For many businesses, brands are among their most valuable assets. Brands provide consumers with reasons to choose theirs and to pay more for some brands than others. Further, they offer consumers “shortcuts” in their decision making on the basis of brand perceptions. Because brands are so overwhelmingly important to the success of a business, enormous amounts of research are devoted to understanding how they function within the mind of the consumer and, ultimately, influence purchasing decisions.

While it is well understood that brands are essential to successful marketing and business performance, marketing research aimed at understanding the consumer mindset in connection with brands has struggled to provide solid answers. This leaves many brand managers at a loss when it comes to understanding how to focus their marketing communications for the best chance of success. I suggest that one reason brand research is often so difficult is because consumers view brands in relation to their personal beliefs and experiences in a manner that is highly complex, multifaceted and fluid.

Researchers often strive to identify a single, simple model or method that can capture everything they want to know about a brand, but this single-metric/single-method approach is misguided. It is unlikely and potentially foolhardy to think that a question as rich and complex as how a brand works to drive consumer behavior could be boiled down to a single metric while still managing to provide significant insight. Therefore, it is essential for brand managers and marketing researchers to embrace a holistic approach to understanding brands. Brands, as they reside within the minds of consumers, are dynamic, changing and ever-evolving with each new experience. Accordingly, researchers are encouraged to use a dynamic approach, employing longitudinal design models for optimal insights. Outlined at the top right is an approach that uses multiple models and such a design.

1. Measure What Is Relevant (Then Measure It Again)

Understanding a brand’s place in the mind of the consumer starts with collecting the right data. Knowing what data to collect can be a challenging task, but understanding how brands exist and are accessed in the mind can provide a useful starting point. According to one model of branding, brand health within the mind of the consumer can be quantified across four distinct dimensions:

  • Salience: This is the initial requirement for branding within the mind. Salience captures the extent to which a brand is in the consumer’s mind at all or is included in the mental set when considering a purchase.
  • Differentiation: To hold a meaningful place within the mind of the consumer, a brand must occupy a distinct position relative to competing brands. Whether or not the brand is tangibly different from the competition, the consumer must perceive the brand as somehow different than others within the choice set.
  • Relevance: While differentiation is important, it is critical to the success of a brand that it be differentiated in a way that is meaningful to the consumer.
  • Loyalty: Conceptually, loyalty captures the extent to which a consumer feels connected to the brand in ways that encourage him or her to choose it over other brands. Loyalty encompasses more than just habitual behavior in a consumer-centered approach and also includes measures like brand affinity and connection.

Incorporating measures of salience, differentiation, relevance and loyalty within consumer research is essential, but a second, often ignored step is just as important when considering the dynamic nature of consumer perceptions and behavior. To fully understand a brand, one must delve into how it changes over time. Changes are often subtle, particularly for well-established brands. As such, simple trend data is not sufficient. What’s important is to examine how, as certain perceptions and behaviors change, these changes affect other perceptions and behaviors.

This exploration is best conducted via longitudinal design research – interviewing the same consumers at two or more points in time. Although monitoring the same consumer at multiple points in time poses methodological challenges, it is ultimately one of the most powerful tools for evaluating brands within the minds of consumers. To truly capture how and why brands change – and, more importantly, how strategically-directed change can be produced in order to obtain desired outcomes – the power of longitudinal data cannot be overstated.

2. Identify Hidden Features

One of the promises of modern analytics is that it allows researchers to see hidden patterns or features that are not apparent with a surface level inspection. To realize the potential of brand data, researchers need to look beyond the surface metrics commonly collected and reported and look also toward multivariate methodologies, such as principle component analysis, factor analysis and latent variable modeling techniques. These multivariate approaches can provide powerful insights into the true structure of how a brand is perceived among consumers and can help researchers, analysts and key decision-makers move from an overwhelming quantity of data down to a set of key underlying factors that are robust across time. In the example below factor analysis was applied to a consumer packaged goods brand’s perceptual data to identify four key underlying factors. This targeted data helped the brand team understand how consumers think about their brand as well as others within the consideration set.

3. Identify Differentiation Points for Brands

Identifying critical points of differentiation from competitors is a key component of a robust brand model. Although there are various methods, one valuable approach to evaluate differentiation is to use correspondence analysis to place brands and perceptions onto a shared two-dimensional space to create a “brand landscape” map. In the second example pictured at left, two principal axes show the landscape in which four frozen food competitors are working. The X-axis identifies brands with a focus on back-to-basics natural food versus product innovations. The Y-axis contrasts brands focused on good taste versus health-related dimensions. With this visual analysis, it becomes easy to see which areas a particular brand “owns” and which areas are potentially underserved. In the visual, we can see that Brand A has a strong dominance in the “natural” space but that it’s not particularly differentiated on the taste/health dimension. In contrast, Brand B is highly differentiated from its competitors on the “taste” dimension but not on the natural/product dimension. Statistical tools can also be applied to these maps to identify “undifferentiated zones” within the map. Undifferentiated zones are areas within which a brand should have concern that it is not differentiated; it is a sign that the brand does not strongly stand for anything within the mind of the consumer. In the example, we can see that Brand C falls within the undifferentiated zone. Tracking movement over time within a brand landscape using longitudinal data can also provide useful insights into the stability of a brand’s positioning within the consumer mindset.

