Charting the Path to Purchase Using Topic Models

Abstract

In gathering information for an intended purchase decision, consumers submit search phrases to online search engines. These search phrases directly express the consumers’ needs in their own words and thus provide valuable information to marketing managers. Interpreting consumers’ search phrases renders a better understanding of their purchase intentions, which is critical for marketing success. In this article, the authors develop an integrated model to connect the latent topics embedded in consumers’ search phrases to their website visits and purchase decisions. Using a unique data set containing more than 8,000 search phrases submitted by consumers, the model identifies latent topics underlying the searches that led consumers to the firm’s website. Compared with a model lacking any textual information from consumers’ search phrases, a model using textual data in a heuristic approach, and a model based on the latent Dirichlet allocation, the proposed model provides a better evaluation of a consumer’s position on the path to purchase and achieves much better predictive accuracy, which could in turn substantially increase the firm’s revenue. The authors also extend the discussion to aggregators, affiliated websites, and segments of consumers who are exposed to the firm’s outbound ads. Marketing managers can use this method to extract structured information from consumers’ search phrases to facilitate their inference of consumers’ latent purchase states and thereby improve marketing efficiency.