Amidst the growing focus on media engagement and customer value in retail marketing literature, mobile commerce (MC) research has gained prominence. This research explores how customers employ mobile operating systems to engage with retailers and extract value within the context of Fast Moving Consumer Goods (FMCG) in retail. In Study 1, a survey involving 398 users uncovered that the customer handset OS moderates the effects of social media, traditional media engagement, and retail “place” on customer value. In Study 2, leveraging data from a foreign FMCG brand deeply immersed in social media platforms, we scrutinize how such engagement dynamics affect the influence of “place” on product sales across e-commerce and conventional retail channels. Our findings make significant theoretical contributions to comprehending customer value in MC, with practical implications for marketers, emphasizing the potential of customer mobile operating system as a valuable tool for effective marketing strategies.

With the significant growth of the e-commerce business, the retail industry is experiencing rapid developments, leading to the explosion of the number of stock-keeping units (SKUs). Therefore, it calls for forecasting algorithms to forecast a large number of product-level demands over a short forecasting horizon. We developed a novel machine learning algorithm—the spatial-temporal gradient boosting tree (ST-GBT)—for demand forecasting for the retail industry. By incorporating the cross-section and time-series information in the existing gradient-boosting decision tree algorithm, our new algorithm can accurately forecast tremendous SKUs in one process. Furthermore, we show potential factors related to the retail industry, while new factors, such as higher-order statistics and risk-free interest, are also proposed for demand forecasting tasks. The numerical experiment results based on a large e-commerce company’s historical transaction records support the comparative merits of the new algorithm with superior accuracy and automation ability.