Total Unduplicated Reach and Frequency
An ice creamery in my neighborhood recently announced its plans to celebrate its new freezer by introducing three new flavors and invited its social media followers to vote for the additions. Hooray for us all, right? But I cringed, fearing a simple poll among existing customers – fans! – would not lead to more customers, and in fact could cannibalize existing sales if a customer simply swaps out an old favorite with a new one. A poll among residents within the creamery’s trade area is a slightly better option, because it includes potential new customers, but that will not ensure that higher awareness translates into higher volume nor attribute additional sales to the new flavors.
What Is Total Unduplicated Reach and Frequency?
I urged the ice creamery to defend its turf. T-U-R-F, or Total Unduplicated Reach and Frequency, is a stronger approach to luring new customers with new ice cream flavors while encouraging existing customers to notice the new flavors.
Here’s How Total Unduplicated Reach and Frequency Works:
We ask a sample of the market (not just current customers) what flavors they would buy. The list should include both the existing flavors and the new flavors being considered (MaxDiff is best, but we can work with multi-select check boxes too). TURF defines thresholds for each person as to whether they would buy each flavor of ice cream. The analysis then runs through all possible subsets of flavors. We want to reach additional people whom we are not already reaching through our existing flavor selection (i.e., what set of three new flavors will reach the largest number of additional people?). We force all of the current flavors into the analysis first to see who is being reached. Then we test all possible subsets of three from the new flavor list and find the optimal combination(s).
Reach is the number of respondents who expressed a preference for at least one of the items in a combination. If at least one of the flavors above your purchase threshold is in a particular subset of products, we consider you “reached.” Unduplicated means we try to reach as many unique individuals as possible. In other words, once the analysis has reached you by adding a flavor you would buy, it seeks to identify flavors that will reach other people. For example, suppose the first five flavors identified by TURF are chocolate, vanilla, strawberry, mint chocolate chip, and chocolate chip cookie dough. All of the people who reported liking chocolate chunk also like at least one of the five listed above, so adding that flavor does not bring in any additional customers. While it might be a smaller set of people who chose the non-dairy chocolate, these people are not currently being reached by the previous five flavors (unduplicated reach).
Frequency is the total number of times the features in each combination were preferred. In our example, this would be number of flavors in each flavor set that people indicated they like. Assume our final flavor set is chocolate, vanilla, strawberry, mint chocolate chip, and chocolate chip cookie dough, and non-dairy chocolate. If Mary likes both vanilla and chocolate chip cookie dough, her frequency would be two. If Juan likes only non-dairy chocolate, his reach frequency would be one. In our example, people are likely to only get one ice cream cone during each visit, so frequency is not as relevant as it would be for something like a fast-food restaurant where each customer is likely to pick multiple items per visit, such as a burger and fries.
By conducting a quantitative market research survey and executing a TURF analysis, the ice creamery identified the flavor list that would keep its existing customers happy while attracting the greatest number of new customers. Everyone is enjoying the new flavors, and the owners are excited to see so many new faces in their shop.
Interested in learning more about TURF? Reach out at firstname.lastname@example.org.