Businesses and their analytics advisers are breaking new ground in using consumers’ online and offline activity to determine how best to shape a competitive advantage. But increasing concerns about privacy mean the data often is stripped of identifying information.
A soon-to-be-published paper from Bauer College Marketing Professor Rex Du and Associate Professor Ye Hu provides a technique for predicting which vehicles are jointly considered by car buyers, even in the absence of information identifying which shopper has shown interest in which vehicle.
Researchers examined data collected through online vehicle shopping sites by a company called Autometrics. Its dataset consists of hundreds of millions of records, capturing consumer shopping interest for different vehicle makes and models, and identifying only the time of the search and each shopper’s zip code.
The researchers’ findings have the potential to help businesses better understand the competitive landscape in the automotive industry, which in turn can help them make better decisions about product design, pricing and promotions that encourage sales.
“Uncovering Patterns of Product Co-Consideration: A Case Study of Online Vehicle Price Quote Request Data,” is forthcoming in the Journal of Interactive Marketing.
By Julie Bonnin