Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model

• 成果类型: Article
• 署名作者: Wang, Tong; He, Cheng; Jin, Fujie; Hu, Yu Jeffrey
• 署名单位: University of Iowa; University of Wisconsin System; University of Wisconsin Madison; Indiana University System; IU Kelley School of Business; Indiana University Bloomington; University System of Georgia; Georgia Institute of Technology
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.1078
• 发表日期: 2022
• 页码: 659-677
• 关键词: generalized additive-models; Budget allocation; sales response

摘要

In this study, we use newly available data and develop a novel interpretable machine learning model to evaluate how different types of marketing campaigns and budget allocations influence malls' customer traffic. The data we use is a large-scale customer traffic data set, collected through AI-chip-embedded sensors, across 25 malls over a two-year period, and we combine it with detailed campaign information for our analyses. We classify the campaigns into five categories based on the approach and timing of the campaigns. We then develop an innovative interpretable machine learning model, named generalized additive neural network model (GANNM), to accurately learn the response curves for different marketing campaigns. The response curves characterize the impact of campaign budget on customer traffic. We demonstrate that this new model has better predictive accuracy compared with current interpretable models and also yields additional business insights. We find that campaigns with experience incentives lead to larger increases in customer traffic than campaigns with sales incentives only, and the contrast is more significant for campaigns in off-peak periods. In addition, malls can piggyback on online promotion events and boost customer traffic with campaigns held at the same time. We further demonstrate that the optimized budget allocation based on the response curves learned by GANNM yields a 11.2% increase in customer traffic overall, compared with 3.2% achieved by a baseline with preassumed functional forms of response curves and 1.0% achieved by a post hoc explanation method. Overall, our proposed model provides more accurate estimations for response curves and presents interpretable and actionable insights for managers.