An example:
Imagine you have 7 different varieties of chocolate (e.g. whole milk, dark, nougat, white chocolate etc.) and need to identify those 5 varieties that combined would reach the biggest number of buyers (Unduplicated Reach).
Indeed this set does not necessarily have to comprise of the five varieties that – considered separately – have the highest share of buyers (as measured with Top-2 Boxes in purchase intention, for example), i.e. A+B+C+D+E…
…instead a better set would be made up with A+B+C+D+F, because the TURF-Analysis proves that buyers of B and E are identical customers, for the most part, while variety F reaches other, additional buyers.
The TURF-Analysis delivers the net share of all buyers for each possible set of 5 chocolate varieties. This makes it possible to quickly identify the combination that promises the biggest total number of buyers that can be reached.
In addition to the above data TURF will also determine the ideal number of varieties that should be included in the sales mix in order to achieve maximum reach. Thus it would be possible that 3 or max. 4 varieties in the sales mix would be enough to reach the biggest buyer share and that any additional variety would not reach a worthwhile number of additional buyers.
Moreover, it is possible to calculate scenarios with a smaller or bigger number of varieties, with or without specific varieties (e.g. an already existing product or a competitor product).
Simple and transparent query:
Respondents are asked to give their purchase intention for all researched alternatives. This can be done in a binary mode (would buy/ would not buy) are in scaled mode (“I would buy definitely” to “I would definitely not buy”). The resulting data will be used to determine reach. In order to predict sellable numbers of products respondents will be asked how often or in which quantities they will buy accepted varieties.
Since the question style is very simple it is feasible to use all of the usual survey channels (mobile at the POS or online).
