This tutorial illustrates an innovative market research workflow for deriving marketing and product planning priorities from auto buyer surveys. In this study, we utilize the Strategic Vision New Vehicle Experience Survey, which includes, among many other items, customers’ satisfaction ratings with regard to over 100 individual product attributes.
With traditional statistical methods, it has been difficult to rank the importance of individual product attribute ratings with regard to an overall measure, such as repurchase loyalty.
The key challenge is that customers’ ratings of individual product attributes are highly correlated. When plotted, we see 100 lines that are nearly indistinguishable in terms of their slope. Given this collinearity of all variables, traditional statistical methods fail to distinguish the importance of individual ratings. We could only naively conclude that an improvement in any rating would generally be associated with higher loyalty. No clear priorities could be established on such a basis.
To overcome this problem we employ an alternative framework: we use Bayesian networks as the mathematical formalism, plus the machine-learning and optimization algorithms of the BayesiaLab software package. This approach embraces collinearity as a feature in the model, instead of suppressing it as a nuisance.
First, using BayesiaLab, we machine-learn a Bayesian network that models customers’ brand loyalty as a function of their ratings of their current vehicle. This identifies key factors as loyalty drivers in the overall market, at the segment level, and finally at the model level. With these factors identified, we perform optimization, for each vehicle within its competitive context. As a result, we obtain a list of specific priorities for each vehicle, along with the simulated gain in loyalty.
Many modeling techniques offered in the field of marketing science are opaque to the end user of the research. The nature of many models make them inherently black-box, and thus require a leap of faith by the decision maker.
Not so in our research framework with Bayesian networks. Regardless of one’s quantitative skills, any subject matter expert can—by simply using common sense—interpret the Bayesian network models generated with our workflow. Any stakeholder can immediately scrutinize such a model, thus enabling him to verify its structure, or, by using his domain knowledge, to invalidate it. Their inherent falsifiability makes Bayesian networks ideal scientific tools.
In most organizations, waiting for research results and their interpretation is a matter of months. The time span between a consumer sentiment expressed in a survey, and a company’s response, can sometimes even exceed the lifecycle length of a product.
Our workflow creates a single, direct, and transparent link from data to recommendation. This directness provides unprecedented analysis speed. We reduce the lag between receipt of data and delivery of recommendation from months to days. As a result, near real-time policy recommendations are feasible for the first time.