Data Requirements
During data onboarding, Anthem requests a sample dataset from operators to ensure that all necessary fields for effective modelling and analytics are present. Essential data elements typically include timestamp details, player and bet slip identifiers, player registration information, and comprehensive bet details—such as bet amount, payout, bet slip status, bet type (e.g., prematch or live, single or acca), and sport type. Optional data elements may include league, event, and market information, along with deposit/withdrawal records, bonus data, and customer demographics.
For detailed instructions on how to integrate your data with Amplifier AI, refer to our Data Integration Guide
Data Delivery
The API payload provides operators with a real-time breakdown of churn predictions, enabling timely and targeted interventions. Amplifier AI's churn models process bet settlement data instantly, assessing player experience through attributes tailored to each operator’s data and preferences. Key player experience attributes include indicators like bet frequency, stake amounts, bet type, and cumulative losses, all of which are used to evaluate churn risk.
If a player is flagged as a potential churner, the system assigns a specific reason code—such as Bad Beat, Significant Loss, or Reduced Activity—which details the likely cause of churn. The API payload example below demonstrates how these insights are relayed back to the operator, including essential data points like player ID, session key, and player experience metrics:
{
"reasonCode": 2,
"schema": 3,
"playerId": 2620637,
"sessionKey": "527493046",
"expirationTime": 1670317250278,
"playerExp": {
"betId": "527493046",
"selectionIds": "1163504926,1163504927",
"lossPct": 0.25,
"bets24h": 1,
"betsPerDay": 5,
"activeDays": 7,
"activeDaysPerWeek": 4,
"stakes24h": 3.32,
"stakePerDay": 18.7,
"stakeAmount": 4.4,
"betType": "AKO",
"totalOdds": 2.18,
"lossAmount": -5,
"betsLost": 3,
"betsLostLossAmount": -15
}
}
All player attributes, experiences, and reason codes can adapt based on data availability, the type of data operators provide, and the operator's specific needs.
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