There is no easy way to say this, so I’m just going to come out and say it. You’ve been mis-led by some mis-information that's been mis-communicated. You're mis-sing your "F". The fact is, your TURF studies are only giving you Total Unduplicated Reach.
You are doing TUR studies.
True TURF is a measure borrowed from advertising, allowing media buyers to determine the best place to purchase space. And, in this context, TURF demonstrates how to maximize unduplicated reach (i.e. putting one ad in Sports Illustrated and one ad in People will likely reach unique consumers) and how to maximize frequency (i.e. putting one ad in Sports Illustrated and one ad in ESPN the Magazine may reach the same consumer, but you have now hit that consumer twice).
When used in market research, the “F” in TURF is not “frequency” in the manner that you think it is because reaching people multiple times does not have the same impact. In other words, just because a consumer is “reached” by both the chocolate and strawberry flavors (as an example) does not mean they are going to buy both flavors when they shop. They are more likely to buy one or the other. So, that “frequency” measure is NOT reflective of the number of purchases.
Fortunately, there are techniques that can complement traditional TURF (or, as you now know, TUR), or even prove more beneficial.
Where you are in the development cycle dictates the optimal approach. If you are early in the cycle and have a broad list of items to test, a modified TUR (M-TUR) approach works well. One success factor is putting the items in the context of a shelf set, or a menu. The key is representing the competitive set and offering choices that consumers are faced with in real life. This approach results in a stronger measure of number of purchases. That, combined with questions on intended frequency of store or restaurant visits, adds a layer of richness to simple reach measures.
Another approach is what we call redundancy analysis. With this analysis, rather than looking for unduplicated reach, we look for items that fulfill the same needs/reach the same consumers. Then, once items are clustered together, key measures can prioritize which items should remain and which may be deleted.
If you are later in the development cycle, and have a small list of test items or solution sets, choice analysis can provide strong measures of performance – including reach and frequency (the definition of frequency you thought you were getting before!).
Borrowing TURF from advertising provided great benefit to reserach on product lines. However, not all the assumptions of the model are applicable. Using modified TURF, choice analysis or redundancy analysis takes TURF where you need to go and provides direction on your mis-sion.
For more information about how you can take advantage of the benefits of using a modified TURF methodology, call or e-mail Brad Barash (800.800.2124 x216).
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