🎯 iGaming Retention Test - Part 1

From Players to Patterns

I’ve started a new piece of the iGaming analytics project — focused on player retention and promo testing.

Main Goals of the day

• Build an A/B test simulation on player promos (NEWUSER10 vs control)
• Use survival analysis to model retention over time
• Create SQL pipelines for session aggregation and player-level metrics

Step by Step

📍 Step 1: Generated synthetic player activity over 4 weeks
📍 Step 2: Flagged promo-exposed players (treatment) vs control
📍 Step 3: Modeled survival function using Lifelines Kaplan-Meier
📍 Step 4: Visualized retention curves and promo impact

Insights

Retention uplift detected around day 10 for promo users.

Next goal: validate if the difference is statistically significant — using the log-rank test.

The interesting part?
Retention behavior in gaming is non-linear: you have short bursts of engagement, then long periods of silence. The model needs to reflect that.

Code Snippet

from lifelines import KaplanMeierFitter

kmf = KaplanMeierFitter()
kmf.fit(durations=treatment['days_active'], event_observed=treatment['active'])
kmf.plot_survival_function(label="Promo Group")

Next Step

Validate statistical significance of retention difference Add monetization variable (GGR / session duration) Create an automated SQL + Python workflow for periodic tests