Be Careful What You Fish For

MaxDiff: Soliciting Consumer Preference through Comparisons and Choices

Jane Tang Monday, May 28, 2012

Poor Dilbert! How should an engineer (or marketer) react when people suggest improvements to their product? The path of least resistance is to simply add each of these features as they are requested (or, at least, those suggestions that are feasible – forget about the holographic user interface). It can take a lot of work to add all of these features, but this approach seems like a no-lose proposition for your customers. Sadly, this is not often true. As new features are added, the product can become more and more difficult to use. Performance suffers, and useful features get lost among a plethora of seldom used options. While you may make some users happy with the new features, there will be a steady trickle of lost customers as a once simple and speedy product becomes bloated. In many products, extra features will also mean extra costs.

The key to success is to prioritize features, using consumer data – obtained from people outside the building. Only those suggestions that truly matter to actual users should be considered for implementation (they may still turn out to be too expensive, risky, or problematic). Features that don’t matter, or don’t matter enough, should generally be omitted. The problem is that this kind of prioritization is easier said than done. The traditional customer-preference rating tools used in market research are blunt instruments. Furthermore, customers have a hard time expressing their real desires. Asked to rate a long list of product attributes on a scale of 1 (“completely unimportant”) to 10 (“extremely important”), survey takers are happy to tell you that all of them are important. Companies need a way to distinguish between features their customer would “like to have” and the features that they truly “must have” or would truly pay more for.

Maximum Difference scaling (MaxDiff) does precisely that. In this technique, a consumer is presented with a short list (often 4 or 5 items) of potential product features. Instead of indicating whether they want each item, the customer is asked to indicate which item they want the most and which item they want least. This task is repeated several times with different lists of features. Not only does this avoid the trap of customers “wanting it all”, it is also easier for the survey taker - picking most/least wanted features is easier than assigning a numerical rating to each feature. This technique is also easier for the researcher because there is no opportunity for scale usage bias (where a rating of 7 out of 10, for example, might mean different things to different people).

Given enough survey takers, and enough most/least wanted comparisons and choices from each participant, the researcher can reliably identify the desirability of each proposed feature relative to all the others. The product developers can then focus on what is truly important to the customers, which is the key to success.