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Surpassing the Norm – Better Approaches to Providing Meaningful Context – Part I

August 13th, 2015 Comments off

Context is important.  Consider the story of little Johnny, just returned from school with a test paper for his father to sign.

“Hi Dad, could you please sign this for me,” Johnny said uncertainly, handing the paper to his father.

“Mm, a 75 huh,” Dad said thoughtfully pursing his lips, “what was the class average?”

“75,” said Johnny slowly.

“Oh, not bad then,” Dad smiled.

Suddenly feeling the need for full disclosure, Johnny continued, “We took the test on the day of the class trip to the aviary that I didn’t go on due to my irrational fear of being carried away by a giant eagle.  Only three of us didn’t go – the teacher left a test for us to do so we wouldn’t be bored.”

Dad raised an eyebrow. “I see,” he said, “how did the other two do?”

“Well, there was a new kid who didn’t go because he didn’t have a signed consent form.  He got a 50, but then he’d never studied fractions before…”

“Oh,” said Dad, quickly computing in his head, “so the other must’ve gotten a 100?”

“Yeah,” said Johnny, “but that was Joey DaBrain – he always gets a 100!”

“Hmm,” said Dad as he handed back the signed paper and slowly walked away, puzzling over whether Johnny really had performed acceptably on this test or whether it really even mattered.

While this scenario may seem slightly bizarre, it highlights the importance of context in evaluating performance, and reflects on some troubling issues plaguing the communications research industry in the provision of benchmarks to put research results in proper context for making crucial decisions for a brand’s communication strategy.

We may be comforted to know that our new advertisement is performing “at norm” when tested, but how can we be confident that a benchmark provided by our research supplier is truly relevant for our brand?  Unfortunately it may not be, since traditional norms – typically category or industry averages – are affected by a variety of issues which may render them inappropriate or even misleading.

Representation:  A benchmark can only be as strong as its representation.  The average of the three students who took the math test is not likely to be representative of the average ability of all 25 students in the class.  A norm computed for a particular category is likely to be composed of whatever the research supplier has at hand, rather than a representation of the category as a whole.  And composition can clearly make a difference in practice.

Take for example average MSW●ARS CCPersuasion scores for four major brands in the same household products category, as shown in the following chart.  The average scores between the brands vary considerably.  The average across all four brands is 7.7.  However as the graphic illustrates, excluding either the strongest scoring or weakest scoring brand can dramatically affect the overall average.  The answer to ‘how are we doing’ for Brand A would be considerably different based on the presence or absence of brands B or D in the normative computations.

norm-fig-01Brand Development: We likely wouldn’t hold Johnny to the same standard as Joey DaBrain when it comes to results on a math test.  Children have unique strengths and should be treated accordingly.  The same is true for brands.  Even if a benchmark were to account for all brands in a given category, it is not a given that this benchmark would then be appropriate for application to research results for all brands in the category.

This is because brands have unique situations that should be accounted for in assessing effectiveness of commercial communications.  A brand team with a new entry to the category shouldn’t necessarily be held to the same standard as the category leader.  As an example, the variation in purchase intent levels for seven different brands in a personal care category shows how a one-size-fits-all approach to normative data will give a misleading result for many brands.

norm-fig-02Consistency: We may attempt to expand the context for understanding Johnny’s test result by considering the class next door, which recently had a test on fractions.  However the results for the other class are obviously affected by the teacher’s choice of questions and typical difficultly level of his or her tests.  When it comes to normative data, similar considerations are also in play.  If we are considering a verbal metric, results could potentially be affected by such factors as question wording, type of scale used, placement within the questionnaire and sample group considered.

Even for a behavioral metric with a consistent and rigorously monitored methodology such as CCPersuasion, there can be differences in how brands in the same category define their competitive brand set, particularly in categories that are somewhat ambiguous or can be defined more broadly or narrowly.  Such differences may again make comparisons to category averages less meaningful than first presumed.

Availability:  In some cases, particularly in new or emerging categories, it may be difficult or impossible to formulate normative data for a specific category or even broader industry segment.  Or for smaller categories, it may be necessary to reach far back in time to assemble sufficient cases, leaving the resulting norms susceptible to changes in market conditions, consumer sentiment or research methods.

Scope: A category norm requires historical test results for the metric of interest across a reasonably robust number of brands and overall cases.  So by definition, such a metric will need to be general enough to be in broad use in the research industry.  This will include common metrics such as liking, purchase intent, awareness and so forth.  While benchmarks may be readily available for these metrics, this likely will not be the case for many of the brand-specific metrics that the brand team is particularly interested in, which leads to the last and perhaps most important issue with normative data – meaningfulness.

Meaningfulness:   Beyond the appropriateness of the “class average” of 75 as a benchmark for Johnny’s performance on the math test, perhaps the larger issue was whether the test result was at all meaningful in predicting Johnny’s success in the course, given that it was likely a make-work exercise for the students not participating in the class trip.  Similarly, while much effort may go into providing normative benchmarks for a battery of standard metrics, are the resulting comparisons useful to the brand?

Generally, a given metric may be considered useful in the assessment of commercial communication if it is either predictive enough of in-market effectiveness (typically sales-response) to be useful as an overall success criterion, or is specifically related to the brand or category in such a way as to guide revisions or future developmental work.  Unfortunately, the metrics for which normative data is typically available, such as liking and recall/awareness, are too general to provide specific guidance to the brand, and they have been shown not to have a strong enough relationship to in-market effectiveness to be appropriate as a success criterion, as for example in matched-market advertising weight and copy tests:

norm-fig-03

Despite these issues, research managers desire context for their research results – and rightly so, as context is imperative.  Part II of this series will highlight approaches pioneered by MSW●ARS that provide appropriate context for research results while avoiding the pitfalls which beset standard normative data.

