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

Tong Wang (), Cheng He (), Fujie Jin () and Yu Jeffrey Hu ()
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Tong Wang: Tippie College of Business, University of Iowa, Iowa City, Iowa 52241
Cheng He: Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706
Fujie Jin: Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Yu Jeffrey Hu: Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308

Information Systems Research, 2022, vol. 33, issue 2, 659-677

Abstract: 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.

Keywords: interpretable machine learning; response curve evaluation; marketing campaigns; mall customer traffic (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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