Stop me if you have heard this one before. Your marketing team approaches you wanting to know how they can identify the best set of features for their new product offering that launches in six months. Naturally, they have come to the insights team to help them solve this rather challenging problem. The product will launch in three countries and they need to narrow down the potential list of features as a first step. What do you do?

The first thing that may come to mind is to conduct a survey asking people to rate each feature on a scale from ‘least appealing’ to ‘most appealing’. Scales are easy for people to understand and you will be able to see which features are rated highly more often. However, the problems with this approach are:

  • Many features having similar ratings for ‘top box’ or ‘top two box’ ratings. This may leave you just as confused as before on what is important.
  • Given that you will be talking to people from different countries, you may experience scale bias (i.e. people in certain cultures responding more favorably).

No problem! You can have people rank each of the features. The ranking exercise will force people to make trade-offs and cultural differences will not show up in a ranking exercise. Although this is true, having people rank many items (10, 20, 30 items) can be a daunting task. It is also difficult to know how much more people may like the top item from the item ranked as second, third, fourth, and so on.


There is an approach that addresses all of these concerns – Maximum Difference Scaling.

Maximum Difference Scaling, or MaxDiff, is a technique that provides a relative importance rating for each item in the list, free of scale bias, and drives differentiation in making trade-offs. MaxDiff can work with lists of 10 to 30 features in a way that does not make the exercise for respondents difficult. The results can also give you an idea of the degree of importance. Instead of just saying that Feature A is favored over Feature B, we can provide a sense of magnitude as well.

In a typical MaxDiff exercise, respondents are shown multiple screens of three to five items. They will often be asked to rate the item they like ‘best’ and the item they like ‘worst’. An experimental design sits behind the scenes, and versioning is distributed in such a way to create a balanced design. Once all respondents have all gone through the exercise, we can determine their share of preference for all features shown.

This technique is also adaptable to different scenarios. An example would be to ask consumers what is important when making purchasing decisions in a given category. They would select the items that are ‘most important’ (best) and ‘least important’ (worst). As long as you have a ‘best/worst’ choice for the respondent, the anchored scale is adaptable.

Not only can MaxDiff scores (often called utilities) be used to provide a measure of relative importance, they can be brought into other techniques like TURF and segmentation. TURF (total unduplicated reach and frequency) can tell you which collection of features is likely to cover the most of your target audience. This can be helpful when you need to design a line-up of products with broad appeal. Segmentation approaches can also be used on the utilities to identify sub-groups of consumers who may have different preferences or tastes.

MaxDiff is not a silver-bullet solution. There are some situations where other approaches, like conjoint, may be better suited. Here are a few questions you can ask yourself to determine if MaxDiff is the right approach for your business problem:

  • Do you have a list of individual items or features where you need to understand the relative importance among a set of consumers?
  • Do you have large set of features (10 to 30) that you need to evaluate?
  • Can the items or features be expressed in a relatively short, pithy way that consumers can understand?

If you answered, ‘yes’, to all of the above questions then MaxDiff may be the right technique for your next study. Max Diff can be a powerful tool to help you identify the relative importance of key features while accounting for cultural differences. ENGINE has the expertise to design build the best design solution to answer your most pressing business questions.

Written by Kyle Swan, Senior Research Director, Data & Analytics at ENGINE Insights.


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