In Part I of this blog series we discussed ten technical characteristics of brand preference which made it suitable for adoption into market research tools.  But just because something can be done doesn’t mean it should be done.  In fact, one of the issues identified early on by Marketing Accountability Standards Board (MASB) was that the sheer number of metrics in use could lead to a type of analytical paralysis; that is, an inability of insights groups to efficiently advise other functions of the organization.  This has been euphemistically referred to within the group as “swimming in data”.

Given MASB’s focus this primarily revolved around the plethora of metrics being applied to quantify the overall financial success of marketing activities.  But from our experience addressing this “swimming in data” issue is even more urgent for tactical research applications, especially brand tracking.  It is not uncommon to see between fifty and one hundred different category and brand attributes being monitored.  Each of these attributes captures a dimension of “equity” deemed important for brand success.  But how does an analyst combine these metrics to quantify the total health of the brand?

One popular approach is to apply structural modeling of the attributes versus sales data.  The resulting model provides a means of “rolling up” attributes into one number.  However, there are several challenges with this approach.  One is that such a model often becomes viewed as ‘black box’ by other functional areas.  This lack of transparency and simplicity fuels distrust and slows down adoption of insights.  But even worse is that such a model is only applicable to the environment in which it is derived.  Technological, financial, and even style trends can dramatically change the relative importance of attributes within a category thus uncoupling the model’s link to sales.  For example, being viewed as ‘having fuel efficient models’ is much more important for an auto brand when gas prices are high than when they are low.

Brand preference offers a better approach to the “swimming in data” issue.  As a truly holistic measure it captures the influence of all these attributes.  This was confirmed in the MASB Brand Investment and Valuation project.  Several of the marketers participating in the brand preference tracking trials provided equity data from their internal tracking programs for comparison purposes.  Across the categories investigated there were seven other brand strength ‘concepts’ commonly used.

A correlation analysis was used to contrast their relationship to changes in brand share of market versus that of brand preference.  What was found is that the strength of their relationships to share varied by category and oftentimes fell below the correlation level deemed moderately strong by Cohen’s Convention (Jacob Cohen, Statistical Power Analysis for the Behavioral Sciences; 1988).  Furthermore, all of these other metrics exhibited correlations to market share substantially below that of brand preference.

But brand preference didn’t just demonstrate stronger relationships to market share than these other measures, it also captured their individual predictive power.  This is most readily seen by contrasting each measure’s explanatory power of preference to that of market share.  All seven measures exhibit a stronger relationship to preference than to market share.  Given that the preference was gathered on a completely different sample than the other measures, this strongly suggests that the explanatory power of these other measures is acting through preference in explaining market share.

In addition to these seven common concepts, category specific attributes were also examined.  Of the seventy metrics examined not a single one showed potential to substantially add to the predictive power of preference.

Probably the most extreme example of the advantage of brand preference as a holistic tracking measure comes during a product safety recall.  During these situations it is not unusual to see top-of-mind awareness levels peak near one hundred percent.  At the same time, brand attributes such as safety and trust which typically show milder importance rise to the top.  Under these conditions a structural model’s ability to explain sales may not just drop to zero but actually turn negative.  That is, it will report brand strength rising even as sales precipitously drop!  Since brand preference not only captures the changing level but also the changing importance of these other dimensions, it remains a valuable tool for navigating such times at it will accurately monitor progress in rebuilding the brand in the hearts and minds of consumers.

The Tylenol tampering incident illustrates this.  As the nation watched several people die from the poisoning, brand preference plummeted thirty-two points.  The Tylenol brand could no longer be trusted.  Concurrent with this brand preference drop, Tylenol’s market share fell thirty-three points.  As Johnson & Johnson addressed the situation responsibly, the brand’s previous place in the minds of consumers was slowly rebuilt.  This set the stage for a rebound in brand sales as tamper protected versions of the brand’s products made their way onto store shelves.

Because of its ability to accurately monitor the total health of a brand, the MSW●ARS Brand Preference measure is quickly becoming viewed as the ‘King of Key Performance Indicators’.  But there are other very pragmatic reasons for incorporating it into your tracking and other research.  In future blog posts we will discuss these and how easy it is to do.

Please contact your MSW●ARS representative to learn more about our brand preference approach.