One of the most significant challenges owners and managers of fuel stations face is setting the correct fuel price daily–or even updating the price throughout the day. Selecting the right price to get the most margin involves knowing the wholesale cost of the fuel and the prices currently being charged by the location’s immediate competitors. There’s not much room to play with, in 2018, NACS estimated the average margin on a gallon of gas to be 23.8 cents or 8.7% of the fuel’s price.
In the past, fuel station managers relied heavily on “drive arounds” to visually track the price their competitors were charging and then adjust their prices accordingly. Since then, websites collect information from consumers purchasing fuel to provide pricing information for customers and businesses alike.
Modern software vendors take price optimization a step further. Today’s solutions include descriptions of “AI (artificial intelligence) ability to optimize your pricing strategies.” What is this technology, and do you really need artificial intelligence to develop pricing strategies?
Do you need artificial intelligence to optimize your pricing strategies?
For some of us, the term “artificial intelligence” brings back memories of Steven Spielberg’s 2001 film, “A.I.” where a robot son is trying to become more human to gain the love of his mother. For decades, people interested in technology have wondered whether artificial intelligence might replace humans in jobs like the defense industry. Visitors can explore the possibilities of AI during a visit to a hotel in Japan that is almost entirely operated by robots. If you want to take a short, entertaining break from your workday to view the possibilities of AI for yourself, see a few examples interviewed by Jimmy Fallon.
Artificial intelligence presents many opportunities for the fuel and petroleum industry to increase production efficiency and create safer handling of hazardous spills and fires. However, it’s not likely something that managers will use for pricing strategies–at least not anytime soon. Terms such as data analytics, machine learning, and AI are used interchangeably; however, they are three different things.
Data analytics helps business leaders understand all the data they have available to them. Traditionally, managers relied on data hand-entered into spreadsheets and simple charts and graphs. Today’s solutions make collecting and reporting data easier than ever before.
Data analytics includes the processes for collecting data, performing statistical analysis of the information, and reporting the results as tables, graphs, charts, infographics, or other discernable formats. Often, businesses will hire data analysts to manage the data analytics function of the company and translate the results into narrative descriptions of trends and predictions of what may likely happen in the future.
Machine learning expands on the concepts of data analytics. Software that features machine learning technology processes all the information through data analytics to learn and share meaningful insights about the business. In other words, machine learning transforms data into actionable insights in much the same way that data analysts do. Machine learning can identify patterns and trends and then create predictive forecasts. Each time the software receives new data, it compares it to past predictions and updates its learning about the information. The software gets more intelligent and makes better predictions as it gets more information.
An example of machine learning that many people use daily is Facebook. Facebook operates with a complex set of algorithms that analyzes user behavior online to make predictions about which posts you want to see–whether it’s the cute picture of your nephew’s first day of school or an advertisement for laundry detergent. The more you use Facebook, the better those predictions become.
Artificial intelligence is a broad category of “smart” technologies. AI includes machine learning as a subset within its scope, but there’s usually one significant difference between them. Whereas machine learning relies upon receiving new data to create new insights, AI actively seeks new information constantly to evolve its learning.
AI technology solves significant, complex problems (i.e., interacting with guests checking in to a hotel), while machine learning is focused on understanding problems and giving accurate results in a predetermined area (i.e., how much fuel consumers will need over the next six months and what will they pay for it.) Not all artificial intelligence solutions are robots; for example, AI technology is used in many industries to respond to customer service inquiries. Customers might never know they had a complete conversation with a computer. AI uses learning, reasoning, and self-correction processes, while machine learning uses a cyclical routine of learning, reporting, receiving new data, and learning again.
Don’t let software promises overwhelm your selection process.
At WEnd Consulting, we don’t use big, fancy terms to sell our products and services. Instead, we’re focused on finding the right-sized solution for each of our client’s needs. We can help you wade through the promotional promises and dog-and-pony shows of software vendors to figure out exactly what each solution offers and help you make the best decisions for your business. We’ve spent decades in the industry and know the ins and outs of major players in the software industry. Contact us today to learn more about how we can help you transform your business to achieve your strategic goals.