Inspiration
Our goal was to analyze real-world interaction data from the Federato RiskOps platform to identify patterns that drive higher engagement and longer session times.
What it does
Our project explores user activity data to detect trends in retention, session duration, and engagement levels. By analyzing event-based interactions—such as page views, button clicks, and form submissions—we provide insights into what actions lead to prolonged platform usage.
How we built it
- Data Processing: We worked with large-scale CSV files, filtering key user events.
- Analysis & Visualization: We used Python (Pandas) to clean, and explore engagement patterns.
- Pattern Recognition: We identified the impact of different actions (e.g., dashboard views, widget interactions) on session time.
Challenges we ran into
- Handling large datasets efficiently without running into performance issues.
- Identifying meaningful engagement metrics from a complex event dataset.
Accomplishments that we're proud of
- Successfully processed and analyzed millions of user interactions.
- Discovered key engagement drivers, which can be leveraged to improve retention.
What we learned
- How user interactions can be quantified to measure engagement.
- The challenges of real-world SaaS data processing and how to optimize it.
What's next for Federato
- Building an interface and further analyzing the data
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