Player Intelligence & Data
Intelligence & RiskPhase 1Lead: Intelligence Orchestrator Agent
Architecture Pattern
View full details →Intelligence Orchestrator assigns analysis tasks. Segmentation, LTV, and behavior agents run in parallel on player data. Batch operations process large datasets.
Foundation. Every other agent depends on player segments, LTV, and churn scores. Must be built first.
Tools
Goals
Agent Sizing Rationale
8 agents: 1 orchestrator + 3 analytics specialists (odd for segmentation voting) + 4 BI/data (including batch operations). Segmentation decisions use 3-agent consensus to avoid bias.
Player Analytics (3-agent panel)
Business Intelligence & Data (3 specialists + orchestrator)
Agents Used From Other Departments
These agents from other departments feed data into or are called by this department's agents.
Risk scores enrich player profiles and inform churn prediction.
Game activity data is a primary input for player segmentation.
Betting activity data feeds into player analytics and LTV models.
Attribution data from Marketing feeds into player acquisition cost analysis and channel-level LTV calculations.
Churn intervention outcomes and retention campaign data feed back into predictive models, improving churn prediction accuracy.
Data privacy requirements constrain what player data can be collected, how long it is retained, and what models can be built from it.