Proper operational fuel pricing fundamentals provide solid benefits today while making a transition to an AI solution easier down the road.
By Brandon Gormley
Fuel pricing models have progressed from rudimentary cost-plus approaches to competitor-based rules pricing and spreadsheets to SAS solutions. So, what is the next step in the progression? The answer is undoubtedly AI. More specifically, sophisticated neural networks that “think and learn” like humans and churn through a multitude of datasets in a manner beyond human capacity.
Setting a pathway for AI pricing and other AI enterprise solutions can be much easier by taking some fundamental steps, mostly related to your team, data and execution.
First, it helps to have a team with strong lines of communication, which acts as an internal conduit to keep team members aligned with the decision-making process. It’s crucial to have a system in place to accept feedback, review performance and make adjustments when necessary. Without a systematic process, decisions lack discipline and aren’t appropriately evaluated for future corrections.
The fuel team must also have clear objectives that they are collectively working to accomplish. This could be achieving internal KPIs or company-specific financial goals that help align efforts and keep team members marching in the same direction.
The next critical element is data. Nowadays, knowing the costs, volumes and margins of your company, as well as brand performance versus competition, is practically table stakes. The faster and more accurately you can capture this information, the better suited you are to make more accurate, data-driven decisions and react more quickly to market changes.
Lastly, there is execution. How quickly can you implement new price positions for your customers, and what effort does it take once a price change or change in pricing strategy is made? Ultimately, an automated integration directly to your point of sale and/or price signs is optimal. It will enable you to confidently change prices more frequently and faster. At an absolute minimum, establish high expectations with store team members for manual price changes.
Why mention the importance of these building blocks regarding AI? Their value is certainly not exclusive to that technology. It is because AI will challenge your teams to manage your fuel business at a far more sophisticated level. Your pricing analysts will make the transition from an analyst who prices fuel all day to a true analyst who occasionally must make refinements to price positions. Today’s AI constantly review data inputs, including prices, cost, volumes and margins, but they also evaluate customer transactions and mobility data to create demand curves by hour and by day so that you can optimize your price position depending on what you want to achieve. These minor price adjustments at the correct times present themselves as “micro-opportunities” to boost your bottom line.
The benefits can go well beyond these micro opportunities. Because these models use transactional-level data, more sophisticated versions can alert or notify you when pumps aren’t working correctly or when other issues arise that could cause a bad customer experience at the store, helping your fuel operation run as smoothly as possible.
So get ready for the AI future and enjoy the clear operational benefits from solid fundamentals both before and after that transition.
Brandon Gormley joined OPIS in November of 2023. Before joining OPIS, Brandon spent a decade in the convenience retail industry, leading fuel pricing, merchandise pricing and business intelligence teams.
