RMS was recently contacted by a major quick-casual restaurant company. The company had raised menu prices by more than 12% over the previous two years resulting in an 11% decline in traffic and virtually no change in check averages as consumers traded down to lower priced items. Senior management was left struggling to identify the best recovery strategy and looked to RMS for identification of item level price recommendations by location and an estimate of the expected profits for this new strategy.

Approach:

RMS conducted a complete analysis of the company’s unit sales and financial performance using data from the previous two years. Using its proprietary statistical methodology, RMS then identified that the majority of price sensitive stores still had opportunities to improve profitability either through price reductions or through promotions on the more sensitive items to generate traffic. RMS also highlighted which items traded with each other and determined which item prices should be increased or decreased in order to maximize potential profit. For example, if RMS identified that a low margin, highly price sensitive piece of pizza traded with a more profitable but also price sensitive chicken sandwich, then RMS would recommend increasing the price of the pizza and lowering the price of the chicken sandwich to shift demand towards the more profitable chicken sandwich.

Results:

For the highly sensitive stores, this strategy translated into a slight reduction in the actual menu board price but a large increase in profits as a result of having a higher margin per transaction and an increase in traffic. RMS’ strategically formulated pricing recommendations yielded a net improvement in gross profit equal to just over 1%.

Approach:

Using its proprietary statistical methodology RMS developed restaurant-specific price recommendations that increased gross margin dollars at the unit level by identifying the inherent price opportunity in individual units. Once the opportunity was established through the analysis of restaurant-level POS data, RMS leveraged that same restaurant-level data set to understand and leverage local demand.

Results:

By focusing on local demand, RMS was able to provide price recommendations that provided relief to escalating labor costs without pricing the restaurants out of the market or losing critical transaction counts. While some franchisee restaurants that did not take part in the RMS program were able to offset the labor costs, these restaurants found it necessary to take a more aggressive approach on the menu board in order to yield a similar result. These more aggressive price changes in non-participating restaurants resulted in the erosion of transactions relative to restaurants following the RMS approach.

FastCo hired RMS to provide a plan for a new pricing strategy. The program was to include:

• Identification of groups of restaurants with similar characteristics which would qualify for price changes
• A recommendation of the number of price tiers to implement
• Identification of specific price levels for each tier and item level price recommendations by location
• An estimate of the expected impact on profits for the new strategy
• A proof-of-concept test of the final plan before implementation
• Post change tracking of implemented results during rollout for potential fine tuning of prices

Approach:

RMS first obtained all FastCo’s product mix information for the past two years. This data was cleaned and organized to provide a customer view of pricing. Store level weekly product data was analyzed to provide insights into the reaction of restaurants to past price changes and promotional activity. This analysis was conducted on a store-by-store basis to assess the sensitivity of the location to traffic and gross profit changes associated with pricing changes. The analysis showed a range of customer reactions to price changes from highly sensitive to highly insensitive.

To determine the number and composition of price tiers, RMS evaluated a range of options including geography, demographics and economic conditions associated with the current sites, the ability of FastCo to handle multiple price tiers in marketing communications and operational performance.

The final price tiers of restaurants were developed using geographic proximity and economic conditions.

A total of five tiers were created. This number was a consensus decision based both on internal factors and potential customer reaction. While the goal was to have additional tiers in the future to better optimize prices, this was considered a feasible start. The tiers which would have the highest prices were those with the least sensitive restaurants, and highest CPI. One price tier with poor economic conditions and highly sensitive restaurants took a price decrease.

The price changes for the tiers ranged from an increase of 6.9% to a decrease of 2.0%. One tier’s pricing was held constant to provide a benchmark on the effectiveness of the new strategy.

FastCo and RMS consider a number of different scenarios on price changes. Because FastCo’s final price decision exceeded what RMS felt could be achieved with no impact upon guest counts, the estimated gross profit was adjusted to reflect guest resistance. RMS estimated a weighted average price increase of 4.8% for all locations would generate a 5.9% increase in gross profit.

The price changes were first tested in a small number of locations with minimal customer complaints. Following the test, the full price adjustments were rolled out in phases, again to insure minimal impact on customer resistance.

Results:

Price changes were implemented with minimal customer resistance. Gross profit increased by 5.4% versus the control group for the average restaurant. While the profit impact was slightly less than modeled, FastCo decided to implement a stronger promotional calendar in an effort to offset the predicted loss in guest counts. This decision reduced the effective price increase, but did help reduce the impact on guests as anticipated.

The company assumed that there was a trading relationship between one of their sandwiches and its related combo meal. They wanted to encourage a trade up from the individual sandwich to the meal and assumed that a narrower price difference between the two would help accomplish that objective. Operating under their own constraint to leave the price of the sandwich untouched, the company lowered the price of the combo meal to narrow the price gap. They also simultaneously increased the price of the other more expensive combo meals. The outcome of this test indicated that consumers did not trade up from the sandwich to its combo meal version. The wider gap between the individual sandwich and the rest of the combo meals actually triggered a trade down. This highlights a lost opportunity of approximately $128,000 in gross profit. The company came to RMS for guidance in order to try and correct the pricing error and prevent more financial loss.

Approach:

RMS’ patented modeling of the company’s historical product mix before price change provided highly valuable information that, in hindsight, could have saved the company thousands of dollars. The results of the multiple regression analysis indicated that there was no evidence of a trading relationship between the individual sandwich and the combo version. Analysis also showed that the price decrease for the combo meal was unlikely to result in a trade-up from the individual sandwich. There are strong trades between entry-level combo meals and more expensive, profitable meals. Expanding the price gap (lowering the price of the entry-level meal and increasing the price of the rest of the meals) was found to have a high risk of trade down. To counteract this blunder, RMS recommended that the company revert to the original price point for the entry-level combo meal based on the trade information and the results of the original pricing strategy.

Results:

Reverting back to the original price did not negatively affect the item demand. The resulting incremental increase of gross profit was equal to approximately $240,000. This illustrates that fact-based knowledge on trade relationships between menu items enables highly efficient and profitable pricing decisions.

The company had many unanswered questions:

• What is the impact of national vs. local Gross Rating Points (GRP)?
• What are the best months to increase weeks of National and Local GRPs?
• What are the best months to increase weekly levels of National and Local GRPs?
• What is the optimal flight length? And what is the optimal time gap between flights?
• What is the impact of National Cable, National Prime and National Sport?
• What is the impact of print?

Approach:

RMS’ Media Optimization Analysis linked the company’s media spending decisions to business outcomes. First, RMS collected financial performance data (sales, aggregated contribution margin, traffic per store, per week) from the company as well as promotional information such as GRP levels (National TV, Local TV, National Hispanic TV, Sports, Prime and Cable, 15 and 30 second ads), promotion type and competitor GRPs. RMS also obtained information on macroeconomic indicators (gas prices by state by week, unemployment by state by month, housing data by month, etc.), demographics of those living, working and shopping near each store and the weather. RMS then conducted various multiple regression analyses on the different variables to single out the impact of each characteristic as well as see the effects of combinations of characteristics.

Results:

Based on the results of these analyses, RMS was able to provide the company with insights and recommendations about which types of promotions should be used at which times of year and for what products in order to maximize the impact of its media spend. This includes recommendations for optimal flight lengths and gaps between flights to optimize costly TV advertising. Implementing RMS’ recommendations yielded the company an annual impact of about $5.2 million.