In parts I, II, and III of this blog series we discussed the added benefits and technical details of incorporating brand preference throughout the brand building process.  During this time we have received numerous requests for more details with the most common being of the form, “Are brand preferences important for my category?”  Typically this question has come from brand stewards competing in categories where the products or services are either not bought on a recurring, individual basis, are not “bought” at all, or had dynamically changing competitive sets.  So we wanted to take a brief moment to reiterate the breadth of category representation designed into the MASB BIV project and to provide examples from published cases demonstrating the importance of brand preference in other category types.

The MASB-sponsored, multi-year longitudinal study was conducted with the cooperation of six blue chip corporations from a variety of industries including fast moving consumer goods, food, beverages, and autos.   Each of these participants chose two categories to be included.  The resulting twelve categories represented a wide variety of product types and market conditions.  Individual unit prices ranged from under one dollar to over thirty thousand dollars.  Some of the product categories lent themselves to spontaneous purchase while others required greater deliberation which could include third party influencers in the decision making process.  Some of the categories were highly fragmented while others had only a small number of competing brands.  Typical consumer purchase cycles could vary from a week to up to a decade.

Despite these category differences, brand preferences were shown to be the strongest predictor of individual brand unit share both across and within the twelve categories examined.

 

Unit share was chosen as the dependent variable for two reasons.  One is that marketing is primarily focused on creating conscious (cognitive) and unconscious (affective) predispositions to choose the advertised brand over competitors.   So for a measure of brand strength to be relevant it must explain the percent of choices allocated to the brand.  The second, equally important reason is that for all of the categories included in the study units sold drive financial cash flow models.  By combining an estimate of a brand’s unit share of market at a given price point and cost of production with assumptions of future category size based on population and category penetration trends, a projection of cash flow can be made.  A discounted cash flow calculation can then be used to create a brand valuation.  Hence, explaining unit share for these categories is fundamental to brand valuation.

But there are other categories where the cash flows do not source from unit sales within a relatively stable competitive set.  For these categories the dependent variable changes but the role of brand preference remains the same.  Here are some examples drawn from previously published MSW•ARS studies.

Web Search Engines

In our first example the service isn’t bought at all!  When a person uses a search engine like Google or Bing they aren’t charged.  Rather the revenue stream comes primarily from advertising revenue from the searches conducted.  So the key variable to understand is the share of searches.

Credit Card Networks

Credit card networks are similar to search engines as the users don’t purchase them.  Plus there is an added complication that the network (e.g. MasterCard, Visa, American Express, Discover) oftentimes shares the value proposition with partner brands in the cards issuance (e.g. financial institutions, retailers).   But even with this complication brand preference for the networks themselves plays a key role in determining their share of the cards in circulation.

Restaurants and Retailers

For restaurants and retailers brand preference exerts itself in numbers of visitations and/or the amount purchased on each visit.  Collectively this translates into receipts for products acquired through each brand’s outlets.  The following graph shows the relationship between brand preferences for casual dining restaurants and their percent of receipts captured.

Pharmaceuticals

Pharmaceutical brands are unique in that the ultimate decision of which to use is necessarily made in partnership with an expert, their doctor.  Still, patient brand preference plays an important role in the process as demonstrated by this meta-analysis comparing preferences for pharmaceutical brands for five afflictions to their corresponding share of prescriptions.

Auto Insurance

Every subscription based service faces a moment-of-truth in which a customer’s decision to change to a competing service will likely lock the spurned brand out of that customer’s consideration for a period of time, sometimes several years.  This makes consistently maintaining brand preference critical not only for growing share but combatting churn, as demonstrated by this chart of auto insurance brands.

Movie Box Office Openings

When it comes to movies, predicting opening weekend ticket sales is of paramount importance.  But this is difficult given the constantly changing theater environment – each week the mix of competitors changes with up to one half being entirely new!  As a meta-analysis covering one hundred fifty three movie releases shows, not only is brand preference the most important single element for determining a new release’s share of a weekend’s total box office receipts, but when combined with other elements can accurately project weekend gross well before the opening!

 

Please contact your MSW●ARS representative to learn more about how our brand preference approach has been integrated across our entire suite of solutions.

