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TBSM Chart of the Week 2022 Review

December 8th, 2022 Comments off

The MSW TBSM tracking service collects a variety of metrics across a wide range of categories. Throughout 2022, we published Charts of the Week using data drawn from the TBSM survey.  Six different categories and multiple measures within each category have been explored.  The categories were:

• Subscription Streaming Video Services
• Cryptocurrency Exchanges
• Domestic Airlines
• Meal Kit Services
• Body Moisturizers
• Fast Casual Restaurants

To illustrate the utility of some of the metrics available from the TBSM service, we will review some of the charts published over the past year.

One core question included in the TBSM survey is Brand Franchise. This question efficiently gauges the relationship of consumers with the major competitors in a particular category. The results can be used to quantify a wide range of concepts, such as awareness, consideration, usage, loyalty, conversion ratios, etc. One interesting analysis we like to focus on in our Charts of the Week is cross brand consideration – that is the overlap in consideration among brands. This analysis allows us to create a consideration map which helps to reveal which brands are closest competitors, the composition of market niches and brands that may be perceived as unique versus the competition. An example is the Airline Cross Brand Consideration chart which shows the overlap among the three traditional major airline brands, the close proximity of the two ultra-discount carriers, but the greater dispersion among the three discount airlines.

 

 

Another portion of the TBSM survey focuses on product characteristics that play a role in decision making when choosing a brand in a particular category. Respondents may identify multiple characteristics that are important to them and which one of those characteristics are most important. This information can be crossed with other metrics such as demographics, usage levels or brand relationship status to generate important insights. One example Chart of the Week from the Body Moisturizer category shows how skin rejuvenation is particularly important to the 55+ age group, among other insights.

 

 

Trends in important characteristics can also be informative. One interesting example comes from the Cryptocurrency Exchange category. We compared characteristics of primary importance before and after the high profile Crypto Super Bowl advertisements. The only item to gain, with an increase of 4 percentage points, was “From a Brand I Trust”. This seemed to indicate that the advertising and associated hype had a positive effect on branding in the category.

 

 

TBSM also collects information on the level of category usage. This information can provide insight into the most important consumers in a category – those who use, and hence purchase, in the category the most. An example Chart of the Week from the Fast Casual Restaurants category reveals that these all-important heavy users tend to be male, age 18 to 34, higher income and have children in the household.

 

 

Finally, TBSM also captures Brand Preference as one component of the survey. Brand Preference is the gold-standard metric for assessing a brand’s strength in the hearts and minds of consumers. In fact, independent studies conducted by the Marketing Accountability Standards Board (MASB) have found that preference proved to be a better fit to market share than any other standard research question examined. Brand preference has been adopted as the cornerstone of all MSW research systems due to this strong relationship with market share. Several of our Charts of the Week illustrate the utility of brand preference in different applications.

First, results from the Fast Casual Restaurants category illustrates the utility of brand preference as a proxy for market share and hence an unparalleled measure of brand strength. As the following scatterplot shows, the brand preference penetration metric is strongly related to systemwide domestic sales levels for Fast Casual Restaurants (as published by Nation’s Restaurant News), with an overall correlation of +0.93.

 

 

Next, changes in brand preference are reflective of actual changes in business results for a brand. An example Chart of the Week illustrating this application is drawn from the Subscription Streaming Video Services category. In March of 2021, ViacomCBS expanded its CBS All Access service and rebranded it as Paramount+. In the extremely competitive and dynamic online video streaming services category, Paramount+ saw a 67% gain in brand preference in 2021 versus CBS All Access preference in the pre-pandemic time-period (last quarter of 2019). While ViacomCBS doesn’t separately report Paramount+ subscriber numbers, total ViacomCBS subscribers (which also includes Showtime and other services) jumped to 47 million in Q3 2021 versus 17.9 million in Q3 2020 and 10.4 million in Q3 2019. According to ViacomCBS, this surge in subscriptions was driven by strong growth in Paramount+ sign-ups.

 

 

In addition, brand preference can detect changes in brand strength attributable to marketing activity. This is illustrated by returning to the example of the effects of the 2022 Super Bowl advertising on brand preference for Cryptocurrency Exchanges. All four brands that advertised in the Super Bowl saw at least some level of positive movement in brand preference. FTX was the winner with a jump in brand preference of 2.6 percentage points.

