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Stated vs. Derived Importance in Key Drivers Analysis

Back by popular demand…derived importance.

A great deal of research is designed to measure the relative impact of specific features of products or services on customers’ satisfaction with those products or services.

Sometimes, surveys are designed to measure importance of those features explicitly and in isolation—no further analysis is necessary than an understanding of which features are more important to customers than others.

In other cases, the importance metrics will be used to determine what, if anything, could or should be changed to improve the product. That’s where key drivers analysis comes in, but more about that later.

Measuring importance through traditional Likert scales, while certainly frequently done, is not the method FGI recommends to measure importance. There are 2 fundamental reasons for this.

First, importance scales often do not provide adequate discrimination and differentiation between product features, especially when viewed in aggregate.

Q: How important is price?  
A: Oh, that’s very important.

Q: How important is product availability? 
A: Oh, that’s very important.

Q: How important are helpful store employees?  
A: Oh, that’s very important too.

Second, people use scales differently (and this problem is not limited to importance scales). Respondents tend to calibrate their responses to previous scores. For example, here’s Respondent #1, rating the 3 attributes in our survey.

Q: How important is price? 
A: Let’s give it a 9.

Q: Now, how important is product availability? 
A: Well, not as important as price, so let’s say 8.

Q: How important are helpful store employees?
A: Less important than price, but more important than availability. 8 for that one too.

But Respondent #2 may follow precisely the same response pattern—9 / 8 / 8—but start their ratings at 6 instead, yielding 6 / 5 / 5. Should we view these three features as more important for Respondent #1 than for Respondent #2?  No. Do any of Respondent #2’s answers qualify for top-2 box summaries?

No. One’s person’s 9 rating may be another person’s 6 rating. The very nature of scales—that the values are relative, not absolute—can cause misinterpretation of the results.

There are occasions where stated importance is appropriate and useful. If this is the case, there are far better ways than Likert scales to measure it, but that’s a subject for another day.

Measuring derived importance

Key drivers analysis yields importance in a derived manner, by measuring the relative impact of product features on critical performance metrics like overall satisfaction, likelihood to purchase again, likelihood to recommend, or some combination of those. The structure of a key drivers questionnaire looks like this:

Q. This next question is about your satisfaction with XX in general. Please rate the store on how satisfied you are with them overall. 10 means you are “Completely Satisfied” and 0 means you were “Not At All Satisfied.”

This question is treated as the dependent variable for our analysis.

Q. Now, consider these specific statements. Using the same scale, how satisfied are you with XX on…

  • Variety of products and services
  • Professional appearance of staff
  • Length of wait time
  • Ease of finding things in store
  • Length of transaction time
  • Convenient parking
  • Convenient store location
  • Price

We can then do some analysis to determine to what extent each of these independent—aka predictor—variables influence overall satisfaction. This is done through something called Pearson’s R Correlations.

In correlations, we get a statistic called R^2 (R squared), which is a measure of the strength of the score of one item to another. In the case of Pearson R, 1.0 means a perfect, positive correlation and -1.0 reflects a perfect, negative correlation. An R^2 value of 0.0 means no correlation at all.

In a key drivers analysis, the higher the correlation between each of the specific attributes and overall satisfaction, the more influence that attribute has on satisfaction, thus the more important it is. Notice that we never have to ask the question “how important is…” since the derived importance tells us everything we need to know. But that’s only half of the equation.

As a result of the question structure, we get explicit satisfaction metrics on each of the individual attributes as well. This data tells us how well we perform on each of the attributes. The resulting output looks something like this:


In our example, “helpful staff,” “coupon policy,” and “items in stock” are the most important attributes; they have the highest correlations to overall satisfaction.

Now compare those attributes to “store location.”  The correlation is still positive, but not nearly as powerful as the first two examples. Remember, derived importance measures importance of individual attributes in relative, not absolute, terms.

The second part of our analysis shows that our store’s employees are helpful. In fact, it’s the highest performing attribute of all (while importance is viewed on the X, or horizontal, axis, performance is viewed on the Y, or vertical, axis).

This means that our store does well on this important attribute, and is considered a core strength. This is not the case with the other important attribute, like having items in stock, however. Our store gets the lowest performance rating on that very important feature.

From our survey results, management can quickly see that resources should be directed toward reducing wait times (more cashiers), improving their coupon policy if they can, and especially keeping popular items in stock.

We’ve precisely identified the few items that need to be prioritized, as improvement in satisfaction with these things will have a direct and measurable impact on overall satisfaction.