arXiv:2605.29018v1 Announce Type: new Abstract: Although a growing body of research has begun to describe user--LLM interactions, the picture it paints is largely static; little is known about how individual users change their behavior over time. To address this gap, we analyze the conversational trajectories of $\sim$12,000 randomly sampled Microsoft Bing Copilot users and compare these with data from WildChat-4.8M. While the Copilot data contains significant population-level trends, we find that trends in individual user trajectories are much weaker; user habits prove to be overwhelmingly st
This analysis emerges as LLM adoption expands globally, making longitudinal user studies crucial for understanding sustained engagement and user adaptation patterns.
A strategic reader should care about understanding user retention and habit formation in LLM interactions, as it directly impacts product development, feature prioritization, and market penetration strategies.
The study reveals that while population-level trends exist, individual user behaviors with LLMs are remarkably stable, suggesting a slower pace of 'adaptation' than 'adoption'.
- · LLM developers focusing on core utility and initial user experience
- · Analytics platforms tracking long-term user behavior
- · Researchers studying human-computer interaction
- · LLM developers banking on rapid, continuous user flow changes
- · Platforms struggling with user retention and engagement
- · Hyperscalers over-investing in adaptive feature engineering
Ongoing LLM product development may shift focus from frequent feature changes to refining core functionalities and improving the initial onboarding experience.
This could lead to a 'stickiness' emphasis in LLM design, prioritizing robust foundational capabilities over a constant stream of new, potentially underutilized features.
The market might stratify, with some LLMs excelling in initial adoption due to novelty, while others thrive on long-term user retention driven by core utility and subtle, effective improvements.
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Read at arXiv cs.AI