An American beer brewing company approached MSA seeking to better understand how advertising leads to increased brand loyalty and sales by linking various measures of advertising to key sales indicators for their flagship light beer. MSA’s Advertising Integration Analysis (AIA) solution was the perfect approach to understand both short-term and long-term impacts of advertising within the category and for the brand.
The Client is an American beer brewing company. They approached Management Science Associates, Inc. (MSA) seeking to better understand how advertising leads to increased brand loyalty and sales by linking various measures of advertising quality and effectiveness to key sales indicators for their flagship light beer.
This data heavy project involved integrating multiple data sources; ad tracking, behavioral tracking, copy tests, GRPs, PR and marketing mix coefficients, across brands, ethnic groups, age groups, gender and geographic regions.
MSA’s Advertising Integration Analysis (AIA) solution was the perfect approach to clarifying both short-term and long-term impacts of advertising within the category and for the brand. In this case, we applied our AIA solution using five analytical analysis steps:
- Create a large dataset by integrating ad tracking and behavioral tracking data based on age groups, gender, regions and ethnicity. Then tabulate and graph all of the variables to get an overall understanding of data trends.
- Create 2-dimensional maps to visualize which ad tracking and behavioral tracking variables are key drivers of ‘brand attachment’ vs. ‘behavioral motivations’ and how they relate to each other. Create 2-dimensional maps for the category, key brands, and for ethnic groups, age groups, gender and geographic regions for the subject brand.
- Use Factor Analysis to consolidate the data and help manage the correlation between similar variables.
- Use Structural Equation Modeling (SEM) to quantify the relationships between measures, such as awareness and loyalty, and to see how they vary across demographic groups and brands.
- Create a second data set integrating the ad tracking data with GRPs, copy tests, marketing mix coefficients and PR data. Use this secondary data set to model the impact of TV advertising on awareness via Regression Analysis.
The study identified nine key measures for the Client’s brand that are influenced by a combination of both advertising and product experience. MSA calls these “bridge measures”. More importantly, MSA was able to provide the Client with absolute clarity on the intricate web of interrelationships between these nine affinity (brand image) and involvement (repeat purchase) bridge measures that comprises the brand’s purchase funnel.
As a direct result of the study, the Client was able to refocus brand monitoring on just these nine bridge measures. Whenever they saw any significant shifts in these key measures, they knew they had to look into making changes to their communications and/or other marketing levers. Not only did this immensely simplify their brand monitoring activities, it also provided the Client with a tactical marketing early indicator mechanism for their most important brand.
For more information on this MSA solution, please contact Kevin Mason (email@example.com), Vice President.