In a recent strategy shift,  Quiznos has announced that it is dropping its value priced items and focusing its brand on more expensive offerings. This shift towards “tastier foods,” such as prime rib sandwiches, may help to differentiate Quiznos from competitors in the ever-increasing sandwich space, especially as more concepts try to position away from yet another value offer. Still, success will depend on guest response.

Will the sandwich chain trade existing value-conscious customers up to quality offers? Or could Quiznos lose some value-conscious guests? Will it win incremental guests at the other end of the spectrum? As companies like Quiznos change menu pricing and value offerings, it is critical to accurately understand causal relationships between price changes and guest count, sales, and profits.

When restaurant companies institute chain-wide pricing and menu changes, they are easily able to track how their restaurants are trending year-over-year, but they may find it much more difficult to pinpoint the true root cause of these sales trends. For example, are comps, positive or negative, caused by the new strategy? Better weather? Or are they simply a side-effect of competitive activities?

In order to accurately understand the impact of a pricing change and to predict the price point where higher margins offset a decline in units, restaurants must begin by testing price changes. In well-designed tests, chains can scientifically determine whether higher prices will lead to shifts toward cheaper items and decreased guest count. By understanding the full economics of price changes in advance, restaurants can mitigate the risk of profit declines and improve chances of a successful rollout plan.

Additionally, accurate reads can answer other important questions: To which menu items did guest demand move? What happened to items on the same transaction as the item that saw its price increased? How do these impacts change over time, through multiple purchase cycles? And importantly, how does impact vary by location, so that restaurant pricing can be appropriately tiered and targeted by location?

For example, during a price increase test, one casual dining restaurant found that higher-priced meals were being replaced with less expensive sandwiches and appetizers, but specific salads saw negligible unit decline. It also found that restaurants with certain predictable characteristics performed much better than others with increased prices. Using these learnings, the chain was able to identify a subset of restaurants where guests responded best and tailored its initiative to target price moves to locations and items with low price sensitivity.

Applying price changes to the right restaurants and menu items can make or break a price change program. To read more about how Test & Learn helped a leading national restaurant brand increase profit by $20M annually, please visit our pricing case study.