levels

Abstract

It is imperative that online companies have a complete in-depth understanding of online behavior in order to provide a better service to their customers. This paper proposes a model for real-time basket addition in the e-grocery sector that includes predictors inferred from anonymous clickstream data, such as a Markov page view sequence discrimination value. This model aims at anticipating the addition and the non-addition of items to customers’ market basket, in order to enable marketers to act conveniently, for example recommending more appropriate items. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of anonymous clickstream data taken from the servers of a European e-retailing company is explored. The empirical results reveal the high predictive power of the model proposed, based on the explanatory variables introduced, as well as the supremacy of random forests over logistic regression.