Solutions by Industry

Optimizing Fuel Pricing: A Case Study

A leading US convenience retailer recently turned to APT to find opportunities to improve their existing fuel pricing strategy.

  • The retailer wanted to refine their existing fuel pricing strategy using rigorous in-market analysis.
  • APT designed tests to identify stores where a price strategy change would drive improved fuel and merchandise profit.
  • Targeted fuel price strategy changes improved profit by $4 million annually.
The Company:

 A leading US convenience retailer with over 1,000 locations

The Challenge:

This retailer felt money was being left on their table with their current fuel pricing approach. The company wanted to maximize the total site profit including fuel margin and merchandise profit. They wanted to price fuel high enough to have strong margins but not drive away traffic. But the noisy and rapidly changing environment of convenience retail made it difficult to quantify those factors. The retailer turned to APT’s Test & Learn™ Management System to cut through the noise and created dynamic store-level fuel pricing strategy focused on maximizing total site profitability.

The company’s existing strategy consisted of store-by-store protocols aimed at maintaining high margin per gallon and a price position informed by nearby competition. The protocol for each store had been based on anecdotal evidence – not hard data – of market dynamics, and the company wanted to embrace a more informed, data-driven pricing strategy.

The Solution:

Using APT's Test & Learn™ solution, the client implemented a price decrease in high potential stores, analyzed the results to understand why some sites performed better than others, and refined the pricing protocol going forward. This was executed as a series of tests. Each test refines the store-by-store strategy and provides hypotheses for future tests. For example, the company hypothesized that a certain segment would benefit from lowering the price by one cent versus nearby competition. Quantifying the impact is simple in concept but is difficult to execute because of the “signal vs. noise” challenge. In this example, the retailer wanted to isolate a small percentage change while fuel volume and merchandise sales fluctuated by up to 70% each day. APT applied sophisticated analytic techniques to mitigate the fuel volume and merchandise sales volatility. Cutting through the noise, APT found that a one-cent decrease in fuel price drove a significant increase in fuel volume and a modest increase in merchandise sales. This result translated into $7,000 of incremental annual profit per store. The test was a success.

The test results also provided hypotheses for future fuel price testing. APT’s analyses identified specific site characteristics linked to outsized fuel and merchandise lifts. For example, fuel volume and merchandise sales in type A stores increase 3x faster than all other stores.

This observation helped to confirm the long-held belief that customers visiting Type A stores were less brand loyal and more aware of competitive pricing than average customers. More importantly, the incremental profit from Type A stores was $20,000 on average. Future price decreases were therefore focused more on Type A stores.

The Results:

Through this ongoing testing and monitoring, the company can continuously refine its fuel pricing strategy over the long-term. This approach generates about $4 million in incremental annual profit for the company.