Benchmarks are a valuable asset for assessing the performance of a program. At PortMA, we have an internal database which we use when benchmarking data for clients. It allows us to look at consumer segments, demographic data, and future purchase and recommend intents. All of this data can be used and combined for comparative ROI, or just performance benchmarks. The important thing when using benchmarks though, is making sure you use the appropriate set of data.
Granularity in your benchmarks is important.
You can be highly restrictive, and make sure to only use the programs most similar to the current program you are analyzing. Alternatively, you can step back a bit, and examine how the product is performing at a more broad category level. This is the major reason we include NAICS and SIC codes in our database. Initially it lets you dive into the data at a very granular level, and slowly back out until you have a view from 10,000 feet. I find one of the best places to do this type of analysis is with things like the liquor industry.
We recently had a project that was looking at performance of a wine. Now we have a solid data comparison for wine. We knew that wine averaged about a 87% purchase intent. We knew more about our clients activations than just the fact that it was a wine, so we started to refine the data. Knowing that the client was focusing on in-store activations, we used that to further limit our data. Interestingly, this had virtually no effect on the purchase intent, which shifted to 88% by limiting it to just the in-store activations.
From there, we decided to step back. We looked at all alcoholic beverages as a category. Interestingly enough, this had a moderately negative effect on purchase intent, dropping to 62%. When we restricted this to just in-store again, we did see it pick back up, with purchase intent reaching 70%.
By looking at all of these segments, we can get an understanding of exactly how the industry works. While focusing on wine gave us the most relevant numbers, by looking at a broader level we were better able to understand a part of the story that was less clear when assessing the wine data solely.