Augmented Cross-Selling Through Explainable AI : a Case From Energy Retailing

Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Haag, Felix ; Hopf, Konstantin  ; Menelau Vasconcelos, Pedro; Staake, Thorsten
Title of the compilation: ECIS 2022 Proceedings
Corporate Body: Association for Information Systems (AIS)
Conference: 30. European Conference on Information Systems (ECIS), Timișoara, Romania
Publisher Information: AISeL
Year of publication: 2022
Pages: 1-19
Language(s): English
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.
GND Keywords: Cross Selling; Energiehandel; Maschinelles Lernen; Relationship-Marketing; Verbesserung
Keywords: Cross-Selling, Energy Retailing, Explainable Artificial Intelligence, Machine Learning, Relationship Marketing, Task Augmentation
DDC Classification: 004 Computer science  
333.7 Natural ressources, energy, environment  
RVK Classification: ST 300   
Peer Reviewed: Ja
International Distribution: Ja
Type: Conferenceobject
Release Date: 5. July 2022
Project: Kombinierte Verhaltens- und Analyse-Innovation zur Steigerung der Energieeffizienz mittels Smart Meter in Privathaushalt; Teilprojekt: Maschinelle Lernverfahren für Energieeffizienz-Feedback