🎯 iGaming Retention Test - Part 5

SQL Pipelines & Player Metrics

After modeling churn, it’s time to structure the data pipeline that feeds all metrics — just like in a real iGaming environment.

Main Goals of the day

• Load player data into a SQLite database
• Create SQL tables for player-level KPIs
• Aggregate sessions, deposits, and feature usage per user
• Prepare data for daily retention tracking

Step by Step

📍 Step 1: Loaded CSV data into SQLite
📍 Step 2: Created player-level aggregation table
📍 Step 3: Calculated churn and feature usage metrics
📍 Step 4: Validated the schema for analytics use

Insights

The SQL layer allows easier scaling and integration with BI dashboards.

Once structured, retention metrics can refresh automatically — daily or weekly.

Code Snippet

CREATE TABLE player_metrics AS
SELECT 
  user_id,
  user_group,
  SUM(sessions) AS total_sessions,
  SUM(deposits) AS total_deposits,
  AVG(feature_used) AS feature_ratio,
  AVG(churn) AS churn_rate
FROM player_activity
GROUP BY user_id, user_group;

Next Step

Connect SQL data to Plotly visualizations Build an interactive dashboard for real-time retention tracking