Things are looking up for in-store shopping at the convenience store: Vaccine availability is increasing consumers’ comfort levels with entering the store; consumers are resuming their commutes to work; and summer travels are likely to usher in motorists seeking fuel, snacks and supplies on the go. According to Technomic’s Q2 2021 C-Store MarketBrief, fuel customer conversion has returned to pre-pandemic levels, with 50% of c-store customers entering the store nearly every time they fuel up.
While off-premise services have been a key facet of c-store operations amid the pandemic, the promise of an uptick in foot traffic offers an opportunity to increase basket sizes. What’s more, retailers can implement forefront technologies—namely, data collection for weather and events forecasting—to gain a competitive edge during the dynamic months ahead.
Machine learning affords a competitive edge
C-store customers’ needs change with the weather—literally. Perhaps more than many other retail markets, convenience stores hold a special responsibility to pivot on a dime and meet their consumers’ of-the-moment needs.
External factors such as the weather, local sports and events can have a significant impact on demand: Hot summer days can drive demand for cold drinks and sunglasses, for instance, and nearby events can bring in foodservice customers seeking a quick bite. Predicting these external changes and extrapolating their effects on consumer demands, however, is easier said than done. How can c-store retailers know exactly when to stock up on ice cream or move the umbrellas to the front of the store?
The use of weather data in demand forecasts is a prime example of the power of machine learning. Machine-learning algorithms can automatically detect relationships between local weather variables and local sales. They can answer the questions that retailers might not think to ask, mapping market patterns on a highly granular, localized level and identifying key relationships that are less apparent than what “common sense” might detect.
So how can c-stores account for the full range of weather-related variables that influence consumers’ needs, including temperature, sunshine, rainfall and more? And how do other details factor in? Will the weather-related impact of sunshine be stronger in summer than in winter, or stronger on weekends versus workdays?
AI reporting holds the answers to all these questions and more with a specific focus on each convenience store’s location and consumer base. With machine learning, retailers can capture, analyze and implement data insights with accuracy and agility.
Leveraging key strategies in the coming months will be crucial for retailers to increase their sales and stand out from their competitors. RELEX’s proprietary demand forecasting technology can make this possible. Taking everything from historical demand patterns to internal business decisions and external factors into account, RELEX offers highly accurate, granular forecasts to help retailers improve decision-making and outcomes in areas such as store and distribution center replenishment, capacity planning, resource planning and more.
To learn more, visit Relex Solutions.
This post is sponsored by RELEX Solutions