Games produce some of the richest behaviour data anywhere — sessions, matches, purchases, progression and social graphs. Our recommendation and interest-evolution research maps directly onto player modelling, so studios can act on player behaviour instead of just logging it.
Model how each player's taste in genres, modes, maps and items evolves across sessions and titles over time.
Rank opponents, teammates, lobbies and content using graph and sequence signals — not a single static skill number.
Spot players drifting away before they leave, with confidence values that LiveOps and CRM teams can act on.
Top-K recommendations for items, bundles, cosmetics, events and modes, tuned to each player's recent behaviour.
Cohort-aware personalisation for offers, events, difficulty, onboarding and re-engagement campaigns.
Connect the same player across PC, console, mobile and storefronts, and model behaviour that spans devices.
Player behaviour is relational (who plays with whom, which items pair with which modes) and sequential (what happened this session versus last month). That is exactly the structure our cross-platform interest models are designed for — which is why the research transfers with very little adaptation.
Players, squads, items, modes and maps as connected structure.
Recent matches and actions signal current intent.
Level, spend, tenure and account features.
Churn risk, recommendations and match quality.
Matchmaking, retention, personalisation or store recommendation — we can scope a research plan around your data.
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