Editor’s note: David Bryant is VP, Ironwood Insights Group, Chandler, Arizona. 

While it may be simple to define the characteristics of an important attribute, estimating its derived importance is another matter unless you have attributable effects analysis in your toolbox. With this probability-based analytical tool, you can identify which attribute(s) offer the most opportunity and/or the most risk while also providing a clear view of how the relationships between these attributes can help improve purchase intent.

Traditionally, measures of importance have been expressed as functions of statistical relationships such as regression coefficients, correlations and/or the amount of variance explained. Attributable effects analysis takes it further in that, while based on a function of the statistical relationship, it expresses the association between an attribute and overall liking. It does so in terms of the proportion of those respondents whose overall liking is attributable to or influenced by positive perceptions of the attribute. Conversely, the statistic can be interpreted as the change in the proportion of respondents liking the brand if it no longer provided adequate performance on the attribute being studied.

Attributable effects partitions the impact of each possible attribute into two components: potential and loss. The goal is to identify areas of greatest opportunity (expressed as potential), and areas of greatest risk (expressed as loss).

In this example, frequent users of a specific type of body spray were asked to evaluate a number of body spray brands on a set of attributes relating to performance. Overall liking was obtained for each brand as well. We’re looking at the relationship between overall liking and fragrance performance for one specific brand. While all evaluations were obtained using five-point rating scales, the data have been separated into top two box vs. bottom t...