A theory of predictive sales analytics adoption

Abstract

Given the pervasive ubiquity of data, 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 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 (PSA) applications support sales employees’ job performance. In particular, the PSAA model conceptualizes the key contingencies governing how the availability of PSA applications translates into adoption of these applications and, ultimately, job performance. These contingencies determine the extent to which sales employees adopt these applications to revise their decision-making and the extent to which these updates improve the decision outcome. To build the PSAA model, we integrate literature on predictive analytics and machine learning, technology adoption, and marketing capabilities. In doing so, this research provides a theoretical frame for future studies on salesperson adoption and effective utilization of PSA.