Behavioral infrastructure for
high-value account retention
Exchanges and sportsbooks know who their most valuable accounts are. They cannot see which of those accounts is cooling. One API returns a stability score, a state, and the behavioral reason behind it.
Revenue sits with a handful of accounts. The ones worth keeping leave quietly.
Exchanges and sportsbooks are staffing account-manager teams around their most valuable accounts right now. What they cannot see is which of those accounts is cooling, across a book too large to call one by one.
Loud exit
Escalating position sizes, loss chasing, mid-session deposits. Visible in a dashboard, eventually. Some platforms catch part of it.
Quiet exit
Withdrawals rising, sessions thinning, stakes shrinking, balance moving out. Invisible until it is over. CRM churn models catch it late and cannot say why.
By the time churn shows up in a dashboard, the balance is already gone.
Top 10% of accounts generate 79% of revenue. Forrest & McHale, Journal of Gambling Studies (2024), N ≈ 139,000 UK online accounts. One US sportsbook drew over 70% of revenue from 0.5% of customers, Wall Street Journal (2024).
How it works.
Send your session stream
Trades, deposits, withdrawals, and resolutions through one REST endpoint. No SDK. About a week of integration.
Score the high-value book
The platform tags which accounts sit in the high-value book. Every event is evaluated against that account's own baseline. Score, state, alert tier, and ranked drivers return in one to two seconds.
Route to the account manager
Alerts land in the account-manager queue with the three behavioral drivers behind them. The operator sets the queue threshold to match their headcount. We surface. The platform acts.
We never contact the trader, never block a trade, and never generate a marketing list.
The same accounts, the same behavior, two markets that both concentrate revenue.
Prediction markets
$42B in US volume across six native exchanges. No behavioral vendor embedded anywhere. No gatekeeper, direct founder access. The account-manager function is being built right now.
Sportsbooks
$167B in US handle in 2025 on $16.96B of revenue. VIP host teams already staffed and budgeted. We sit beside the CRM, we do not replace it. Same engine, recalibrated per operator.
One API, one signal set. Only the calibration and the vocabulary change.
US sports betting handle and revenue: American Gaming Association Commercial Gaming Revenue Tracker, full-year 2025. Prediction-market volume per Pew Research / The Block (Apr 2026) and Kalshi volume implied from 2025 fee revenue.
One POST request. A score, a state, and the reason behind it.
POST /v1/stability-score
Host: api.playmindlabs.com
Authorization: Bearer sk_live_••••••••••••••
Content-Type: application/json
{
"account_id": "acct_4471",
"events": [
{ "type": "trade", "stake": 250, "market": "btc-15m-2026-08-05" },
{ "type": "resolution", "pnl": -250 },
{ "type": "withdrawal", "amount": 4000 }
]
}200 OK
{
"account_id": "acct_4471",
"stability_score": 31,
"state": "disengaging",
"alert_tier": "elevated",
"drivers": [
{
"signal": "cumulative_drawdown",
"detail": "equity 38% below peak, no deposit in 19 days",
"contribution": 0.41
},
{
"signal": "stake_contraction",
"detail": "average stake down 61% against 30-day baseline",
"contribution": 0.29
},
{
"signal": "session_gap_widening",
"detail": "4.2 days between sessions, against a baseline of 1.0",
"contribution": 0.18
}
],
"routing": "account_manager_queue"
}Alert routing
| Tier | Score | Action |
|---|---|---|
| Stable | Above 65 | No alert |
| Watch | 40 to 65 | Weekly cohort digest |
| Elevated | 20 to 40 | Enters account manager queue |
| Critical | Below 20 | Real-time alert with drivers |
The operator sets the queue threshold to match their account manager headcount. Scoring is deterministic. No model call sits in the request path.
Why PlayMind?
They already know who. They do not know why.
A risk score on its own is something an analytics team can approximate from recency and frequency. What no standard model produces is a reason an account manager can act on. That is a phone call the host can open. A probability is not.
Calibrated on their book, not ours
Every operator tunes signal baselines against their own historical data behind a human approval gate. No cross-operator data joins, ever.
Beside the CRM, not instead of it
We do not own the customer relationship, the outreach, or the offer. We produce the signal and hand it to the team that already exists.
0.737 AUC under 5-fold cross-validation on 2,067 real Polymarket wallets. Frequency acceleration inverted against the gambling literature: fast trading predicts retention here, not churn. Result is on the general trader population. The high-value cohort cut is in progress. Positioned as a transparent risk index with a calibration plan, not a calibrated churn probability.
See which of your high-value accounts is cooling.
Free 30-day shadow-mode pilot. Silent scoring across your high-value book, no user impact, no changes to the trading path. At day 30 we score 6 to 12 months of your own historical data and show which accounts we would have flagged before they left.
Or reach us directly at info@playmindlabs.com