A closer look at art vs science in retail fuel pricing and location planning

For too long, art and science have been pitted against one another. Though Aristotle's distinction between knowledge and craft might be one of the first known challenging characterizations of these concepts, we can see that the idea has propagated. (Note: Aristotle's "science" was about certainty or knowledge; whereas, science today is about experimentation, but eliciting relative certainty is still the aim.)

This dichotomization has grown, lending itself to much of our conversation around education, business and acumen. Even now, creative and factual approaches are often seen at odds — as much within the fuel retailing industry as in other industries. But is that really the right way to view art and science? And if not, as a retailer, how should you think about it? It turns out, both your fuel pricing teams and retail network planners can harness the power of both to grow fuel retail — by finding a balance.

Growing fuel retail success with art and science

A couple of decades ago, using mathematical algorithms to predict prices or sales — in other words, the so-called “science” — was viewed, largely, as a black-box effort and a daunting proposition by fuel retailers. In those days, fuel retailers came from a space where their future success was drawn from their experience, their intelligence and their past successes. Organizations were not likely to consider whether their efforts were art or science, so it’s difficult to say whether these retailers might champion one over the other. But one thing is for certain: emotion was involved in decision making — without an easy way to back up said emotion with data.

Then, things changed. Suddenly, data boomed. You could see everything! You could learn so much! This led most organizations down a new path, where experience and emotion needed verification to yield validity. Today, as machine learning and artificial intelligence blanket our business intelligence worlds in new knowledge and capabilities, the “black box” of pricing and planning may seem to be growing larger. But we actually feel it’s being stripped away.

Instead of a cold analysis backed by nothing but numbers, data analysis via machine learning is quite dependent on experience and gut feel. The two ideas are so intermingled that if you attempt to “do” pricing and planning with only your emotions, you would lose data, and if you attempt to “do” pricing and planning with only your data, you lose the valuable realities of experience.

Specifically, though Artificial Intelligence might be far more capable at computing than your strategy team, strategy is ultimately an art. Every scientific effort requires creativity, and every creative effort features some semblance of human data processing and output, even at the lowest level. With regard to fuel, you must tell the algorithm what you’re looking for. You must know which inputs need to be considered to create clarity of output. You must think, in other words, and not fall into a trap of blindly trusting all data and all “black-box” processes. In the decision-making process, there is strategy to be created — both before you begin to analyze, and after your analysis is complete.

Of course, not all retailers will apply information in the same fashion. Not everyone makes the same decisions, even with the same data in hand. Everyone will “craft” a strategy in a different manner. In pricing, this element of artful strategy allows you to lean on science and data to save time; you can choose to step in only when you need to manage by exception. Combining art and science, you can automate the simple, frequent price change decisions and then highlight anomalies or new plays, giving you the space and freedom to think more deeply about those larger strategic shifts towards more volume and retail success.

In planning, you can gain a scientific picture of where you strengths and weaknesses lie, and of your future potential. You can discover, numerically, where success might be available, via data. But being able to reposition a strategic offering, in cases where the data reveals it is necessary, comes from the years of experience as network planners your team possesses.

As you can see, a healthy balance of the classically dichotomized “art” and “science” is a better path to fuel retail success than depending on just one or the other. This balance may shift, tipping the scale in favor of one vs. the other, depending on the situation at hand. But there should always remain a connection between the two. Science and data should be seen as decision support systems, not replacements for creative, strategic thinking. Yes, they make life easier, provide validation and allow you to reach your goals more quickly. But creativity, built on experience and emotion, will always have value. Without it, how would your offering remain unique? How would you change, and grow?

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