Gaming & Interactive Entertainment
Gaming studios need player intelligence — not just telemetry.
The studios that scale are not the ones with the best content alone — they are the ones that treat player data as a core operational asset. Telemetry sits unanalysed, UA spend runs without LTV modelling, and LiveOps decisions are based on intuition rather than signal. The gap between data collected and data acted on is where growth is lost.
$11.6B
Global AI in gaming market size projected by 2032
Precedence Research, 2024
70%
Of mobile game revenue comes from 2% of players — identifying them early is the growth lever
AppsFlyer, 2024
3.8×
Higher D30 retention for games with personalised LiveOps vs generic events
Unity Gaming Report, 2024
Gaming data maturity
Where most studios stall.
Five stages define the gaming data maturity curve. Most studios only operate in the first two — and wonder why player acquisition costs keep rising while retention stays flat.
Telemetry capture
Events fire everywhere but taxonomy is inconsistent and incomplete
Player profiling
Basic segmentation exists but behavioural and value-based profiling is rare
LTV modelling
Predicting lifetime value per player to optimise UA spend and monetisation
Personalised LiveOps
Dynamic events, offers, and content tailored to player segments
Predictive churn & reactivation
Identifying at-risk players and triggering retention before they lapse
Failure patterns
Recognise any of these?
UA spend scales without LTV feedback — the studio optimises for installs, not for revenue-generating players
Growth budgets are measured in cost-per-install, but the campaigns generating the cheapest installs often deliver the lowest-value players. Without LTV attribution feeding back into acquisition targeting, every scale-up amplifies waste. The unit economics look fine at the top of the funnel and collapse further down.
Telemetry events exist but no unified player model connects behaviour to monetisation and retention signals
Sessions, purchases, progression, and social interactions are logged in separate pipelines. No single player profile ties these signals together. Product and growth teams work from different slices of truth — and the discrepancies only surface when a launch underperforms and nobody can explain why.
LiveOps decisions are calendar-based, not data-driven — every player sees the same event regardless of engagement state
Events, sales, and content drops follow a fixed schedule designed around release cadence, not player behaviour. A whale in their fourth month and a lapsed user returning after six weeks receive the same offer. The opportunity cost of undifferentiated LiveOps is invisible until a competitor demonstrates what personalisation looks like.
Event taxonomy is inconsistent across games, making cross-title analytics impossible
Each game defines its own event names, parameters, and structures. When leadership asks for a portfolio view of retention or monetisation, the data team spends weeks reconciling schemas instead of answering the question. Cross-title learnings — which should be a competitive advantage — remain locked inside individual projects.
Monetisation strategy is designed once at launch and never re-calibrated based on actual player behaviour
Economy design, pricing tiers, and offer structures are set during development and rarely revisited with live data. Player spend patterns, price sensitivity, and willingness-to-pay signals are available in telemetry but never connected to economy tuning workflows. Revenue is left on the table because the feedback loop does not exist.
Churn is measured retrospectively — by the time the dashboard shows the drop, the players are already gone
Retention curves are reviewed in weekly or monthly reports. By the time a cohort's D7 or D30 number appears, the intervention window has closed. Predictive churn models that trigger automated reactivation before lapse are technically feasible with existing data — but the infrastructure to operationalise them has not been built.
The gap
Where you are vs where you could be.
Install-optimised campaigns with no downstream attribution — cost-per-install is the primary metric, disconnected from player quality and revenue outcomes
LTV-optimised acquisition with predictive cohort modelling — UA spend allocated to channels and creatives that deliver high-value players, not just volume
Basic segmentation by install date and spend tier — no real-time behavioural profiling or value scoring across engagement dimensions
Real-time behavioural profiling with value scoring — unified player models that connect session behaviour, progression, social activity, and monetisation signals
Calendar-based events and blanket offers — every player receives the same content regardless of engagement state, value segment, or lifecycle stage
Dynamically personalised content and offers — event targeting, offer timing, and reward calibration driven by player segment, behaviour signals, and predicted response
Retrospective churn reports reviewed weekly — intervention happens after players have already lapsed, with no automated recovery mechanism
Predictive early warning with automated reactivation — at-risk players identified before lapse, with triggered re-engagement sequences tailored to churn reason
What we build
The player intelligence your studio deserves. Engineered.
We build the data infrastructure, analytics pipelines, and decision systems that gaming studios need to move from raw telemetry to actionable player intelligence — so every growth, LiveOps, and monetisation decision is grounded in what players are actually doing.
Event taxonomy & telemetry
Standardised event design, pipeline architecture, and data quality frameworks that make telemetry queryable and consistent across titles
Player intelligence layer
Behavioural profiling, value scoring, and segment modelling that unify session, progression, social, and monetisation signals into a single player view
LTV & cohort modelling
Predictive lifetime value models, UA attribution systems, and ROAS optimisation that connect acquisition spend to downstream revenue outcomes
LiveOps decision engine
Dynamic event targeting, offer personalisation, and A/B testing infrastructure that tailors content and rewards to player segments in real time
Churn prediction
Early warning models, automated reactivation triggers, and lapse prevention systems that identify at-risk players before they disengage
Growth dashboards
UA performance, monetisation health, retention curves, and cross-title views — decision-oriented analytics, not just reporting
Start a discovery
Your players are telling you everything. Your systems are not listening.
A 30-minute diagnostic conversation. No proposal before we understand the system. No commitment before we demonstrate the value.
For studio and product leadership
Player intelligence systems that make LiveOps, content, and economy decisions faster and more defensible. Clear visibility into what drives retention and revenue — not just what happened last week.
For data and LiveOps teams
Production-grade analytics infrastructure, not ad-hoc queries. Unified event taxonomy, real-time player profiles, and decision engines that turn telemetry into operational action.
Relevant services
Capability areas we most often combine for this context.