Restaurant Pricing: A Case Study
A leading casual dining restaurant chain optimized their pricing by menu item and by restaurant.
- Some executives were concerned that price increases would dramatically impact guest counts, while others were confident that brand strength could support the increases.
- Using the APT approach, the restaurant chain determined the attributable impact of pricing moves by menu item and by restaurant.
- Future pricing moves were recommended by item and by restaurant, resulting in over $20M in annual profit.
The Company
A top-tier national casual dining restaurant brand
The Challenge
A top-tier national casual dining restaurant brand had tested price increases in some locations, but there was significant internal debate about the effect on guest count. Was the guest count decline more than offset by gross margin percentage increases? The client also worried about guests trading down into lower pricing items as prices increased. Finally, the client understood that the increase was likely to work better in some restaurants than in others but was unsure of which restaurant characteristics drive success. The client turned to APT's Test & Learn™ solution to identify the go-forward pricing strategy.
The Solution
The client started by understanding the overall impact of the pricing move. To net out the wide range of competitive, market, and consumer activities not related to the pricing move, the client employed APT’s best practice methodologies for matching an optimal control group to each test restaurant. Once they saw through the noise, the signal was clear – guest count moved down a bit but margins were way up. The effect was sustained, with incremental profits enduring for months after the price increase.
Diving to a menu item level, the client was able to understand the effects of price on each item. For example, one type of salad held up well to the increase, whereas another was ordered far less often. Certain entrées were particularly susceptible to “trade down,” with guests replacing the higher-priced items with sandwiches or just an appetizer. The effect of crossing price point thresholds was also clear, generally driving prices towards “.99” price points.
Client executives had long speculated about what caused certain restaurants to hold up to price increases better than others. Was it higher income, low competition, high comp rates, high guest satisfaction, presence of certain traffic drivers, or something else entirely? APT enabled the client to check all of these hypotheses at once and to separate the real drivers from the myths. Using this knowledge, the client could target future price increases to the most receptive environments.
Finally, the client realized that a dynamic approach to pricing was required going forward. As commodity costs, macroeconomic conditions, and competition change, the best answer on price today becomes sub-optimal tomorrow. They set up an ongoing price testing program, both successfully validating the initial recommendations and enabling them to regularly adapt over time with each menu cycle.
The Results
By using the Test & Learn™ approach to target price moves by item and by location, the restaurant brand realized over $20M in incremental pretax profit impact in the first year, a figure validated by the ongoing testing program.
