Machine Learning Empowers C-stores to Predict Consumer Behavior and Increase Profit
The current c-store customer journey is much more complex than in years past, but it is also more important than ever before. Figuring out what customers want is a never-ending process because customer demand and expectations change. This is especially true during periods of rapid environmental and economic changes when consumers are likely to adjust their purchasing behavior and spending habits (i.e. during times of economic downturn, certain CPG sales increase while others may decrease).
However, most consumers do tend to revert back to their wants over time. For example, people who usually purchase Snickers for an afternoon snack will still buy Snickers when they stop to fill up their gas tank, and tobacco users will eventually make their way to their local c-store to purchase their favorite brands and products. There are some consistencies in c-store shoppers in that they still want easy, simple, and fast transactions with a pleasant shopping experience. Although, the products that were important in 2019 have shifted greatly in 2020.
During a rapidly changing landscape, c-stores can’t rely on past behavior alone as a predictor of future behavior. For that reason, traditional demand forecasting methods are not sufficient. Looking at historical sales data and using traditional demand forecasting is not as accurate as newer methods. Also, the findings from these methods tend to be a poor indicator of future decision making, especially when the global economy has been unexpectedly impacted.
C-stores can, and should, have a localized product assortment that meets consumer expectations and demand. When looking at the scope of foodservice, CPG, and tobacco products available in the market, deciding what products to carry (and how much) should not be based on outdated methods or gut instinct. Not only will this result in fewer sales, but it can waste other resources, like time and human capital.
How can c-stores accurately predict what future consumer behavior will look like? The answer can be found in machine learning. By using your customer and supply chain data, machine learning makes in-store shopping patterns easier to identify and can learn what products you should stock, how much in each location, and when you should carry them. Machine learning also accounts for external disruptions and helps your c-store chain get back on track faster.
At 4R, each of our c-store and grocery solutions is built using machine learning and AI. Powered by 4R’s proprietary algorithms, your c-store chain can predict future demand and purchasing behavior. As a result, you will:
- Increase sales
- Increase in-stocks
- Learn what each stores’ assortment should be
- Utilize and maximize shelf space.
This is all done without expensive hardware or IT resources and will save you time. 4R customers realize ROI in months, not years.
Contact us today to learn more about how 4R’s machine learning solutions can help you grow your business.