Federato RiskOps Platform Optimization Challenge

Inspiration

User retention is a critical challenge for any digital platform. We wanted to explore how predictive modeling, graph-based analysis, and causal inference could help optimize engagement on Federato’s RiskOps platform. Our goal was to identify key user behaviors that drive retention and develop actionable recommendations to enhance the overall user experience.

What it does

Our project analyzes user session data to determine factors influencing engagement, session duration, and feature adoption. We built a data-driven recommendation system that:

Predicts user actions and drop-off points.
Identifies key friction areas that impact retention.
Provides real-time suggestions to improve engagement and feature utilization.

How we built it

Exploratory Data Analysis (EDA): Cleaned and visualized user session data to uncover key trends.
Predictive Modeling: Used Random Forest and XGBoost to predict user actions and engagement patterns.
Graph-Based Analysis: Mapped user journeys to identify high-impact interactions.
Causal Inference: Applied propensity score matching to determine causal relationships between actions and retention.
Real-Time Recommendations: Designed engagement nudges and UI enhancements to improve feature adoption.

Challenges we ran into

Handling large datasets efficiently while ensuring accurate insights.
Differentiating correlation from causation when analyzing user behaviour.
Balancing engagement improvements without overwhelming users with notifications.
Optimizing recommendations for both guest and registered users with different behaviours.

Accomplishments that we're proud of

Identified key retention drivers, such as optimal engagement times and friction points.
Developed a real-time recommendation system to enhance user interactions.
Provided actionable insights for increasing guest-to-registered user conversions.
Created an optimization framework that can scale with Federato’s platform growth.

What we learned

The importance of combining predictive analytics with user experience design.
How session timing, navigation patterns, and feature interactions influence retention.
Best practices for implementing A/B testing to evaluate engagement strategies.
The value of real-time insights in shaping platform usability.

What's next for Federato RiskOps Platform Optimization Challenge

Implementing A/B testing to validate the effectiveness of engagement nudges.
Refining the recommendation engine with more advanced deep learning techniques.
Expanding cohort analysis to develop personalized user engagement strategies.
Enhancing onboarding experiences to boost new user retention.
Exploring additional behavioural metrics to further improve platform stickiness.