Fuel pricing models can help convenience retailers overcome missing competitor pricing data.

In some markets around the world, fuel prices can change up to 20 times a day. Sure, that’s an extreme case, but what influences price changes at the pump? The short answer is data, and lots of it.

An effective fuel pricing algorithm should evaluate multiple data sources: volume and margin targets, market costs and historic site-level sales, competitor prices, as well as seasonal, holiday, special event and time-of-day considerations. In our densely interconnected world, with information flowing left, right, and center, it’s easy to assume the fuel pricing data you need will always be at your fingertips. But what do you do when it isn’t?

Here, data scientists, like the ones who design PDI’s leading fuel pricing software, can help. Their strong analytical and numerical backgrounds combined with business and market expertise make them uniquely qualified to fill in the gaps. If you’re like most companies, however, you don’t keep a spare data scientist laying around. That’s where having a robust fuel pricing model with built-in machine learning capabilities comes into play.

Missing data can compromise your fuel pricing strategy

In a crowded market, knowing your competitors’ pricing positions, strategies, and behaviors can be one of your most valuable assets. Having an accurate picture of your market allows you to be agile and responsive when pricing, while ensuring your strategy is always purposeful and data-based.

But not all markets are created equal.

Although pricing data is public, it’s rarely transparent and readily available.  In these cases, the only available competitor price data usually comes from field observations or via third-party sources. Consequently, data can be incomplete due to technical issues or even a site manager oversleeping. And guess what? You can’t afford the errors or lost profits.

To be competitive, it’s necessary to have the best data to make the best decisions. A lack of information can have sites instantly falling behind in a rapidly changing market.

Harness the power of historic data and machine learning

Machine learning is a powerful tool that is transforming our industry, and it’s time to embrace it in our pricing strategies. A good pricing solution can harness machine learning to turn the historic data at your site into a predictive model.

These models can remove the guesswork and fill the gaps in your data. By combining pricing patterns, market trends, and current site information, a pricing model with machine learning capabilities can accurately forecast the missing prices across the market.

Combining the right solution with the right supplier

For convenience retailers hoping to remain competitive in an increasingly crowded marketplace, having the right technology in place will be a key differentiator in helping them optimize their operations, maximize profits and deliver competitive prices that keep their customers coming back.

With a honed understanding of the dynamics and interactions within your market, the right solution provider can leverage the science of machine learning and their industry expertise to give you an edge over the competition. For best results, be sure the predictive model you decide to use, like the one PDI recently released, works seamlessly with your holistic fuel pricing solution.

By filling in the gaps of missing data, you’ll have peace of mind knowing that you’re setting optimal prices for your market.

Did You Know: Your Source for PDI News provided by PDI, the leader in enterprise management software for the convenience retail and petroleum wholesale markets.

Meet PDI’s Data Scientists

Ed Cockcroft: Ed joined the PDI team in the late 90s and currently leads our data science team. One of his favorite things about being a data scientist is being able to engage with customers’ business problems in ways that provide genuinely new insights. Ed has a first-class undergraduate degree in mathematics, plus a master’s degree with distinction in mathematics, from Cambridge University.

Stephen Thatcher: Stephen joined the PDI team as a data scientist in 2017. Before that, he graduated with a first-class master’s degree in Mathematics and Philosophy from the University of Oxford. Stephen enjoys the challenge of keeping our solution “current” in the fast-growing field of data science.

Keanu Weng Kan: Keanu has been with PDI since 2018, following an undergraduate degree in Physics, and master’s degree in Biomedical Research at Imperial College, London. He particularly enjoys learning about how different businesses work and creating bespoke solutions for their needs.