You've probably been hearing the word "optimization" everywhere.
Contrary to the way many have been using it, the definition of fuel price optimization is not about simply having a smooth end to end process in place for pricing your network, neither is it about getting from price A to price B as quickly and efficiently as possible.
It's time to rein in the definition of fuel price optimization — to rescue it from a place of meaningless buzzwords and bring it back into the mathematical arena, where it truly belongs.
The bottom line: Fuel price optimization is the concrete execution of a mathematical paradigm and not the concept of wider process efficiencies (which are important also — our article, "What is Optimization, Anyway?" dives deeper into this).
Optimization is truly being utilized when your algorithm can make decisions for you, when your algorithm has used its amazing capacity and power to ascertain the best possible price at which you will sell fuel at the highest possible volume in your market.
There may be multiple price points available for each of your products. Optimization, then, occurs when your algorithm derives the optimal price for each product. By combining your products within product groups, you can understand forecast volumes, including different grades and octanes. Then, you can use your optimization tool to find the best set of prices to maximize your volume and profitability.
In other words, a fuel pricing optimizer:
Of course, fuel price optimization does involve timing as well — but these time constraints are based on your pricing position, your strategy, and the market itself. (See list above.)
For example, in incredibly dynamic markets, such as the German or Australian market, where competitor prices can change multiple times a day, being able to react at the appropriate speed is essential. However, even in other markets where the timing of your pricing changes is unlikely to make or break your retail experience, your algorithm's ability to consider every data point as it comes in — rather than at the behest of manual intervention — is still critical to the optimization process.
So how can fuel retailers commit to optimization in this way — as a true, scientific process?
To put it shortly, you need to know your software has a team of data scientists working to evolve the optimization algorithms to ensure they're capturing the latest market dynamics. That algorithm is being fed with data representing the market's current truth.
A fuel pricing optimization algorithm is not a "set-it-and-forget-it" kind of thing. It needs to be as flexible and up to date as possible to cater to the constantly evolving fuel markets we see today. We are able to benefit from the concept of citizen data science where sophisticated algorithms are encapsulated in easy to use software so that all of us have access to the power of optimization.
Additionally, you need clean, comprehensive data to feed to that algorithm to ensure it's putting out the right insights for your pricing team. You need to have documented your strategy through the use of constraints, guides, and rules that will support your algorithm's ability to consistently reflect those messages to the end consumer, while still focusing on your business goals. This piece of fuel pricing optimization requires a human element to ensure that the brand pricing strategy remains consistently at the forefront.
If you haven’t had to do any of that, then you're not using "fuel price optimization" in the true sense of the word.
Fuel price optimization is a much more scientific process than some fuel pricing strategists have been making it sound. Don't be fooled by the buzzword — true optimization requires the right mix of art, science, and experience to get right.
Luckily, you don't have to figure it out on your own. With decades of expertise in the fuel retail and convenience industry and complex optimization tools, Kalibrate can help you truly optimize your fuel pricing for success. Contact us today to learn more about how we can help you.