 

 

Finally, brand preference levels can be examined by target groups, defined by dimensions such as demographics or usage level, to understand where a brand’s (and their competitors’) strengths lie. An example from our Chart of the Week series shows brand preference by usage level in the Domestic Airlines category. One insight from this chart is that discount airline Southwest has by far the highest preference level among light users. On the other hand, the brand’s preference level among heavy users is exceeded not only by the three traditional major airlines, but also by fellow discount brands JetBlue and Alaska.

 

 

These are but some of the many applications of the data provided by the TBSM tracking service. We look forward to sharing more such insights in the coming year. In the meantime, have a Happy New Year!

Categories: Chart of The Week, Uncategorized Tags:

The “5 CMO Objectives” According to the AMA / Deloitte and How MSW Addresses Each

March 14th, 2022 Comments off

The 26th edition of the annual CMO survey conducted in February 2021 for the American Marketing Association by Deloitte, identified that the importance of Marketing had increased during the pandemic.  CMOs said that they were focused on brand building and reported 5 specific “Objectives”.

MSW Research directly addresses each of these “5 CMO Objectives” with unique measurements that enhance your insights process; measurements all founded upon the evidence based, MSW Predictive Brand Growth Marketing Model™

The “5 CMO Objectives” and how MSW addresses each with, a Philosophical Framework, specific Applications/Products/Services, and the Evidenced Based Proof that supports this:

“CMO Objective” Number 1:  Building Brand Value That Connects with Customers

Two elements of the MSW Marketing Model that address this CMO ”Objective”:

1:  Brand Relationships; existing brand relationships drive Brand Preference.  MSW uses a segmentation model that places every individual into one of eight groups for each brand in the category.  We utilize a relationship decision tree to identify the strength of the brand relationship with customers.

2:  Brand Perceptions; all successful brands have a set of distinctive brand assets (sensory cues: color, logo, design, character, jingle, etc.) that aid memory encoding and act as signals to enhance availability.  Additionally, all brands also have a differentiated positioning (a reason to be).

Framework and Research Objectives:

  • Category whitespace and market need priorities-Decoder™
  • Brand lift opportunities in competitive context-BrandScape™
  • Preference shift linked to short term sales and long-term brand equity-The Brand Strength Monitor™
  • Ongoing campaign and individual message/medium brand lift in competitive context-Advertising Performance Monitor™
  • Pre and post campaign message and media effectiveness-Touchpoint™

Applications/Products/Services:

  • A&U – Decoder™
  • Brand Purpose – BrandScape™
  • Brand Equity Tracking – The Brand Strength Monitor™
  • Early-Stage Message Screening – Sifter™

Evidence Based Proof that Supports this:

  • Of the points that support this objective, one that stands out in particular because of its validation as a predictive indicator of brand health and sales is our RDE Analytic Framework™, which measures Relevance, Differentiation and Emotion.  RDE™ has been proven to grow brands based on 17 years of experience, with 3,500,000+ individual brand evaluations, across 400+ categories, for 2,000+ brands, in 44 countries.

“CMO Objective” Number 2:  Increasing Awareness

Every piece of brand communication needs to build awareness. At MSW, we have proven methods to effectively measure awareness and determine the contribution to the memory structure that drives saliency and brand association.

Framework and Research Objectives:

  • Ongoing campaign and individual message/medium awareness and preference lift in competitive context-Advertising Performance Monitor
  • Pre and post campaign message and media effectiveness-Touchpoint

Applications/Products/Services:

  • Development / Copy Testing – TouchPoint™
  • Advertising Performance Tracking – APM™

Evidence Based Proof that Supports this:

  • Saliency, as measured by Top-of-Mind Awareness, is a stronger predictor of sales than Aided Awareness (average R2 = 0.70 vs. 0.44). Simply improving a brand’s TOM Awareness can often lead to an increase in market share.  We see this in the correlation between TOM Awareness and sales.  TOM Awareness is usually the second most accurate predictor of sales after our CC Brand Preference measure, which is independently proven to correlate at .94.

