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Are We Falling Into the Cart Before the Horse Trap???

The topic of dynamic pricing in the restaurant industry has been tossed around a lot lately, but exactly what is it? Some companies talk about it being price changes a few times a year, while others propose continual price changes. While there are certainly plenty of mathematical models that can be used to develop optimal menu prices, the math is actually the easy part! The key questions to address before embarking on an analytical pricing program are (1) how customers will react and (2) how these prices will be implemented and communicated.

Without this, I fear that we may be falling into the proverbial cart before the horse trap.

Let’s talk first about customer reaction. Based on previous research, consumer reaction to truly dynamic pricing (as in demand-based) is quite negative unless associated with rate fences (conditions) considered to be fair and reasonable. Pure demand-based pricing has been shown to be associated with price gouging and is often viewed as extremely unfair. And, based on Kahneman, Knetsch and Thaler’s work, if customers view a company’s pricing policy as unfair, they are unlikely to patronize that company again in the future.


This is not to say that dynamic pricing is a bad thing, but unless you carefully plan how your prices will be presented to customers, you may be asking for trouble.

Let’s turn to line-level operators and managers. In my experience, they are likely to be quite resistant to adopting dynamic pricing policies since (a) they’re worked about customer backlash and (b) they’re worried about how their competition will react.

And, to add to this resistance are the implementation issues that the operators may face. For example, can their POS system handle dynamic pricing? Are they able to make price changes in their third-party delivery platforms? How do they communicate not only the rationale for these price changes to their employees, but also how will they communicate these to their customers?

One other source of possible resistance may be a lack of understanding of some of the mathematical models used to develop the dynamic pricing. More pragmatic and intuitive models that avoid unnecessary complexity would go a long way to addressing this issue.

Again, I’m not at all opposed to dynamic pricing (hey, I’m a revenue management person!), but pricing is much more than just a math problem to be solved by the latest data science algorithms. My key message is that it is essential to address customer and implementation concerns BEFORE embarking on a dynamic pricing strategy. If you don’t, you risk falling into the cart before the horse dilemma and not meet with much success.

I welcome your comments!

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