🎲 Casino Analytics Dashboard - Part 6

SQL Cohort retention

I wrote the SQL cohort analysis to find who’s about to leave.

Main Goals of the day:

• Write SQL to calculate intra-week retention (Day 0 to Day 6)
• Focus on first deposit date as cohort anchor
• Don’t do “monthly” — do weekly, because my data is 7 days

Step by Step

📍 Step 1: Found first deposit date per player → MIN(session_start) where deposit > 0
📍 Step 2: Joined back to sessions → now I know: “Player P0123 deposited on Sep 2”
📍 Step 3: Calculated day_diff = session_date - cohort_date
📍 Step 4: Filtered to day_diff BETWEEN 0 AND 6 → only sessions within 7 days of first deposit
📍 Step 5: Grouped by cohort_date + day_diff → counted active players per day

Challenges / Insights

Cohort analysis is used for monthly retention. I tried to adapt it according to the simulated dataset in the window you have.**

With this method, you could see the effects on a 7-day cohort of the marketing campaigns.

And guess what?
It’s more useful.

Because in iGaming:

• If a player doesn’t return by Day 4 → they’re gone.
• If they return by Day 2 → they’re hooked.

This isn’t academic, but survival.

SQL Snippet Final

WITH first_deposit AS (
  SELECT player_id, MIN(session_start) AS cohort_date
  FROM player_sessions
  WHERE deposit_amount > 0
  GROUP BY player_id
)
SELECT 
  DATE(fd.cohort_date) AS cohort_date,
  DATEDIFF(ps.session_start, fd.cohort_date) AS day_diff,
  COUNT(DISTINCT ps.player_id) AS active_users
FROM player_sessions ps
JOIN first_deposit fd ON ps.player_id = fd.player_id
WHERE ps.session_start BETWEEN fd.cohort_date AND fd.cohort_date + INTERVAL '6 days'
GROUP BY 1,2
ORDER BY 1,2;

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Next Step

👉 Generate the heatmap in Python → show it visually.