Groundbreaking AI Model Enhances Customer Behavior Prediction in Marketing

Marketing researchers from the University of Maryland's Robert H. Smith School of Business have unveiled a pioneering AI-driven model that significantly improves the ability to predict digital customer behavior. This innovative approach gives marketers a powerful tool to deliver personalized insights across intricate, multi-touchpoint customer journeys, demonstrating superior precision and return on investment when contrasted with traditional marketing methodologies.

The model, set to be detailed in an upcoming issue of the Journal of Marketing Research under the title "AI for Customer Journeys: A Transformer Approach," adopts transformer-based technology originally formulated for language processing. By doing so, it allows for a comprehensive analysis of complex, multi-channel sequences involving customer interactions. According to P.K. Kannan, the Dean's Chair in Marketing Science at UMD Smith and a co-author of the study, this transformative shift in marketing analytics enables researchers and practitioners to view customer journeys holistically rather than as a series of independent interactions.

The researchers noted that their model stands apart from traditional approaches, which often rely on methods like Long Short-Term Memory (LSTM) networks, Hidden Markov models, and Poisson Point Process models. These conventional models sometimes fail to adequately capture the nuanced timing and nature of each customer touchpoint, particularly in today's fragmented marketing landscape. Kannan and marketing PhD candidate Zipei Lu emphasized that the new transformer model is equipped to provide a more accurate reflection of the increasingly multifaceted customer interactions that define contemporary marketing.

A pivotal feature of this research is its integration of customer-level heterogeneity within the transformer architecture. This advancement enables the model to generate individualized insights regarding how various customers react to marketing initiatives over time. Lu affirmed that they meticulously designed the model to encapsulate the complexity and uniqueness inherent in digital customer journeys, a facet often overlooked by traditional models.

Kannan added that the model's ability to incorporate customer diversity marks a departure from the conventional