Applying Advanced Analytics to B-to-B Branding Research
by John Colias, Ph.D.
The importance of branding is understood by serious business-to-business marketing researchers, but when it comes to analyzing brand equity, most companies fail to use advanced analytics to its full potential. All too often, branding studies simply track key measures; they fail to quantify causal relationships, and deliver weak predictive power. Or, if research attempts to deliver statistical models, the models identify key attributes and their relative importance, but do little else.
To be sure, some useful information and knowledge can be gleaned from these expedient solutions. In most cases, however, there is little insight to guide branding strategy and define concrete actions that impact brand equity. When it comes to the practical application of branding research, senior management is usually underwhelmed.
Most senior managers are not very patient when it comes to long-term branding assessments. They want to know what customers expect from their brand and why they choose it versus competitive brands. When they invest in marketing research, they are looking to the findings for guidance on achieving immediate marketing goals and for making strategic decisions that will impact the future.
With the increased emphasis on accountability, measurement and ROI in B-to-B marketing, the focus is often on measuring the efficacy of tactical programs. While necessary, these assessments don’t lead to insights that impact strategic decisions about what new direction a business should take.
Management is asking questions such as: Where should resources be allocated for differentiation? What actions should be taken to have the greatest impact on customers choosing our brand? What will be the impact of these actions on our competitors?
New Tools Enhance the Value of B-to-B Branding Research
B-to-B branding research can be a far more effective strategic tool for understanding a company’s brand and factors that make up its brand equity. Advanced factor analysis, regression and simulation tools enable B-to-B marketing researchers to quantify brand equity and predict, virtually in real-time, the impact of specific actions on the company’s brand and on competitive brands.
As shown in Diagram 1, Factor Analysis identifies examples of observed brand attribute ratings which best indicate the relative performance of firms, as perceived by those who influence the buying decision. A rational assessment of the brand is formulated based on objective personal experience or verifiable information from others. Rational assessments are typically empirical in nature. Simultaneously, an emotional assessment of the brand is based on experience and communications, filtered through personal beliefs and attitudes. The rational and emotional assessments influence each other and ultimately cause changes in brand attributes reflected in survey ratings.
Additional Factor Analysis identifies which overall indicators best measure Brand Equity. Diagram 2 shows how Brand Equity has three dimensions—exposure, affinity and preference. The overall indicators of brand equity measure success within each of these dimensions. For example, high levels of awareness, familiarity and purchases indicate high exposure.
Latent-Class Factor models quantify the structures in Diagrams 1 and 2. For example, when the desired observed ratings (Diagram 1) are selected, the model Latent-Class Factor models quantify the structures in Diagrams 1 and 2. For example, when the desired observed ratings (Diagram 1) are selected, the model provides the appropriate weighting of these ratings which are used in the next step in the analysis: Latent-Class Regression
Latent-Class Regression quantifies the causal linkages from perceptions (rational and emotional) to the components of brand equity (exposure, affinity and preference). Latent-Class Regression delivers the added benefit of greater predictive accuracy. Greater accuracy is attained by breaking the unrealistic assumption that all customers are alike, an assumption ingrained in older regression techniques.
Diagram 3 shows a comprehensive analysis beginning on the left with observed brand ratings and perceptions of the brand (from Diagram 1). In the center is the Latent-Class Regression model that links brand perceptions with brand equity components. On the right are the overall indicators of brand equity (from Diagram 2). This model allows marketers to predict changes in brand equity and its overall indicators by inputting changes in observed ratings.
For example, if the model were to be used to simulate an improvement of the quality rating by 5%, then brand equity might be predicted to improve by 3%. This 3% improvement in brand equity might, in turn, be predicted to increase Recommendations for the brand by 2%. Combining Latent-Class Factor and Regression models into a simulation tool allows marketers to simulate alternative strategies causing changes in brand equity. This helps senior management define a viable brand strategy and investigate dozens of strategic alternatives to achieving set goals. Building advanced analytics, simulation and modeling into branding research transcends measuring awareness, interest and perception, and offers marketers the ability to make better strategic decisions faster, and with what implications. Marketing research has a seat at the decision-making table thanks to the strategic value it brings.
How Does B-to-B Brand Equity Monitor Stack Up Against
Conventional B-to-B Awareness and Branding Studies
The B-to-B Brand Equity Monitor is a purpose-built tool for B-to-B marketers, jointly developed by Godfrey and Decision Analyst, a leading global marketing research company headquartered in Arlington, Texas. The B-to-B Brand Equity Monitor is a strategic tool for assessing the strength of a company’s brand relative to competitors in its market. Through the use of advanced analytics and modeling, it offers insight executives need to make better strategic decisions that will drive business success. The B-to-B Brand Equity Monitor is the only business tool that fits this requirement.
About the Author
John Colias (firstname.lastname@example.org) is a Senior Vice President and Director of Advanced Analytics at Dallas-Fort Worth based Decision Analyst. He may be reached at 1-800-262-5974 or 1-817-640-6166.
Copyright © 2009 by Decision Analyst, Inc.
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