About

This project analyzes user behavior data on Zerve to identify what drives long-term success.

By transforming event-level data into user-level insights, the analysis focuses on key behavioral metrics such as total activity, diversity of actions, and interaction with core platform features like agent workflows and canvas operations.

Users were classified as successful based on engagement depth and activity patterns. The results show that users who actively interact with multiple features and perform diverse actions are significantly more likely to succeed.

Key insights include:
- Higher activity strongly correlates with success
- Behavioral diversity is a major success indicator
- Agent-based and canvas interactions are common among successful users

This project demonstrates how behavioral analytics can be used to understand user success and improve product engagement strategies.