Scholarly Insights: AMA’s digest of the latest findings from marketing’s top researchers
Monitoring consumer searches with free tools like Google Trends enables marketers to understand who their customers are and what they are looking for. A recent study from the Journal of Marketing examined the predictive power of searched keywords, giving insight into how marketers can better measure, analyze and apply big data to their efforts.
Using automobile sales in the U.S., the study looked at online search trends to compare the significance of searches relating to a car’s features to those of a car’s brand name. The results concluded that feature-searches were notably more accurate in forecasting sales.
“Although brand search trends may reveal which brands are gaining or losing consumer interest, they do not indicate why brand popularity is shifting. In contrast, feature search trends can reveal at a more fundamental level how the underlying preference structure may have evolved,” write authors Rex Yuxing Du, Ye Hu and Sina Damangir. While brand searches only track a brand’s shift in popularity, feature search trends offer insight into shifting consumer tastes.
Here’s how marketers can utilize big data and search-trend monitoring:
Product Design
Tracking feature-search trends allows managers to design the product consumers want. By staying up to date on what consumers are looking for online, managers can better determine what a new product should feature.
Budgeting
The possibility of sales forecasting through monitoring search trends allows managers to optimize budgeting allocations.
Advertising
Search trends regarding features may also help managers to design advertising that highlights popular features.
Production Planning
Sales forecasting with search-trend monitoring gives managers the ability to anticipate demand for a product while providing insights useful to production planning.
Article Citation:
Rex Yuxing Du, Ye Hu and Sina Damangir (2015) “Leveraging Trends in Online Searches for Product Features in Market Response Modeling.” Journal of Marketing: January 2015, Vol. 79, No. 1, pp. 29-43.