Before developing a campaign, it is critically important to know what buying criteria are most important to your consumers. However, when trying to uncover these insights, many clients simply ask respondents to select from a list of attributes or to rate the importance of each attribute on a numerical scale. There are two drawbacks to proceeding in this manner.
- All attributes end up being “important”. Safe, organic, environmentally friendly, visually appealing, cost-effective, pocket-sized…left to their own devices, respondents tend to indicate that many factors are important in their decision-making process. As a result, it becomes difficult to establish a hierarchy and to know which ones are most important to focus on.
- Gap between attributes is unclear. Even if results do demonstrate a hierarchy, this approach does not quantify the gap in importance between the various attributes. Top attributes may be only slightly more important or far-and-away leaders, and knowing the difference is vital when developing a marketing strategy.
To address these drawbacks, a MaxDiff analysis can be used. With this approach, respondents are shown a list of five to seven attributes and are asked to select which one is the most important and which one is the least important. Once they have done so, the list repopulates with a different combination of five to seven attributes, and the respondent answers again. This cycle repeats as many as 10 times.
Once the process is completed, a certain number of “utility points” are assigned to each attribute, the total of which equals 100. With these results, we are able to ascertain which three attributes are the most important, and exactly how large the gap is between every attribute. For example, with this approach, an attribute with a score of 20 is deemed to be twice as important as an attribute with a score of 10. By further manipulating the results, we can also determine what the optimal number of attributes to focus on is, knowing that at a certain point, the marginal increase in utility points will not justify what will inevitably be a more complicated message or advertisement.
The only downside to the MaxDiff analysis is that it requires a few minutes of survey time on its own, so the overall duration of the survey should take this into account.
If consumers’ key buying criteria is a blind spot in your data, the MaxDiff analysis is easy to apply and provides a complete overview to help direct marketing efforts.
To learn more about this approach, or any of the other advanced statistical analyses that we use, please contact Tom Rigby (tom.rigby at callosum.ca).