Mining longitudinal user sessions with deep learning to extend the boundary of consumer priming

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Priming is challenging when consumers start shortlisting products before the final purchase. This is because this shortlisting process is performed in multiple user sessions online across time, the shortlist does not stay as a static list, and product comparison in this stage uses the heuristics internal to individual consumers. The goal of this study is two folds: (1) to approximate user heuristics after B&B product shortlisting using NLP and deep learning techniques, and (2) to identify optimized deep learning models for the representation of key elements of consumer heuristics. This offers an extension of the priming theory into product comparison and shortlisting stages that were traditionally difficult for marketers to tap into. By analyzing the B&B product information repeated visited in user sessions, the formation of shortlists is identified and products in the shortlists can then be compared. Subsequent priming and promotions can therefore be performed closer to the actual purchase. Our study also provides marketers keywords and their associated activated words relevant for crafting marketing messages. As these activation words are extracted from the B&B sites and product reviews that the users had visited repeatedly in long-term tracking sessions, they are analogous to effects produced from user participatory design, an approach popular in the IT world. Our work shows that opportunities for marketing decision support, especially into the shortlisting phase, are now possible through machine learning techniques. Both theoretical and practical implications are provided.