Real-Time Footfall Prediction Using Weather Data: A Case on Retail Analytics

Abstract

Be it a retailer, producer, or supplier, the weather has a substantial effect on each one of them. Climate variability and weather patterns have become critical success factors in retail these days. As a matter of fact, weather forecasting has become a $3 billion business now. One of the main reason behind this surge is the capability of the forecasters to sell weather-related information to businesses who then strategize their various decisions regarding inventory, marketing, advertising, etc. accordingly. Hence only those retailers who stay “ahead of the game” will be able to enjoy huge sales while others who do not would face the consequences. Various studies regarding change in consumer behavior occurring due to the change in weather conditions have shown that even a degree change in temperature affects the store’s traffic and reflect the growing importance of predictive analytics in this domain. However, these studies incorporate only the historical weather statistics into account. In this paper, we will propose our methodology for footfall analytics to see how the changes in weather conditions will impact the retail store’s traffic and thereby retailing value chain, using real-time weather forecasts and footfall data. This analysis provides a platform for retailers to make evidence-driven decisions and strategize their business plan which would help them to deepen the customer involvement and to get efficiency in the planning process.