“CMO Objective” Number 3:  Acquiring New Customers

Evidence shows that the primary driver of brand growth is penetration, all other growth mechanisms are secondary. New customers can be acquired though promotional activity, but these gains tend to be short lived. A more successful longer-term strategy is to invest in brand building, and evidence shows that message quality is the most impactful element when explaining changes in market share.

Framework and Research Objectives:

  • Customer Acquisition Forecast and sales from advertising

Applications/Products/Services:

  • Development / Copy Testing – TouchPoint™
  • Advertising Performance Tracking – APM™

Evidence Based Proof that Supports this:

  • Research conducted by MSW on its database of advertising has found that 52% of changes in market share can be explained by ad quality.  Media explains 13% of market share changes and a variety of other factors explain 35% of the changes.  The importance of ad quality is undeniable, and our validated, proven and in 2 cases Patented, advertising development tools such as CC Brand Preference™, CC Brand Persuasion™, RDE Analytic Framework™ and Outlook® Media Mix Optimization & Market Share Forecast Model™ deliver high quality advertising.

“CMO Objective” Number 4:  Retaining Customers

This was the primary activity of more CMOs than any other in the 2021 CMO survey.  The pandemic was a shock to the system, but brand loyalty has been declining for years along with trust in brands.  MSW’s brand tracking studies show that approximately half the consumers in any given category are not loyal to any one brand.

An entire industry has been built to measure Customer Satisfaction, and to help companies improve their Satisfaction scores, yet customer loyalty continues to decline.  This has led people to question the value of customer satisfaction and to recognize that the real goal is Loyalty.

For MSW Research, the key to addressing this objective is the ability to accurately measure human emotional response to brands and their messaging, combined with measurement of various brand relationship segments.

Framework and Research Objectives:

  • Loyalty shifts among various brand relationship segments-The Brand Strength Monitor
  • Preference Shift linked to traffic, sales, etc.-The Brand Strength Monitor

Applications/Products/Services:

  • Brand Equity Tracking – The Brand Strength Monitor™
  • Brand Franchise Analysis™
  • Persuadables Segmentation Analysis℠

Evidence Based Proof that Supports this:

  • Our data shows that Brand Loyalty is affected by how each brand interaction makes consumers feel.  The MSW brand relationship model captures attitudinal loyalty and allows us to understand the drivers of loyalty. A battery of emotional response measurement tools drills down to provide exact direction to activate these drivers.

“CMO Objective” Number 5:  Improving Marketing ROI

Most importantly of all, brand and marketing investment must create brand preference over competitive offerings, which propels sustainable and longer-term financial value and growth.

Framework and Research Objectives:

  • Continuous measurement – The Brand Strength Monitor
  • Point-in-time measurement – Touchpoint 360

Applications/Products/Services:

  • Brand Equity Tracking – The Brand Strength Monitor™
  • Outlook® Media Mix Optimization & Market Share Forecast Model™

Evidence Based Proof that Supports this:

  • MSW Brand Preference explained more of the variation in sales across a study of 120 brands in 12 categories over 18 months than other classic market research metrics.  MSW Brand Preference is a measure of Long-Term Brand Equity and it explains most, but not all, changes in brand sales; but we still have a Market Gap that is explained by other factors.
  • The link between MSW Brand Preference and Sales has been presented to the ARF and AMA, written about in The Economist, The International Finance Review, The Journal of Brand Management and CFO Magazine, and has been discussed with The International Accounting Standards Board and incorporated into the ISO definition of Brand Equity.

Conclusion

The 5 CMO “Objectives” can be summarized by one illustration.

All brands have potential, not all are living up to that potential.  We help brands identify the reasons for the gap between their Performance and their Potential and provide guidance to help close the gap.

In addition to helping brands meet their current potential we uncover opportunities for brands to expand their future potential.

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Unusual Statistical Phenomena, Part II: Stat Testing of Percentages

January 24th, 2022 Comments off

Sometimes when looking at the results from survey data, we see something that makes us say ‘huh?’ or ‘that doesn’t look right’. When the odd results persist after verifying the data were processed correctly (always a good practice), there is typically still a logical answer that can be uncovered after doing some digging. Sometimes the answer lies with something that we will call ‘unusual statistical phenomena.’  This is part 2 of a series that will look at some of these interesting – or confounding – effects that do pop up now and then in real survey research data.

