The basic principles of fuel price optimization

In volatile markets, competitor fuel prices can fluctuate dozens of times per day, so you must be able to keep up with the changes and adjust your prices at the required speed – which is where automation and machine learning come in.
The basic principles of fuel price optimization

Monitoring and reacting to competitors’ fuel prices

Knowing your competitors and keeping an eye on their pricing activity is essential. In volatile markets, competitor fuel prices can fluctuate dozens of times per day, so you must be able to keep up with the changes and adjust your prices at the required speed – which is where automation and machine learning come in.

There’s an important point to be made here about also taking the time to consider and regularly re-evaluate who your true competitors are. Geographic proximity doesn’t necessarily make another site or fuel retailer a direct competitor — at least not one that you should measure your strategy against.

Take time to analyze all of the fuel retailers in the given area and any changes that have taken place. It’s not only new sites that impact the competitive landscape, changes to competitor brands, and new offerings from the local competition can have a significant impact on the effectiveness of your site. Ensuring your data is complete, and current, gives you a fuller, far more accurate view of which sites and brands you’re competing against.

Crucially, however, delivering on your strategy means not only being able to react to external events – such as competitor price changes, market events, and demand disruptions – but also being able to proactively approach your pricing, using your market position or your brand strength to make changes that positively impact your performance.

Assessing your margin capabilities

Keeping an eye on each product’s potential for margin is fundamental to profitable pricing, especially as margins fluctuate all the time.

Your margin potential will depend on your most recent product costs as well as taxes and duties, so these must feed into your pricing strategy and be updated regularly.

Tracking your volume performance

Your forecasted volumes are calculated from the pricing rules you have in place, so when you compare those forecasts to your actual sales volumes, you see exactly how effective your current pricing rules are – and whether they ought to change.

Closely tracking your volume performance will leave you better informed about which volume changes are a signal to adjust, and which are just short-term fluctuations that don’t require price reactions. Improving the measurement and visibility of your volume performance can help you to make optimal adjustments at the right times.

Aligning your prices with brand image, product strategy, and site strategy

There is more to price optimization than meeting volume or margin targets and competing with rivals.

You should always ensure that the fuel prices you’re proposing are right for your overall brand and the position you’re striving for in the market. If you start to price too low or too high, consumers will notice and this may change their perception of your brand, which in turn will affect brand loyalty and impact overall performance.

Your customers build a mental model based on the information they have and will generally refer to that same model until something significant changes their perception. So being the highest or lowest priced competitor for just a short period can have long-term impacts on customer perception and the volumes you sell.

On top of that, your prices must align with product strategy and individual site strategy.

So there is a lot to consider, and it’s a fine balancing act.

Harnessing the benefits of artificial intelligence and machine learning

Finally, making use of artificial intelligence and machine learning is important to ensure that you aren’t incorporating your team’s inherent bias in your fuel pricing strategy.

While subjective judgment and the knowledge and experience of your team can be hugely valuable, they should be reserved for exceptional cases, and not integrated into the fuel pricing model. Base your fuel price optimization models on data — not emotion.

Read more about how to achieve optimized pricing with AI and data science in Fuel futures: price optimization

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