DOES REACH REALLY MEAN SALES?

Not necessarily. This question is best addressed in your survey design. We recommend that you approach the design of your stack rank exercise by first understanding what questions you are really trying to answer. If you ask your customers to rank items from best to worst, you will get a stack rank of the items’ relative importance. However, you will not learn if they are likely to purchase any of the items.

To help solve for this challenge you should consider options like anchoring your stack rank in a behavioral outcome so that you not only understand the relative order of your items, but the which items actually resonate. A good design will give you comfort you can answer questions like, “Does reach mean sales?”.

ARE WE MAXIMIZING THE BOTTOM LINE?

Again, not necessarily. To assess the impact on your bottom-line you will have to include things like Revenue and Cost-of-Goods-Sold (COGS) into your analysis. Imagine Item A increases your reach by 15pts whereas Item B increases your reach by 10pts. It might seem Item A is the way to go. But, if Item B cost half as much as Item A, it may contribute more to the bottom line.

Another potential leverage a constrained TURF analysis is identifying which features to build as a part of a near term product roadmap. Let us say you are planning to release a new version of your app in 6 months and you are considering 20 possible new features, but you can’t build all of them. A traditional TURF analysis will give you the order in which you should develop your features based on which feature gets you the next most reach. But, if you understand that you have 10,000 hours of developer time and some features take 1,000 hours to develop whereas others may take 4,000 hours, you can constrain your analysis to identify the set of features that maximize your reach will keeping you at or under 10,000 total hours of development.

By running your TURF analysis with these constraints in place, you can connect the granular insights from your customer preferences with the broader context of your product roadmap and business strategy to make the best strategic choices.

DOES OUR TURF SOLUTION ALIGN WITH OUR BRAND/OTHER OFFERINGS?

TURF does not directly answer this question, but if you collect the right data in your survey you can assess each items alignment with your brand position. Those assessments can also be included in your TURF analysis to ensure you get the mix you are looking for.

For example, to build on our prior restaurant example, imagine your brand image is “Healthy and Fun”. You will likely want to maximize reach while constraining the final list to options that get a nice mix of items that also rank as “healthy” and “fun”. This may mean dropping some highly rated “fun” items that are not as “healthy”.

CAN WE DO BETTER THAN TRADITIONAL TURF?

Yes. But it requires a) capturing data that will allow you to project your reach onto behavioral outcomes, b) bring real-world constraints into your analysis and c) identifying and pruning your TURF analysis within the context of your brand and larger set of offerings.

Following those steps can significantly improve the quality of the analysis and the reliability of the answers it provides. It can ground your TURF analysis in reality.