Editor’s Note: This is the first in a series on pricing strategies within the quick-service restaurant industry.
Recently, I went on a road trip across the Midwest, and on that trip I consumed more than my fair share of fast food. As I darted between Burger Kings and Taco Bells, I began to notice some of the menus’ nuances: the way LTO items were prominently displayed, design elements like typeface or color, and, most interestingly, the wide variety of prices. For instance, why is Burger King’s large french fry worthy of a $2.39 price tag while McDonalds’ large french fry sells for a cool $2.19? Why does Taco Bell sell a large fountain soda for $1.89 while the Burger King right down the street sells a large fountain soda for 10 cents less?
These small discrepancies in price are as common as apple pie in the foodservice industry, and are often the result of one or two tactics: a cost-plus pricing approach or a value-based approach. Today, cost-plus and value-based pricing are practically tired terms. Both strategies have been around for quite a while and have developed their fair share of shortcomings as the world around them has updated and innovated. One more notable and recent shortcoming of both these models is that neither have a way to take into account the massive amount of “big data” about consumer behavior and foodservice costs that is now available to the industry. Though tried and true, these models are far from sophisticated and are likely causing operators to leave large amounts of money on the table.
To understand the potential benefits of data-enhanced pricing strategies, let’s first look to an e-commerce leader that has already put similar strategies in place: Amazon.
A Business Insider article recently pointed out a few shocking statistics: That Amazon had changed the price of a wireless Internet router eight times in one day and that evidently Amazon alters prices more than 2.5 million times daily. Now if Amazon’s decision-makers were implementing either a cost-plus or value-based approach to pricing, there would be no need for this kind of scrupulous adjusting. But they aren’t. Rather, they have built systems that leverage massive amounts of data to understand more about purchase behavior than any decision maker ever could.
Elements like time of day, region, past individual consumer behavior, past aggregate consumer behavior, and so on are taken into consideration in a split second and affect what the consumer sees and how that product is priced. Obviously, Amazon’s algorithms are an extreme example of a data-enhanced pricing strategy, but it’s important to realize just how sophisticated some pricing strategies have become. Meanwhile, the food industry still depends on strategies that have been around before the invention of the computer.
The good news is that much can be borrowed from Amazon’s strategy and applied to the foodservice industry. In fact, many newer foodservice concepts are already primed to implement more sophisticated strategies. With roots in Silicon Valley and technology in their DNA, food industry start-ups like Sprig and Plated, which deliver healthy pre-made meals to busy diners, are bound to incorporate big-data insights when considering pricing (if they don’t do so already), making them all the more capable of being restaurant replacers and Millennials’ new food provider of choice. This means that, in addition to the typical brick-and-mortar competitor down the street, restaurant operators now have to worry about these start-ups, who have the technological prowess to optimize their prices instantaneously based off data that is no different than the data that is available to every restaurant operator around the country. That’s the other good news. What these technology-inclined companies are doing is not out of reach for any operator, it just requires a few lessons on the nuances of data as it applies to the food industry. That’s where I come in.
Over the next series of articles, we will be exploring a variety of alternative pricing strategies that leverage recently available data and new technology. To make things more interesting, each new approach and its key takeaways will be discussed within the context of a hypothetical restaurant that our team has, developed replete with its own menu items and pricing that is derived from a cost-plus pricing strategy. We will use this restaurant and its menu both as a way to make clear how to implement these strategies and as a vehicle for discussing their real world benefits upon implementation. Menu item pricing will be based off of food costs provided to us from Reinhart Foodservice’s powerful new suite of tools, TRACS Direct. By the end of this series, operators will be ready to implement more effective pricing strategies that allow them to determine quality inputs, analyze the data, optimize prices based off that data, and then do it all over again with a different set of inputs.
In the meantime, let us know what you think we should name our hypothetical restaurant on Twitter using the #menudata. We’ll pick back up in a few weeks with the introduction of our restaurant, its menu, and the first new pricing approach. Until then, class dismissed.
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