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

Posted: 18 Jun 2019 Last revised: 25 Jan 2021

See all articles by Tong Wang

Tong Wang

University of Iowa

Cheng He

Wisconsin School of Business

Fujie Jin

Kelley School of Business, Indiana University

Yu Jeffrey Hu

Georgia Institute of Technology - Scheller College of Business

Date Written: June 1, 2019

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 customer traffic to malls. The data we use is a large-scale customer traffic dataset, collected through AI-chip-embedded sensors, across 25 malls for a two-year period, and we combine it with detailed campaign information for our analysis. 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 pre-assumed functional forms of response curves. Overall, our proposed model provides more accurate estimation for response curves and presents interpretable and actionable insights for managers.

Keywords: Interpretable Machine Learning, Response Curve, Mall, Marketing Campaigns

Suggested Citation

Wang, Tong and He, Cheng and Jin, Fujie and Hu, Yu Jeffrey, Evaluating the Effectiveness of Marketing Campaigns for Malls Using A Novel Interpretable Machine Learning Model (June 1, 2019). Georgia Tech Scheller College of Business Research Paper No. 19-11, Available at SSRN: https://ssrn.com/abstract=3400105

Tong Wang

University of Iowa ( email )

341 Schaeffer Hall
Iowa City, IA 52242-1097
United States

Cheng He

Wisconsin School of Business ( email )

975 University Avenue
Madison, WI 53706
United States

Fujie Jin

Kelley School of Business, Indiana University ( email )

Business 670
1309 E. Tenth Street
Bloomington, IN 47401
United States
812-855-0943 (Phone)

HOME PAGE: http://https://kelley.iu.edu/facultyglobal/directory/FacultyProfile.cfm?netID=jinf

Yu Jeffrey Hu (Contact Author)

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

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