  The advance of Machine Learning (ML) has led to a strong interest in this
technology to support decision making. While complex ML models provide
predictions that are often more accurate than those of traditional tools, such
models often hide the reasoning behind the prediction from their users, which
can lead to lower adoption and lack of insight. Motivated by this tension,
research has put forth Explainable Artificial Intelligence (XAI) techniques
that uncover patterns discovered by ML. Despite the high hopes in both ML and
XAI, there is little empirical evidence of the benefits to traditional
businesses. To this end, we analyze data on 220,185 customers of an energy
retailer, predict cross-purchases with up to 86% correctness (AUC), and show
that the XAI method SHAP provides explanations that hold for actual buyers. We
further outline implications for research in information systems, XAI, and
relationship marketing.