In Part I and Part II of this blog series we discussed the empirical strengths and corporate needs driving brand preference’s adoption.  But one aspect that pleasantly surprises those new to the technique is how easy it is to deploy relative to other measures.

Most common brand metrics are collected through the use of a closed-ended question followed by a Likert or intention style scale.  An example would be the common stated purchase intent question:

How likely are you to buy [INSERT BRAND] in the next [INSERT TIME PERIOD]?

1. Definitely will buy
2. Probably will buy
3. Might or might not buy
4. Probably will not buy
5. Definitely will not buy

While on the surface this looks fairly simple, in practice it is difficult to extract meaningful, sales calibrated information from it.  Since this is a stated measure it is subject to each respondent’s subjective interpretation and cognitive bias.  One respondent’s understanding of “Definitely”, “Probably”, and “Might or might not” can vary dramatically from another.  And while this effect can be averaged out across large samples, it makes subgroup comparisons very difficult; with psychographic and demographic groups oftentimes exhibiting substantial mean differences.  Without strong normative data (which is oftentimes very difficult to achieve) this can lead to false relative conclusions.

Worse yet, differences can also be manifested by seemingly innocuous changes in survey deployment like changing question order or sample sources.  As demonstrated in the ARF Foundations of Quality project, different panels produce substantially different response levels even when great effort is applied to demographic balancing.  This occurs for even the most straightforward stated questions, such as reported product usage, at rates which exceed those expected from sampling error.

And even when the above factors are rigorously controlled the stated questions still require a scale translation to calibrate the results with in-market performance.  This translation, which in itself is subject to estimation error, results in a ‘black box’.  This slows down the analytic process and can also reduce confidence in the results by end users because the linkage is no longer intuitive.

Brand Preference by comparison is much more robust.  The incentivized act of choosing from a competitive set replicates much of the dynamics of an actual purchase occasion.  Therefore respondents intuitively understand the exercise and the results naturally calibrate to sales performance.  This makes it an ideal method for sub-group comparisons as no norms or translations are needed for interpretation.

But perhaps most exciting is how respondents respond to the brand preference exercise.   Surveys consisting of closed-ended and open-ended questions can quickly disengage respondents leading to straightlining, speeding, satisficing, and other bad survey taking behavior.  In an attempt to combat this insight teams have been compelled to continually reduce the number of questions asked in a survey and the number of options, especially brands rated, included within attribute tables.   Essentially depth of research is being traded off for response quality.

Including a brand preference exercise within such surveys counteracts this trend.  Not only does it provide valuable information for each brand within a category in a very time efficient manner, the nature of the exercise improves engagement in much the same manner as gamification.  In fact, when brand preference is added to a survey it is common to see self-reported survey length drop while survey satisfaction ratings rise.

As an example of this, we recently created for a client a first-of-its-kind brand preference based, behavioral in-store shelf optimization testing platform.  Respondents have often viewed traditional approaches to this type of research as tedious and not worthwhile.  By contrast, the results for this new approach have been outstanding.  On a ten point scale, 98% of respondents rated the system a 5+ and 55% rated the new system a perfect 10.

 

But perhaps more impressive than this quantitative assessment is the open-ended survey feedback respondents chose to share.  Comments like these were common:

“LOVE that it was short and to the point, no dragging it out.”

“…the ease of instructions. They were not confusing.”

“There was not a lot of ambiguous stuff. Well prepared.  User friendly.”

“It was very different than other surveys I’ve taken, and I appreciated that variety!”

 “This survey was very different, fun, interesting, and relevant. I like the conciseness of it and that it didn’t ask the same questions over and over again. Nice survey and great topic.”

“I was actually a little disappointed when the end questions came up. I wanted to shop more.”

Simply put, when it comes to survey deployment MSW•ARS brand preference is unlike any other metric.  MSW•ARS Brand Preference can be incorporated into a wide variety of research and can even become a standard key performance indicator in your reporting, particularly in your tracking data.

Please contact your MSW●ARS representative to learn more about how our brand preference approach has been integrated across our entire suite of solutions.

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.