A Theory of Predictive Sales Analytics Adoption

Due to the pervasive data ubiquity, sales practice is moving rapidly into an era of predictive analytics, using quantitative methods including machine learning algorithms to reveal unknown information such as customers’ personality, value, or churn probabilities. However, many sales organizations face severe difficulties when implementing predictive analytics applications. This article elucidates these difficulties by developing the PSAA Model—a conceptual framework that explains how predictive sales analytics applications support sales employees’ job peformance. Specifically, the PSAA Model posits that predictive sales analytics applications only improve job performance if (1) sales employees adopt these applications to revise their decision-making, and (2) these updates inherently improve the decision outcome. The mechanisms underlying these two preconditions fundamentally differ. While the former is explained by well-established technology adoption theories, the extent to which adoption improves decision-making is determined by the value potential in the PSA application and the decision-making environment. Thereby, this paper provides a theoretical frame for future studies on predictive sales analytics