Effective Implementation of Predictive Sales Analytics

Abstract

Sales managers are unlikely to reap the benefits of implementing predictive analytics applications when salespeople show aversion to or lack understanding of these applications. For managers, it is essential to understand which factors mitigate or exacerbate these challenges. This article investigates these factors by studying the implementation of an application that predicts customer churn. Using 9.7 million transactions from a business-to-business company, the authors develop a predictive model of customer churn, implement it in a field experiment, and study its treatment effects using causal forests. Furthermore, the authors manipulate one specific mitigation strategy proposed by prior literature: the fostering of users’ realistic expectations regarding the accuracy of an algorithm. The results show that the effectiveness of the churn prediction application strongly depends on customer characteristics (most importantly the predicted churn probability and prior revenue) and salesperson characteristics (technology perceptions, abilities, and selling orientations). Fostering realistic expectations improves the effectiveness of the churn prediction only under very specific circumstances. Two follow-up stimuli-based experiments conceptually replicate key results of the field study. Therefore, this article helps build theory on predictive sales analytics and provides specific guidance to managers aiming to increase their return on analytics investments.