Advanced MaxDiff HB TURF Analysis

TURF stands for Total Unduplicated Reach and Frequency Analysis. The main objective is to provide detailed statistics on how having multiple items "enabled" at the same time affects their total overall appeal. The appeal in many cases is used as a proxy for the market share, thus enabling various conclusions to be drawn on how certain set of items/features/products/descriptions would perform in a real market environment.

Example study questions could be:

• What will be the market share that can be obtained if we launch product X to a market with competitor products already in it? 
•  Where should we promote this product to maximize our reach in the industry? 
•  In how many distribution channels would a respondent be exposed to the product, on average?

Once the Maxdiff HB analysis is complete, the individual utility scores for the items become available. TURF makes use of the coefficients to produce output. In order to convert the utility scores into "reached" and "not reached" statuses for a given combination of items for a given respondent, aytm employs five different industry-standard approaches:

• Average-based PL (50%): “reached” if the respondent shows a preference for an item over an average one, at a likelihood of at least the set threshold.
• Preference Likelihood x/screen at y%: where x is the number of items per screen programmed in the question; “reached” if the respondent shows a preference for an item over x-1 others, at a likelihood of at least the set threshold (y%).
• Weighted by Probability: “reached” if the respondent shows a preference for an item over an average one, with results weighted by purchase likelihood.
• Weighted by Probability x/screen: where x is the number of items per screen programmed in the question; “reached” if the respondent shows a preference for an item over x-1 others, with results weighted by purchase likelihood.

The recommended approach is Average-based PL (50%) at 70%. If you are new to the aytm TURF simulator, we recommend starting the exploratory analysis with the Average-based PL (50%) at 70% method—here’s why:

Since the goal with TURF is to optimize a set of offerings in terms of their appeal or importance, you want to set a threshold above which you can reasonably assume an items is appealing or would effectively drive purchase intent.

Since by definition of the Average-based PL (50%) metric, a score of 50% means an item performed on par with the average, choosing a threshold below or even slightly above 50% likely allows too much leeway in defining what is “appealing” or “important,” whereas choosing an 80% preference likelihood threshold might feel too exclusive or restrictive. Selecting a threshold of 70% is considered a “sweet spot” that balances potential reach with exclusivity. If ultimately, a 70% threshold yields an unrealistically high reach, you can refine reach by selecting a threshold of 80%, since raising the threshold may give you a more realistic representation of reach. Likewise, if a 70% threshold yields an unrealistically low reach, you can refine your reach by selecting a threshold of 60%