Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
By: Olivier Jeunen, Schaun Wheeler
Marketing and product personalisation provide a prominent and visible use-case for the application of Information Retrieval methods across several business domains. Recently, agentic approaches to these problems have been gaining traction. This work evaluates the behavioural and retention effects of agentic personalisation on a financial service application's customer communication system during a 2025 national tax filing period. Through a two month-long randomised controlled trial, we compare an agentic messaging approach against a business-as-usual (BAU) rule-based campaign system, focusing on two primary outcomes: unsubscribe behaviour and conversion timing. Empirical results show that agent-led messaging reduced unsubscribe events by 21\% ($\pm 0.01$) relative to BAU and increased early filing behaviour in the weeks preceding the national deadline. These findings demonstrate how adaptive, user-level decision-making systems can modulate engagement intensity whilst improving long-term retention indicators.
Similar Papers
The Personalization Paradox: Semantic Loss vs. Reasoning Gains in Agentic AI Q&A
Information Retrieval
Makes AI tutors give better, personalized advice.
AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups
Social and Information Networks
AI helps groups form or stay together.
The Adoption and Usage of AI Agents: Early Evidence from Perplexity
Machine Learning (CS)
Helps AI assistants learn what people need.