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 consumers’ purchase intentions, which is critical for marketing success. In this paper, we develop an integrated model to connect the latent topics embedded in consumers’ search phrases to their website visits and purchase decisions. Using a unique dataset containing more than 8,000 search phrases submitted by consumers, our model identifies latent topics underlying the searches that led consumers to the firm’s website. Compared to 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, our 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. We also extend our discussion to aggregators, affiliated websites, and segments of consumers who are exposed to the firm’s outbound ads. Marketing managers can use our method to extract structured information from consumers’ search phrases to facilitate their inference of consumers’ latent purchase states and thereby improve marketing efficiency.

Keywords: path to purchase, search phrase, textual analysis, topic model, hidden Markov model.