Using AI to battle declining profitability in convenience stores

Owners of convenience stores continue to fight an uphill battle with declining profitability, which hovers around 5% for small stores and 10% for large chains. Many face the daily challenge of having to do more with less to survive and thrive in today's chaotic business environments.

Fortunately, there could be a way to combat these challenges — and it starts with leveraging something you have in your possession: first-party data.

Your most valuable asset

"Data is the new oil," a once corny catchphrase, is fast becoming a go-to strategy for business leaders in the convenience store arena to implement to garner a competitive advantage.

Convenience stores typically have a library of customer, product, and sales information in their POS, ordering app, or kiosks. When interconnected and made actionable, these data repositories provide a wealth of information to help business leaders in this vertical know their customers better than they know themselves — potentially giving your business an unfair advantage over your competitors.

Three ways AI can help

According to data from your rewards platform, John is a loyal customer who tops off his car every Sunday evening and buys a bunch of avocados. However, upon further analysis, John's buying patterns could do more harm than good for your business, as he seems only to purchase loss leader items.

Another area for improvement is the store owner's overstocking of avocados and having to discount them heavily on Sundays, eroding profitability for this and other low-margin items.

By studying data that you own, the predictive power of AI can help improve your store's top and bottom-line growth:

1. Personalized recommendations: By analyzing John's purchase patterns and comparing them to similar customers, offer John other high-margin products that pair well with avocados - chips, salsa, or beer. Communicate these recommendations via text, email, or your app before his weekly Sunday store trips.
2. Inventory forecasting: Overstocking and understocking products affect your store's financial health. Having too much of a product, you risk loss through theft, expiration of products, and increasing storage costs. Carrying too little of popular items leads to lost sales, damage to your store's reputation, and higher customer churn rates. By studying when and what customers purchase using time-series analysis, stores can forecast how much of a given product will sell tomorrow, next week, or next month and adjust stock strategies accordingly.
3. Optimal pricing: Setting the right price helps the store protect its margins. In the past, setting pricing strategies were as simple — I put the product's price equal to my competitors, minus 5%. Now, it's more complex. Machine learning regression algorithms and price elasticity models would set the optimal pricing strategy by studying your store's historical sales patterns, looking into seasons, time of day, and customer segments.

All three of these AI-driven strategies correlate with one another to help maximize revenue, profitability, and competitiveness in your respective markets.

In the future

Today's convenience stores and their customers' expectations are constantly evolving. Customers want a place to get quality food and drink items - craft beer, artisan coffee, and sandwiches when fueling their vehicles or charging their EVs.

Serving your customers better and improving the store's profitability starts with knowing what your customers will do before they enter your establishment. By leveraging your first-party data and using AI in personalizing recommendations, inventory forecasting, and optimizing pricing, you can expect to improve sales, increase average order value, and reduce customer churn to enhance profitability for the foreseeable future.