查看文章

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 …