Heterogeneity in feature importance and prediction performance for sales at the market and store levels: the case of branded yogurt products in Quebec

The supply and demand of fresh food products must be tightly integrated to mitigate food waste, economic losses, and expansion of the environmental footprint. In this study, we use a novel loyalty program dataset from a grocery retailer in Quebec, Canada to predict demand for yogurt products for 17 months from 2015 to 2016. Focusing our attention on 13 newly launched yogurt products from a local manufacturer, we build and test 18 different machine learning models capable of predicting demand for individual products at the aggregate market level, as well as for each store. Store-level data were matched to neighborhood demographic data from the 2016 Canadian census to enrich features. Overall, 330 features were engineered to provide information on the product, marketing and promotions, store, and neighborhood over time. Analyses were conducted using Python 3 in Google Collaboratory and open-source libraries. Results from the best market-level model (random forest) achieve an r-squared of 84.0% on test data, while the store-level model (light gradient boosting machine) only achieves 57%. The results show that ML tools can be useful in modeling demand for new products at aggregate levels but achieving accurate predictions at more granular levels remains a hurdle to overcome. Insights for the preparation and analysis of loyalty data are discussed.