Game Analytics Platform

Event schema design for your game's specific player actions -- session start and end, level attempt and completion, match result, item purchase, social interaction, and any other events your game team needs to analyse. Client SDK for your game engine that handles event batching, local queuing for offline play, and transmission when connectivity is available. Server-side event validation ensuring client-reported events have the required fields and plausible values before ingestion. High-throughput event ingestion pipeline handling the volume of events a large concurrent player population generates without affecting game performance. Event storage in a queryable data warehouse with the retention period your analytics team needs. The event pipeline that turns player actions into analysable data without the game client team writing data pipeline code.

Retention cohort analysis tracking the percentage of players who return on day 1, day 7, and day 30 after their first session, segmented by acquisition channel, game version, geographic region, and device type. Rolling retention tracking the return rate over a moving window rather than just the fixed-day cohorts. Engagement depth metrics measuring session frequency, session length, and the player actions per session that indicate engaged vs passive play. Reactivation analysis showing how many churned players return after absence and what triggers their return. Feature engagement tracking showing which game features are used by which player segments. The retention analytics that give the game team evidence for where the game is losing players and which segments are most and least sticky.

Funnel definition for any progression sequence in your game -- the tutorial steps, the level sequence, the unlock pathway, or any multi-step flow where understanding drop-off is important for design decisions. Funnel visualisation showing the completion rate at each step and the absolute player count at each stage. Time-to-complete analysis showing the distribution of time players spend at each funnel step -- identifying steps where players stall rather than quit. Segmentation overlays showing whether drop-off patterns differ by device, acquisition channel, or player segment. A/B test funnel comparison for testing progression changes with the statistical significance reporting your product team needs. The funnel analysis that makes progression design an evidence-based process rather than an intuition-driven one.

Currency source and sink balance showing the ratio of currency being generated (gameplay rewards, purchased currency) to currency being consumed (purchases, spend sinks) across the player population. Inflation and deflation detection with alert thresholds that fire when the balance moves beyond the design parameters -- giving the game team time to adjust before players notice. Conversion rate tracking from active player to first purchaser, and from first purchaser to repeat purchaser. ARPU and ARPPU trends with the player segment breakdown that shows which populations are monetising above and below the average. Item sell-through analysis showing which catalogue items are purchased and which are ignored. Drop rate validation comparing actual drop rates from loot systems against configured rates -- catching the bugs that cause items to drop at unintended frequencies.

Live ops event metrics showing player participation rates, economy impact, and retention change for each live service event. Matchmaking quality metrics tracking skill differential between matched teams, wait time by region and game mode, and match completion vs abandonment rates. Server performance metrics showing latency and error rates by region correlated with player experience signals. Content release impact analysis comparing retention, engagement, and economy metrics before and after each major update. Live service health dashboard giving the operations team a real-time view of the metrics that indicate the game is running normally vs showing early warning signals of a problem. The live service analytics that make operating a live game a monitored system rather than a reactive exercise.

Dashboard design for the specific reports your game team uses regularly -- daily health report, weekly retention review, economy balance report, and live event post-mortem. Scheduled report delivery sending the key metrics to the game team's communication channels on a configured schedule without anyone needing to log into a tool. Data export for the cases where your analytics team wants to query the raw data directly. Integration with your existing BI tooling for organisations with an existing data stack. Custom report builder for game teams that want to build their own analyses without engineering involvement. The reporting infrastructure that makes game analytics a routine part of the game team's workflow rather than an occasional engineering project.