Boosting Store Sales Through Machine Learning-Informed Promotional Decisions

The sales of fashion products are influenced by uncertain and heterogeneous demands, necessitating predictive analytics to consider multiple explanatory variables and address the challenge of model uncertainty, which has been overlooked in prior research. To illustrate our solution, we first propose a novel forecasting estimator, which is characterized by provable optimal weighted forecasts and a collection of sub-model forecasts with various model specifications. We then validate our method empirically with store-level sales observations of a well-known international footwear brand, as an example of how a retailer can enhance its sales forecasts and improve promotion decisions after controlling model uncertainty. In a predictive analysis, the results show that controlling for model uncertainty between various predictors and store sales can produce more accurate forecasts of sales. With our proposed estimator, we also demonstrate the heterogeneity of promotion strategy importance for store with high and low previous sales. The additional analysis reveals that combo promotions have the most significant impact, and suggests adjusting the frequencies of gift and combo promotions to boost store sales. However, caution is advised when implementing both gift and combo promotions together to mitigate cannibalization effects.