Please contact your MSW●ARS representative to learn more.

MASB’s Game Changing Brand Investment and Valuation Project – Part II

August 4th, 2015 Comments off

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”.

MASB PART II FIG 01

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.

MASB PART II FIG 02

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.

MASB PART II FIG 03

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.

MASB PART II FIG 04

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.

MASB PART II FIG 05

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.

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MASB’s Game Changing Brand Investment and Valuation Project – Part I

July 20th, 2015 Comments off

How much is my brand worth in financial terms?  How much will my marketing grow its value?

Despite their seeming simplicity, these two questions have frustrated brand practitioners for decades.  It is well accepted that there is a link between brand building activities and corporate profits.  After all, the entire field of marketing is based upon this proposition.  Yet it is equally well accepted that there is no standardized approach that companies can rely on to quantify brand value in the dollar-and-cents terms applied to other assets.  This puts marketing at a severe disadvantage within boardroom discussions of resource allocations, as its expenditures are all too often seen as pure costs rather than investments in the business.  And this is despite a growing realization that intangibles account for up to eighty percent of overall corporate value with brands being at the top of the list.

But one industry group is actively working to change this.  The Marketing Accountability Standards Board (MASB) created the Brand Investment and Valuation (BIV) project to establish the quantitative linkages between marketing and financial metrics.   The solution they have proposed is as simple as the questions themselves:  Identify a “brand strength” metric which captures the impact of all branding activities, understand how this metric translates into financial returns (ultimately cash flow), and then use this to calculate a brand value and to project the return from future marketing investments.

MASB-FIG-01

Of course this begs the question, does such a “brand strength” metric exist?  And if so, is it practical enough to be used?  After an exhaustive search of research literature, MASB identified brand preference as the most likely candidate for the brand strength metric.  Brand preference (also known as brand choice) is defined within the common language in marketing dictionary as:

One of the indicators of the strength of a brand in the hearts and minds of customers, brand preference represents which brands are preferred under assumptions of equality in price and availability.

The ability of brand preference to isolate brand strength from other market factors (e.g., price and distribution) separates it from other marketing measures.  Furthermore, previous studies demonstrated that the behavioral brand preference approach pioneered by MSW•ARS met MASB’s predetermined ten criteria of an ideal metric:

  1. Relevant:  It has been proven to capture the impact of all types of marketing and PR activities.  Over the last 45 years it has been used to measure the effectiveness of all forms of media (e.g. television, print, radio, out-of-home, digital), events (e.g. celebrity and event sponsorships), and brand news (e.g. product recalls, green initiatives).  It has also been shown to capture both conscious and unconscious customer motivations and so applies equally to rational, emotional, and mixed branding strategies.
  2. Predictive:  Its ability to accurately forecast financial outcomes has been demonstrated in a number of studies.  This includes studies comparing preference to sales results calculated from store audits, in-store scanners, pharmaceutical prescription fulfillments and new car registrations.  When applied to advertising, changes in brand preference have been proven to predict changes in the above sales sources from control market tests, split media tests, pre-to-post share analysis and market mix modeling.  In fact, Quirk’s Magazine noted over a decade ago that “this measurement has been validated to actual business results more than any other advertising measurement in the business”.
  3. Objective:  It is purely an empirical measure by nature.  No subjective interpretation is needed.
  4. Calibrated:  It has been applied to the broad spectrum of brands and categories and its correlation to sales has proven consistent across geographies.  Furthermore, it self-adjusts to the marketplace where it is collected so it has the same interpretation without any need for historic benchmarks.

MASB-FIG-02

  1. Reliable:  It has been shown to be as reliable as the laws of random sampling allow.  This is true both for brand preference gathered at a point in time and for changes over time caused by marketing activities.  The table below summarizes this consistency in measuring changes.  Changes in brand preference caused by 49 campaigns were each measured twice among independent groups of costumers.  Observed variation between the pairs was compared to what would be expected from random sampling.  The ‘not significant’ conclusion confirms that the measure is as reliable as the laws of random sampling allow.

MASB-FIG-03

  1. Sensitive:  It is able to detect the impact of media even from one brand building exposure (e.g., a single television ad shown once).
  2. Simple:  It is easily applied and understood.  It can be incorporated within any type of customer research including tracking, pre-testing, post-testing, segmentation, strategy, product concept.
  3. Causal:  While it captures the effect of product experience, it is not driven by just product experience.  In fact, it has been proven predictive of trial for new products for which consumers have no experience.
  4. Transparent:  It doesn’t rely on ‘block box’ models or norms.
  5. Quality Assured:  Its reliability and predictability are subject to continuous review.

To verify its suitability as the brand strength metric, MASB included an aggressive trial of brand preference as part of its BIV project.  A cornerstone of this endeavor was a longitudinal tracking study sponsored by six blue chip corporations and conducted by MSW•ARS Research.  The two year study covers one hundred twenty brands across twelve categories with a variety of market conditions.  In part II of this article we will review several of the key findings from this project, which are already changing industry perceptions on measuring brand value and making brand building investments.

The MSW•ARS Brand Preference measure 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.  In future blog posts we will discuss this and how you can easily apply it.

If you don’t want to wait then please contact your MSW•ARS representative to learn more about our brand preference approach.