GAMWIT's Player Churn model tells you which players are likely to leave, so you can reach them with the right offer before they go. It reads how players behave, not just their deposits, and it learns from your own data.
Player 28-day churn probability
Example rows, not live data shape as in the GAMWIT platform
Why churn matters
The cheapest growth is in the players you already have. GAMWIT's Churn model shows you who is about to leave while there is still time to act.
See what your team gets5×
A new player costs about five times more to win than keeping one you already have.
Up to 95%
more profit from keeping just 5% more of your players.
Source: Harvard Business Review, as cited on GAMWIT's Player Churn model page.
A real screen from a licensed GAMWIT account, on sample data. Every player gets a churn score and a recommended campaign.
Every player, scored
Customer id, the 7-day churn call, churn probability, the campaign GAMWIT recommends, and the recent activity behind the score. Export it, or send it straight to your CRM.
514 players scored on 8 Sep 2026. Sample data.
These are sample players. In a walkthrough, we run the Player Churn model on yours.
Book a walkthroughWhat changes
If you wait for deposits to stop, the player has usually already gone. Behavior changes first: shorter sessions, a different game mix, less response to promotions.
Spotting churners
Without GAMWITTakes weeks, once deposits have dropped.
With GAMWITChurn risk shows up within a few days.
Timing
Without GAMWITPlayers often leave before anyone notices.
With GAMWITPlayers are flagged in time for an offer.
Who gets the campaign
Without GAMWITEveryone inactive, including players already gone.
With GAMWITOnly the players likely to leave.
Campaign cost
Without GAMWITLarge, expensive sends with low returns.
With GAMWITSmaller sends, aimed where they count.
Decisions
Without GAMWITBased on gut feel.
With GAMWITBased on how each player behaves.
How it works
Why churn is hard to predict
It differs by game
What signals a goodbye in slots is not what signals it in sportsbook.
It differs by operator
Your players' habits are not your competitor's players' habits.
It differs by player and market
Age, tenure, spend, currency and regulation all change how players leave.
GAMWIT trains on your own data, using over 500 ready-made player signals from its Feature Factory. It keeps adjusting as behavior shifts.
Each player gets a churn risk (Low, Medium or High) and a predicted value, so the budget goes to players who are likely to leave and worth keeping.
Fresh predictions reach Retention, Marketing and Customer Service on every run, so the journeys and offers you already built do the work.
In practice
Examples from GAMWIT's use-case guide. They show how the model is used, not a customer's results.
The post-big-win dropout
The situation
Players often leave after a large win, afraid of losing it back.
What GAMWIT does
GAMWIT flags the recent winner as high churn risk. A congratulations message goes out with a small free-spin offer on a different game.
The bad-luck streak
The situation
A loyal player loses five sessions in a row and stops logging in.
What GAMWIT does
The risk score rises on day three of inactivity. Customer support sends a cashback offer that softens the recent losses.
New player drop-off
The situation
Many new sign-ups leave once their first deposit runs out.
What GAMWIT does
GAMWIT flags first-time depositors at risk. An automated journey sends a short tutorial and a small reload bonus to that group only.
How to use the results
Quick winSet an automatic trigger for any player whose churn risk crosses your threshold, and compare against a control group to prove the return.
Try it yourself
Sign up and build your first model yourself, no setup call needed. Prefer a guided tour? Book a walkthrough instead.
Can you trust it
Every GAMWIT model comes with the same three screens: how it tested, the data behind each score, and a warning when your data changes. These are real captures from a licensed account.
How often the model was right, on the players it learned from and on players it had never seen, for churners and non-churners separately, not as one overall number.
Trained on 476 players over 772 days. On players it had never seen: 83 of 85 non-churners and 11 of 11 churners called correctly. Sample data.
The behavior behind the score. Pick any KPI the model read and see how the outcome splits across it, so the team can check the model against what it already knows.
Shown for the Player Churn model. Sample data.
Every signal the model reads is watched for change. When enough of them have drifted from what it learned on, the screen says so and recommends retraining.
Shown for the Responsible Gaming model, 267 signals. Sample data.
Getting started
The Player Churn model at a glance
What it asks of your team
Marketing, Finance, Risk and Compliance teams use it directly, without an analytics background.
Wherever and however your gaming data is stored, the GAMWIT team handles the connection. See how GAMWIT connects
Once connected, the model runs on its own and delivers fresh predictions on every run.
Customer result
10%
reduction in player churn
5%
lift in player value
A UK operator running GAMWIT's Churn and Lifetime Value models on its own players.
BizAcuity can introduce you to long-term customers.Ask for a reference
FinalistCompliance Innovator of the YearVixio · 2024
NominatedCompliance Technology ProviderEGR North America · 2024
NominatedData and AI PartnerEGR B2B · 2022 · 2023GAMWIT's Churn model is already built. Connecting your data takes 2 to 4 weeks.
