There are numerous transportation hubs across the United States that are linked to oil refineries via pipelines, railroads, or water terminals, and these areas often feature large oil storage facilities. Gas stations typically receive their fuel supply through trucks that transport it from these terminals. Every morning, a steady flow of tanker trucks arrives, delivering fuel to underground storage tanks at gas stations before it is sold to customers.
Artificial intelligence is now offering fuel retailers new ways to increase profitability. PriceAdvantage, a division of Skyline Products, has been developing smart signage solutions for over 40 years within the transportation and petroleum industries. Their previous fuel pricing software relied on linear algorithms to predict price fluctuations, which impacted sales and profits—but this method had its limitations.
Brendan Doner, a Scientific Data Specialist at PriceAdvantage, explained that when prices were reduced by just two cents, the predictions made by the old system had about a 30% error rate, making them unreliable. The previous approach used a top-down modeling technique based on economic theory, but it didn’t always align with real-world outcomes. For instance, if a retailer raised prices by 10 cents above their usual rate and 6 cents above the market average, customers might quickly lose trust, leading to long-term losses in sales.
The old model also assumed that lowering prices would always lead to increased sales, regardless of how competitors responded. This outdated strategy ignored the dynamic nature of the market. Experienced retailers, however, often gathered price data through frequent site visits or services like Oil Price Information Services (OPIS) to stay ahead of competitor moves.
Doner emphasized that the new AI-powered system doesn’t rely on assumptions but instead uses real-world data to make more informed decisions. Unlike traditional models, which act like "black boxes" without clear reasoning, the AI-driven approach provides transparency and probability-based insights. For example, it can tell a retailer that there's a 57% chance of meeting a sales target under a specific pricing strategy.
This new software is tailored to individual store needs, calculating success rates in different markets based on factors like profit margins, sales targets, and timing. It also considers how the timing of price changes affects the likelihood of achieving goals—such as whether raising prices slightly above the market could actually boost sales.
Additionally, the new system accounts for competitor reactions. For instance, changing prices at 3 p.m. on a Wednesday might allow the retailer to maintain the change until the next morning, while adjusting prices during peak customer hours could prompt competitors to respond quickly. The AI model helps track these dynamics in real time, giving retailers a clearer picture of how their pricing strategies will play out in the broader market.
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