The worst drought in decades has affected 88 percent of America’s corn crop, sending this staple commodity to an all-time high. The skyrocketing price of corn will have a direct impact on restaurants, as input costs increase for core menu items, from soft drinks to proteins. Though these cost increases may be an uncontrollable reality of doing business, a margin decrease doesn’t have to be.
To remain profitable in the face of escalating costs, executives have considered many approaches to making menu and pricing decisions. These decisions are often based on focus groups, guest feedback, correlative analysis, or simply using business intuition. Unfortunately, these methods generally lead to inaccurate answers. To de-risk these major decisions, the only accurate approach is real-world testing.
The good news for analytically minded restaurant executives is that all restaurants will be affected by rising commodity costs. Therefore, restaurants that leverage their Big Data to drive testing to inform smarter decisions will differentiate themselves from the competition. Before futures contracts expire, restaurant executives have an opportunity to leverage a test and learn process to determine the true cause-and-effect impact of any upcoming decision. Specifically, there are three key strategies executives should consider testing to offset cost increases: change prices, introduce new menu items, and remove high-cost items.
Changing Prices
One clear way to mitigate margin pressure is to raise prices. However, given stagnating economic growth, a menu-wide price increase in this environment is at best uncertain and at worst devastating to guest visits.
The good news for executives is that there is most likely an opportunity to increase prices for some menu items and in some markets. The best way to figure out where to increase prices is to conduct a multi-cell, in-market scientific test. For example, to offset rising protein costs, a restaurant could run a test such as this:
- Cell A: Increase prices on chicken items
- Cell B: Increase prices on steak items
- Cell C: Increase prices on steak and chicken items
- Cell D: No changes (Control Group)
In this example, a restaurant may learn that increasing the price of chicken (Cell A) drives guests out of chicken but into steak and has a net negative impact on total profits. On the other hand, the restaurant may learn that increasing the price of steak (Cell B) is more profitable because those guests are less price-sensitive. Additionally, the restaurant may find out that increasing the price of both chicken and steak is a bad idea in all locations, as this may cause loyal guests to reduce frequency of visits. This type of test-and-learn analysis has created millions in incremental value for restaurants in the past.
Introducing New Items
Another strategy to improving margin is to introduce new, lower-relative-food-cost options. For example, if feed costs are driving up beef and poultry prices relative to fish prices, a restaurant may consider adding seafood items to the menu. These menu additions could be accompanied by marketing initiatives that focus on the margin-preserving items, hopefully driving more seafood checks.
However, rolling out new menu items without first testing them has significant risk. By launching new items in some locations before others, executives can answer two questions. First: What is the total restaurant margin impact? Second: How can we accurately forecast demand for each menu item by location to better inform purchase decisions?
Test and learn analysis not only yields accurate answers about the impact of the new menu items in aggregate, but will also allow executives to understand how the impact varies by location. Oftentimes, restaurants will find that a new menu item or limited-time offer works well in some restaurants or markets, but not in others.
To use the above example, it is possible that in some locations, say those with a more affluent customer base, more guests will trade out of chicken into fish. By clearly and rigorously identifying this trading behavior, restaurant executives can understand the total economic impact of the new item, net of new transactions, halo, and trading behavior. They can then accurately forecast demand by item and by location. In some cases, they can even target the different LTOs to different markets to maximize sales and profit.
Menu Rationalization
The last strategy executives should consider to ease margin pressure is to simply remove high-cost items from the menu. However, executives should be sure to make data-driven decisions to mitigate the risk associated with menu rationalization. Specifically, restaurants should only remove high-cost/low-margin items that are associated with small guest checks. For example, if a $7 steak sandwich usually is included in large orders, removing that sandwich could potentially result in a loss of the whole order in the future.
Executives should also consider frequency of purchase in a more sophisticated way than traditional mix analysis. For example, if a value-menu hamburger is a rationalization candidate and attaches to small transactions but is ordered in 5 percent of transactions, it would seem very risky to simply eliminate this item across the network. Still, one cannot know the impact of the removal without actually trying it with real customers. Specifically, executives should strive to understand if guests shift up to higher-margin items or if removing the burger actually erodes guest visits, driving down total restaurant profitability.
Although outcomes of any changes are unknown until tested, one fact is certain: the drought will impact the restaurant business. The good news is that all restaurants are facing these challenges, presenting an opportunity for competitive differentiation for those that handle it best. Testing potential alternatives now is the only way to make sure your profits continue to rain down in the future.
Jonathan Marek, senior vice president at Applied Predictive Technologies (APT), leads engagements with casual dining, quick service restaurants, specialty retail, big box retail, and banking clients.
