Marketing Research Seminar Spring 2026
Does a Shift in Satisfaction Predict a Shift in Spending and Retention? Transaction-Level Evidence from the Casino Gaming Industry
Companies routinely track customer evaluations after purchases, yet whether a change in these scores for the same customer predicts a change in behavior remains largely untested. Most prior work examines these metrics cross-sectionally or at the firm level, overlooking within-customer attitudinal dynamics. We address this gap by linking consecutive survey responses to behavioral outcomes using a three-window design that measures changes in spending and return behavior while controlling for prior behavioral trajectories. We present model-free evidence across attitudinal transition groups and estimate changes in spending and recency using changes in evaluation over time with customer fixed effects. This trajectory-to-trajectory approach provides a stronger test of what common feedback metrics actually signal about individual loyalty
Impact of Self-Disclosure on Viewer Engagement in Digital Platforms using Text & Image Analysis
This research examines how verbal and visual self-disclosure by content creators influences viewer engagement in digital product review videos. From the platform’s products review videos across different industries, we analyzed video transcripts and thumbnails using natural language processing (NLP) and large language models (LLMs), integrating features into machine learning models to predict viewer’s engagement metrics. Results show that verbal and visual self-disclosure independently increase engagement, with the combination producing the strongest effect, and effects generalize across industries. Findings offer guidance for content strategies and highlight potential applications for AI-driven influencers.
