Designing the Optimal Menu: A Restaurant Case Study
A leading restaurant brand
Management planned to update its menu to focus on items proven to build guest loyalty. They wanted to leverage their incredible amount of stored data in order to target the most appropriate items, but had no established method of doing so.
Management chose APT’s Menu Analyzer to create a detailed report of the performance of each item. Menu Analyzer drew upon detailed check-level data, guest information, and guest satisfaction surveys in order to provide the frequency of purchase, margin contribution, “rest of check” sales, and guest loyalty of each item. With Menu Analyzer, management could easily compare item-level statistics according to guest segment, which helped them see how different customers responded to each item.
Management directed the culinary team to highlight the highest-value menu items, remove those of lowest value, and create new items that fit the high-value profile. In addition, the data from Menu Analyzer helped direct the marketing team to target the items with strongest guest loyalty in new advertising and promotions
A leading restaurant chain reinvented their menu.
Over time, the client's menu had grown larger and more complex – mirroring the category in general.
Past menu item changes had been time-consuming, expensive, and error prone.
For an upcoming menu change, APT's Menu Analyzer was employed to evaluate which items should be promoted, improved, or removed.
A leading restaurant brand
Having relied on less comprehensive approaches to menu changes in the past, a leading restaurant chain wanted to bring a guest-oriented, data-driven perspective to this critical process. The goal was clear: simplify the menu by focusing on items that build guest loyalty. The challenge was making sense of the wide range of available data, including detailed check-level data, guest information/segmentation, and guest satisfaction surveys to make specific decisions about each item.
Using APT's Menu Analyzer, the client created a complete view of each menu item. For each item, this included data such as:
Frequency of purchase
Margin contribution of the item
Margin contribution of the rest of the check, for checks where the items were present (and composition of the "rest of check")
Loyalty of guests to that item (compared to other menu items)
Likelihood of guests ordering the item to return
Guest satisfaction with the food
Going one step further, the client also compiled the same set of statistics for different segments of guests. As with many consumer companies, a relatively small number of guests contribute a disproportionate number of visits. Recognizing this fact as well as the desire to grow specific new segments of customers, the client examined the same set of facts for checks of:
High vs. low guest frequency
Larger parties vs. smaller parties
Day-part segments (i.e. lunch vs. dinner)
Week-part segments (i.e. weekday vs. weekend)
With these menu-item data in hand, the decision process was rigorous but efficient. Culinary decision makers were able to determine where to remove, re-formulate, and innovate. The menu was rewritten to focus on items with the highest total value. Finally, marketing was able to focus promotions and media on items likely to create more loyal guests over time.