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Halowerk Player Churn Risk Scorer
x402
Halowerk Player Churn Risk Scorer is a paid API for AI agents from gaming.halowerk.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Computes a scenario-based churn risk score for a game player using fixed coefficients applied to recency, session trend, playtime, support tickets, and recent purchases.
Price / call$0.003
Success—
StatusUnknown
How this service works
Applies fixed coefficients to recency, session trend, playtime, support tickets, and recent purchases. It is an untrained scenario score, not an individualized prediction, psychological profile, causal model, or recommendation for manipulative retention.
Output
Returns a computed churn risk score based on weighted fixed coefficients across recency, session trend, playtime, support activity, and purchase behavior — indicating how likely a player is to churn under defined scenario assumptions. Not a machine-learned model or individualized prediction.
Example use cases
Daily at-risk player triage
“Run a churn risk score for player xyz-789 — they had 2 sessions this month vs 15 last month, average 10 minutes per session, haven't logged in for 18 days, made 0 purchases in the last 90 days, and submitted 3 support tickets recently.”
Post-purchase engagement check
“Score the churn risk for player gamer-456: 20 sessions in the last 30 days, 18 the previous 30, 45 average session minutes, last played 1 day ago, 5 purchases in the last 90 days, and 0 support tickets.”
Lapsed player win-back prioritization
“Give me the churn risk for player ghost-001 who had 0 sessions this month versus 8 last month, averaged 30 minutes per session historically, hasn't logged in for 60 days, made no purchases in the last 90 days, and raised 1 support ticket.”
Technical details
Endpoint
POSThttps://gaming.halowerk.com/v1/player-churn-riskWhen to prefer
Choose this endpoint when you need a fast, deterministic, rule-based churn risk score for a player using behavioral telemetry you already have — no ML model or training data required. Ideal for lightweight pipelines, daily batch scoring, or when you want transparent, fixed-coefficient computation rather than a black-box prediction.
Side effects
- Missing required player_id returns a validation error
- Out-of-range numeric values (e.g. sessions exceeding 10000) trigger schema validation failure
- All-zero inputs may return a low-signal baseline score rather than an error
- Non-numeric values for numeric fields cause a 400 bad request
- Negative values for session or purchase counts are rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| player_id | string | |
| sessions_last_30_days | integer | |
| purchases_last_90_days | integer | |
| average_session_minutes | number | |
| days_since_last_session | number | |
| sessions_previous_30_days | integer | |
| support_tickets_last_30_days | integer |
Tags
x402
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