Augmented cross-selling through explainable AI -- a case from energy retailing

TL;DR

This paper demonstrates how explainable AI, specifically SHAP, can effectively support cross-selling strategies in energy retailing by providing accurate predictions and meaningful explanations, thereby enhancing decision-making and customer insights.

Contribution

It empirically evaluates the use of SHAP for cross-selling in energy retail, showing its effectiveness in providing reliable explanations for customer purchase behavior.

Findings

SHAP achieves up to 86% prediction accuracy (AUC)

Explanations from SHAP are valid for actual buyers

Supports improved decision-making in energy retailing

Abstract

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.