I am currently working with another principal consultant on a project that focuses on customer lifetime value, and as part of the research and preparation for the project, I have been spending time reading materials in an attempt to qualify what is meant by the term “customer lifetime value” and more interestingly, how it can be computed. As part of that research, I just spent a chunk of time reading through a company’s annual report to understand their key performance measures and examine how those measures relate to an individual customer.
That annual report (for a restaurant chain company, by the way, that has a retail shop within selling items associated with the restaurant’s core themes) provided some key measures of performance (the numbering is mine):
- Comparable store restaurant sales and calculated number of guests for stores open at least 6 quarters
- Percentage of retail item sales to total sales (proportion of amounts spent on items to amounts spent on food, basically)
- Average check per person
- Operating margin, consisting of total revenue minus cost of goods sold, labor, other expenses
I though it was very interesting to compare these measures in the context of each individual restaurant patron. The first measures are gross-level measures (total sales per store, or total number guests). Dividing one by the other provides a gross-level ration that could be attributed to a conceptual single patron, namely average sales per guest. The fact that this is an aggregate value is similar to the third measure, average check per guest (focusing solely on the food, not retail order). Similarly, the second (a percentage) and the fourth (one total subtracted from another total) are both aggregates as well.
Apparently, these high-level aggregates are easy to calculate because most of the totals can stream directly out of the methods of accounting. At the end of the day, you can get the actual register totals for each store, and the sum is the total revenue – that is factual. You can also have someone count individuals as they enter the store, and that is also factual. So the quality of the “big results” should be trustworthy. But what about drilling down into the details, such as “actual spend by customer”? More on this question about “little results” next post.
the Data Roundtable.