Leveraging Trends in Online Searches for Product Features in Market Response Modeling

Journal of Marketing · 2015 · Volume 79 · Issue 1

Abstract

Evolving tastes can change the relative importance of product features in shaping consumers' purchase decisions, which in turn can shift the relative attractiveness of products with different feature levels. The challenge lies in finding a reliable yet cost-effective way to monitor the weights consumers place on various product features. In the context of the U.S. automotive market, the authors explore the potential of using trends in online searches for feature-related keywords as indicators of trends in the relative importance of the corresponding features (e.g., fuel economy, acceleration, cost to buy, cost to operate, body type). By augmenting marketing-mix data with feature search data in a market response model, they show substantial improvements in goodness-of-fit both in and out of sample. The authors find empirical support for the hypothesis that feature search trends are positively correlated with feature importance trends. They discuss how managers may make better decisions by monitoring feature search trends and leveraging those trends strategically.

Keywords

Google trends, market response model, product feature, conjoint analysis, big data

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