Using Bayesian Statistics in Market Research

Our Executive Director and Co-Founder, Ron Stroeven, writes about the Bayesian model and its applications for market researchers in his latest piece for ESOMAR’s Research World blog.

Did you know that we use Bayesian statistics as the foundation for some of the data analysis you can conduct in Harmoni? It is a way we can show probability and relationships among various groups and can help you find the most interesting data insights. It helps us navigate a data landscape that holds many greys, not just black and white data.  

Our Executive Director and Co-Founder, Ron Stroeven, writes more about the Bayesian model and its applications for the market researcher in his latest piece for ESOMAR’s Research World blog, “What you should know about Bayesian statistics in Market Research.”  

The article covers ways that this model can help you better profile your target audiences and compare them easily to other relevant groups. He writes, “For example, you can profile those who prefer your brand and then compare that profile with the same for all the other brands. This allows you to dive in and see which brands are the most and least like yours from the customer standpoint.” 

A more detailed example using data from the New Zealand Visitors Survey that compares groups by market and activity is given. “The Bayesian model allows us to perform statistical tests on each descriptor variable (e.g. age, gender, income, travel style) to calculate the probability a group value is greater (or lesser) than the value for those not in the group.”

He illustrates that a Bayesian model has practical applications in bringing the most interesting insights to the forefront for market researchers. However, we need the right technology to help us use this approach for data analysis. Harmoni’s Discover feature is underpinned by Bayesian statistics, helping market researchers by automatically bringing the most interesting findings to the surface, enabling a deep understanding of target groups, quickly.

Discover works by performing a series of statistical tests on each descriptor, comparing the value for each group with the value for those not in the group (the rest). These values are compared using Bayesian statistics to calculate the probability that the group value is greater (or lower) than the rest values. It quickly allows the user to find similarities and differences among target data groups. Specifically, Discover enables users to:

  • Profile a target group against a set of descriptor variables, such as basic demographics or habits, indicating which descriptors best describe a group, and then compare these with other groups
  • Drag and drop descriptors and demographics for comparison into the easy-to-use interface
  • Instantly access a customizable table view with the variables that stand out in the target group against others.
  • Visualize the uncovered data by color, order, bubble charts, heat maps, and proprietary 3D space graphs to show probability, commonalities and differences in digestible visual forms

Ron concludes his piece, “From a mathematical theorem that was developed nearly 300 years ago, to a foundation for market research data analysis today, Bayesian statistics hold promise for a better understanding of the massive amounts of information we now have available to us. Technology developments are making Bayesian analysis accessible for market researchers and allowing them to uncover more accurate representations of research data that is not black and white.”

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