4. Evaluate Relevance by Analyzing Key Brand Drivers 

While multivariate methods are powerful for finding hidden meaning in data and identifying differentiation points within a competitive set, identifying those elements that are most relevant to the consumer is best handled with modern predictive analytic techniques. Using predictive analytics to identify the key perceptual drivers of demand is a particularly powerful route to understanding the brand’s consumer relevance, providing great value to the brand’s stakeholders. Well-tuned regression models can be particularly powerful for understanding which perceptual features are key drivers of demand for a product. Understanding this relationship provides deep insight into the relevance of a brand to consumers. 

Integrating across the approaches that have been discussed, it is often useful to build factors derived from a factor analysis (as described above) into a relevance analysis. This can provide a wide-screen view of which major factors are drivers of opposed to getting bogged down in the minutia of individual perceptual attributes. This data is often easier to interpret and more consumable by a broader range of brand stakeholders. In this example, we have taken the four factors identified earlier and used predictive modeling to identify their relationship to a set of behavioral measures. For this brand, the analysis identifies that health messaging is most relevant to purchasing decisions within the mind of the consumer. In contrast, specific messaging around usage occasions is relatively unimportant.

Reviewing a brand’s relevance within the context of its primary competitors can be useful because perceptions of competitors are often as relevant to purchasing behavior as perceptions of a brand itself. In the next example, predictive analytics have been applied in order to identify the relevance of both a brand’s perceptual measures and perceptions of its competitors. The diagram on the next page highlights areas where the brand owns a clearly distinguished and relevant perceptual idea (“Is a meal I feel good about”) as well as those ideas that are relevant but that are not owned exclusively by the brand (“makes healthy eating enjoyable”).

5. Harness the Power of Longitudinal Data

While significant understanding of a brand can be achieved with traditional tracking methodologies that do not track the same individuals over time, deeper insights can be achieved by monitoring the same set of individuals longitudinally over an extended period of time. This longitudinal assessment of the same sample, on the same measures, is the best way to understand how the dynamics of a brand have changed over time.

In particular, longitudinal data provides superior insights into brand churn and brand loyalty. Loyal customers are a brand’s holy grail. Longitudinal data provides brands with a wealth of insights and tools such as how loyal their consumers are and, conversely, the churn or volatility around a brand. With longitudinal data, churn can be quantified as the percentage of people who move in or out of a particular measure of demand, such as “brand used most often” or “past week usage.” Brands with differing amounts of churn or loyalty will need to adopt different brand strategies and tactics. For example, brands with a highly loyal customer base should focus on deepening relationships with their consumers while brands with extensive churn may choose to focus on generating large amounts of trial to replace a fickle user base.

6. Don’t Ignore Word of Mouth

Particularly critical, and often ignored, are Word of Mouth (WoM) effects. The rise of online review sites and social media have extended the range of WoM influence and altered the role that it plays in consumer purchasing decisions. Given the increased importance of WoM for sales, businesses must evaluate their effectiveness at generating and capitalizing on all kinds of WoM. Businesses must ask questions and gather data like:

  • What sorts of conversations are people having about our brand and products?
  • When people talk to experts or store employees, do they come away with improved perceptions of our brand?
  • When people share information about our brand, does it tend to be positive or negative, emotionally charged or indifferent?

Answering these questions can be a useful starting place, but to truly see the value in WoM research, a business must put it into the context of its advertising, consumer perceptions, competition and sales. A brand can then dramatically expand the points at which it strategically influences sales via advertising. Additionally, WoM can provide insights into the emotional life of a brand in a special way. Sentiment analysis of WoM experiences can show the emotional connection consumers have to a brand. This analysis includes insights with relevant context such as communications with close friends and family, social media and sales associates. Identifying the emotional context in which a brand is discussed can be a powerful clue to whether a brand is healthy or headed in a problematic direction.

7. Bring Divergent Data Streams Together

While the methods described above can be carried out within research based upon stated attitudes and behavior from a well-designed survey, value and insight can be added by blending the model results from consumer research with other behavioral data. For example, using text analytics to conduct sentiment analysis on social media chatter can be a useful supplement to more traditional perceptual and WoM measures captured in survey research. 

Integrating consumer research with sales data can connect insights about how consumers view a brand with broader business objectives, either by conducting consumer research among individuals for whom true purchasing data can be collected or by finding strategies to link consumer brand data to broader sales patterns. Marrying qualitative data streams with quantitative research is another challenging task that brings with it an opportunity for improved insight. While it is important that each individual data stream stands on its own right, a brand can maximize the effectiveness of its research by devoting significant thought to how these oftentimes divergent streams can be brought into harmony with each other.

Bringing It All Together

Clearly, understanding how brands reside in the mind of the consumer is a significant challenge. Consumers often can’t articulate what they believe to be true about brands and why they choose one brand over another. Therefore, the research and analysis necessary to understand where a brand sits vis-à-vis other brands that occupy space in the mind is rarely a simple task. Indeed, the mind of the consumer can only truly be understood by using a multiplicity of modern analytic techniques and then comparing and contrasting. If brand research is carried out with this perspective, as opposed to searching for a single number or measure, a deeper, richer understanding of the brand and consumer relations will be discovered.