Predicting consumer variety-seeking through weather data analytics

Marketing decision support systems (MDSS) incorporate both internal and external data in performing analytics to improve business effectiveness. Weather data have long been considered a crucial external data input in practitioners' marketing strategy; however, academic research on how weather conditions affect consumer behaviors has been limited. To fill this gap, this research investigates how weather parameters, including sunlight, temperature, and air quality, can be incorporated into MDSS to predict consumers' variety-seeking in their purchases using public weather data and supermarket panel data for five typical retail products. Our analyses show that weather conditions are associated with greater variety-seeking behavior. The results afford insights into how to exploit weather data for data analytics and employ weather targeting strategies to save promotional expenses and increase profitability. (C) 2018 Elsevier B.V. All rights reserved.