How do you know if your "brand advertising" is creating real financial value?
Let’s say you have a tracking study out in the market in which you’ve identified 15 key brand attributes and have a sampling of customers and prospects rating your brand vs. competitors on each attribute. You also ask about self-reported purchase activity in your category. You survey 200 people each month and read the results on a rolling three-month basis.
Now, using statistical regression techniques, you can correlate brand attribute ratings to purchase activity or purchase intentions to identify the attributes that are most strongly associated with increased category or brand purchase behavior.
Simple, right? Hardly.
There are a great many places where this approach can get derailed or become seriously misleading.
First off, self-reported purchase behavior can be significantly different from actual purchase behavior. Sometimes, people forget how much they bought and which brands. Other times they tell little white lies to protect themselves from the judgment of others (even the interviewer). If you can connect a specific individual’s survey responses back to that person’s actual purchase behavior as reflected in your transactional files, you can close the gap somewhat. If not, you might check to see if there's a syndicated "panel" study done in your category where consumers respond to survey questions and share their actual receipts or credit card statements. Failing that, you can conduct a separate study specifically among a group of category consumers and check to see how self-reported behavior varies from actual purchases, then use that as an error factor to adjust what you get from your tracking studies.
Second, attributes are commonly “lumped” together by consumers into positive and negative buckets, making it difficult to see any one attribute as a real driver to a greater degree than others. This is the covariance effect — a statistical term indicating the extent to which two or more elements move in the same direction. Sometimes it’s helpful to group attributes with high covariance into “factors,” or higher-level descriptions. For example, the attributes “offers good value for the money” and “is priced competitively” might be grouped into a factor called “price appeal.” As long as you aren’t grouping so many attributes together into a few still undistinguishable factors, you can still get a strong feeling for which elements of the brand scorecard might be most important.
There are many more ways that this process can become subtly misleading. If you’re not a research professional or statistician, you might consider consulting one of each in your methodology design. But, time and again, interviews with researchers suggest that the best approaches start with sound qualitative research among customers and prospects to identify the possible list of driver attributes and articulate them in ways that are clear and distinct to survey respondents.
Done correctly, this effort can help directly link changes in attitudes or perceptions caused by brand advertising back to incremental economic value creation. But obviously it takes time and money to lay this foundation. If you're spending a few million (or more) annually on brand advertising though, it might just be worth it.
Have you been able to identify specific aspects of your brand that drive customer relationships? We'd like to hear your story.