Let’s face it; everyone in the restaurant industry is looking for a leg up on the competition and trying to maximize profits. Business Intelligence (BI) and Analytics tools show that restaurant chains are pushing the envelope by assembling an optimized menu mix using a back office technique called Menu Engineering.
When you do a quick search for “Menu Engineering”, you will find a whole host of consultants vying to help restaurants figure out what should be on an optimized menu. They’ll even suggest how and in what order those products should be displayed to customers. Restaurant owners are quick to see the value in doing this type of analysis, but the first step is always the same: you need the right data to get started.
The question then becomes, what is the right data and how do I get it?
First, let’s focus on what is the “right” data. In order to get meaningful results from your analysis, your data set needs to include the following data:
1. Transaction level detail
First things first, you need to have comprehensive data on the number of sales of each menu item and how much they actually sold for. You also need this information captured for all the restaurants for which you want to run the analysis. Lastly, think about how often you are going to run this analysis and for what date range. If you have one year’s worth of data, then you could not only run a quarterly analysis, but also compare the current quarter’s results against the previous quarter to see how the outcomes may have changed over time.
2. Ingredient Cost
This might sound simple, but the ingredient cost for each menu item can make or break the process and is the most essential aspect of Menu Engineering. If you can’t effectively breakdown what your product costs your restaurant to make, then you have no shot at determining what the profitability is of the sold product. Not only do you really need to understand your ingredient costs, you need to make sure that they are calculated and reported the same way across all your restaurants.
3. Menu Segmentation
Comparing apples to apples (pun intended) is important. Menu segmentation into categories like entrees, appetizers and desserts or subcategories such as breakfast, lunch or dinner will give you the option to compare like items. Certainly, you can analyze the results across all segments, but more value can be captured when comparing one entrée to other entrees on the menu, or one lunch item to other lunch items. Again, much like costing, these categories must be consistent across the entire data set so that the results of the analysis will be useable.
Now we focus on the second part of the question, where do you get the data from?
This is where business intelligence and analytics platforms come in. A typical platform will access a comprehensive data warehouse pre-populated with all the data (and a lot more) mentioned above. This data is accumulated and driven by your enterprise back office solution.
With a BI tool used in connection with a back office solution, a menu mix analysis should only take a few minutes to complete. You will still need to set up how you want to classify your Dogs, Stars, Puzzles and Plow Horses. Simply point it at your data set, pick the date ranges and let the report engine do the work for you. Then, when you want run this analysis the following quarter (or whenever), simply adjust the date range.