Analytics & KPI Framework
A decision-grade metric architecture spanning definitions, ownership, dependencies, review cadence, and action thresholds.
Explore the engagement →Gaming expertise. Impact that travels.
More data does not automatically create better decisions. A useful analytics system makes definitions explicit, preserves cohort context, exposes dependencies, and tells a team when the evidence is mature enough to act. Gaming is the proving ground here; the same discipline applies to app, SaaS, MMP, and analytics-platform teams.
Every major MMP attributes installs and supports SKAdNetwork. The differences that matter for games are in cost and ROI aggregation, raw data access, pricing model, fraud tools and ownership.
Bookings tell you what players spent this period. Revenue tells you what accounting recognises. Confusing the two is how teams misread momentum in both directions.
A map of the tools most game growth teams use, what each category is for, and how to avoid buying five tools that all answer the same question.
LTV is retention multiplied by revenue per active day, summed over a horizon. The arithmetic is easy; the judgment is in the tail of the curve you have not observed yet.
Large language models can write SQL, summarise cohorts and draft analyses fast. They are only as reliable as the definitions you give them — so the metrics dictionary becomes the most important prompt you own.
Blended D30 can stay flat while the market receiving your next UA dollar deteriorates. An average is useful for reporting; it is not automatically the right number for allocation.
Most dashboards answer a question the operator did not ask: what data do we have? The useful question is what changed, who decides, and what they can do now.
Take-Two reported recurrent consumer spending at 84% of net revenue in Q1 FY2027. That number describes revenue mix — it cannot tell you whether the quarter was healthy.
Roblox reported $4.89B of 2025 revenue and $6.79B of bookings. The gap is not an error. It is a clock.
“Retention is up” can be true and still useless if the team hearing it and the team writing it mean different things.
Teams document the recommendation and the numbers behind it. The assumption carrying it usually stays in the meeting — so nothing reopens the decision when the evidence changes.
More channels don't just add inventory — they dilute signal until you're reconciling data instead of making decisions.
As market intelligence consolidates, the advantage stops coming from having the tool. It comes from the internal intelligence you build around it.
Why North Star frameworks quietly produce the wrong roadmap, and the cheap fix.
Most teams under-experiment on pricing and over-experiment on copy. Here's the heuristic I use.
A decision-grade metric architecture spanning definitions, ownership, dependencies, review cadence, and action thresholds.
Explore the engagement →Need an outside operator’s view of the system behind the number?
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