This time we will look at an unusual phenomenon that can occur when doing something typically considered fairly mundane – testing for statistical significance between percentages. An example will help to illustrate this phenomenon which periodically causes us to question stat testing results.

Let’s say we have fielded the same survey for two different brands. One part of the survey collects respondent opinions of the test brand using a battery of attribute statements with a 5-point agreement scale. The base size for each survey was 300.

Stat testing was conducted between results for the two brands for Top Box percentages on each of the attribute statements. However, some of the results are questionable. Specifically, for the attribute “Is Unique and Different” Brand B’s score was higher than Brand A’s by 4 percentage points, which was statistically significant at the 90% confidence level (denoted by the “A” in the chart below); while for the attribute “Is a Brand I Can Trust” Brand B’s score was higher than Brand A’s by 6 percentage points, which was NOT statistically significant at the 90% confidence level. How could this be!

How can a difference of 4 points be statistically significant while a difference of 6 points is not, even with the same base sizes? To understand how this can happen, let’s first look at the basics of how a statistical test for comparing percentages works.

First, a t-value is computed according to this formula:

Then this t-value is compared to a critical value. If the t-value exceeds the critical value then we say that the difference between the percentages is statistically significant.  The critical value is based on the chosen confidence level and the base sizes of the samples from which the percentages were derived.

In our example, we chose the 90% confidence level for both statistical tests and the base sizes are the same, so the critical value for both tests is the same. We also know the difference between the percentages (the numerator of our equation) is what appears anomalous as the difference of 4 led to a t-value that exceeded the critical value, while the difference of 6 did not exceed the critical value. Therefore, the issue must lie with the Standard Error of the Difference.

Let’s next examine what a Standard Error represents. Our surveys were fielded among a sample of the overall population. If we sample among women 18 to 49 in the United States, we will infer that our results are representative of the entire population of interest, which is all women 18 to 49 in the United States. However, it is unlikely that the measures we compute from the sample (such as the percentage that say Brand A “is a brand I can trust”) will be exactly the same as the percentage would be if we could ask everyone in the entire population of interest.  There is some uncertainty in the result because we are asking it of only a subset of the population. The Standard Error is a measure of the size of this uncertainty for a given metric.

In our equation, the denominator is the Standard Error of the Difference between the percentages. While not precisely correct, the Standard Error of the Difference can be thought of as the sum of the individual Standard Errors for the two percentages being subtracted (the actual value will be somewhat less due to taking squares and square roots). As the graph below illustrates, the Standard Error for a percentage is a function not only of the sample size, but also of the size of the percentage itself.

Specifically, for any given sample size the Standard Error is largest for values around 50% and decreases as values approach either 0% or 100%. For a base size of 100 (the dark blue line), the Standard Error is close to 5 for percentages near 50%, but decreases close to 2 for very small or very large percentages.  You can think about this as it being harder to estimate the percent incidence of a characteristic of a population when around half the population has that characteristic versus when almost all (or almost none) of the population has that characteristic.

In our example, the percentages for Is a Brand I Can Trust are close to 50%, so at a base size of 300 the individual Standard Errors would each be a little under 3. In contrast the percentages for Is Unique and Different are around 10%, so at a base size of 300 the Standard Errors would each be around 1.5.  That’s a big difference!

It follows that the Standard Error of the Difference for Is a Brand I Can Trust would be much larger than for Is Unique and Different. In fact, the actual values are 4.08 for Is a Brand I Can Trust and 2.34 for Is Unique and Different. Again, a big difference. If we divide the differences in the percentages by these values for Standard Error of the Difference, we get t-values of 1.47 and 1.71, respectively. Given the critical value is approximately 1.65, we see that the t-value for the difference of 6 is below the critical value (hence not statistically significant); while the t-value for the difference of 4 is above the critical value (hence is statistically significant).

Hopefully this takes some of the mystery out of stat testing and helps in understanding why what can appear to be anomalous results may actually be correct.

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