Game Analytics Platform Development

Game analytics for one live-service decision, not a catalogue of metrics.

We scope a focused analytics layer around a game's event taxonomy and one decision such as onboarding, progression, economy balance, matchmaking, or live-ops impact. Delivery covers event contracts, identity, ingestion, quality, cohort logic, one decision view, experiment context, access, monitoring, and handover.

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

Evidence and scope

Starts at $25K

Focused first release

One event source, one player identity model, one live-service decision, and one role group.

8 to 12 weeks

Typical focused timeline

Taxonomy, instrumentation audit, baseline, pipeline, decision view, validation, and handover.

No direct case

Evidence boundary

RaftLabs has adjacent event and ledger work but no published game analytics case.

Evidence · planning contextSee the work

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Does the studio have retention and revenue charts but no way to connect a change to version, acquisition cohort, first-session path, or economy event?

02

Are client-reported events missing, duplicated, renamed, or easy to manipulate without anyone knowing which dashboard became unreliable?

Plain answer

A game analytics platform turns versioned player and economy events into decisions about onboarding, progression, retention, monetisation, matchmaking, or live operations. RaftLabs scopes one event source and decision first, with identity, quality, cohort, experiment, and monitoring controls. A focused release starts at $25,000 and usually takes 8 to 12 weeks.

Retention fell after the release. The event model could not say which release.

The dashboard grouped client and server events under the same name, an old build omitted the experiment marker, and reinstalling players acquired new anonymous IDs. The curve was real, but its segments were not trustworthy. Before the studio changed onboarding, it needed versioned events, an identity rule, and a decision the analysis could support.

Focused delivery baseline

$25K
starting first release
One source and one decision
8-12 weeks
typical focused timeline
Instrumentation audit through handover
No direct case
published proof boundary
Adjacent data-platform work is not game evidence

RaftLabs does not currently publish a game analytics outcome case. The delivery baseline is a scope, not a promise of retention, revenue, or infrastructure capacity. Acceptance should cover event completeness, schema validity, identity joins, reconciliation, cohort definitions, calculation tests, freshness, access, dashboard use, and the chosen decision outcome.

Build custom game analytics only when a specific studio decision exceeds the current product.

Commercial game or product analytics is the better first step when standard event, retention, funnel, and experiment reporting is enough.

A fit
01

A live title has proprietary progression, economy, matchmaking, experiment, or live-ops questions that the current product cannot express.

02

Game, data, economy, and product owners can define events, identities, versions, accepted measures, and a bounded decision.

03

The studio needs governed raw events, reconciliation, custom cohort logic, or operational integration it will own.

Not a fit
01

The title is pre-launch, has little representative traffic, or only needs standard acquisition, retention, and funnel dashboards.

02

The team has no maintained event catalogue, server truth, identity rule, decision owner, or capacity to update instrumentation.

03

The proposal assumes analytics will explain causality without experiments or automatically improve retention and monetisation.

Choose the analytics boundary by the question

NeedBest fitBoundary
Standard funnels, retention, experiments, and audiencesCommercial game or product analyticsInstrument the supported event model and use existing reports
Proprietary player, progression, economy, or live-ops decisionFocused game analyticsVersioned events, identity, cohort logic, reconciliation, and decision view
Governed measures across finance, product, and operationsBusiness intelligenceSemantic model, access, dashboards, reporting, and adoption
A bounded investigation or repeatable analysisData analyticsQuestion, dataset, method, evidence, interpretation, and reuse

Scope

What belongs in one game analytics decision path

  • 01
    Versioned event contract
    Define event owner, source, payload, build and content version, player and session identifiers, server authority, experiment exposure, timestamps, units, privacy class, and compatibility rules.
  • 02
    Player and cohort identity
    Choose anonymous, device, account, household, and cross-platform merge behaviour. Preserve the limits of a join and prevent reinstalls or shared devices from silently changing a cohort.
  • 03
    Progression or economy model
    Represent levels, quests, matches, rewards, currency sources and sinks, balances, purchases, and reversals in business terms the game team approves and can reconcile.
  • 04
    Decision view and experiment context
    Show the chosen cohort, version, exposure, denominator, uncertainty, guardrails, and release markers. Link a signal to the review or product decision it should trigger.
  • 05
    Quality and operating controls
    Detect missing and duplicate events, schema drift, impossible sequences, late arrival, bot or fraud patterns, ledger mismatch, refresh failures, and unexpected cost or volume.

How it works

From player question to trusted decision view

  1. Phase 1
    01

    Define the game decision and event owner

    Choose one live-service question, player cohort, game versions, event source, identity boundary, economy or progression concepts, experiment context, costly errors, users, and acceptance measures.

  2. Phase 2
    02

    Audit instrumentation and baseline

    Trace client and server events, naming and payload versions, duplicates, gaps, timestamps, session and account joins, late data, fraud exposure, historical changes, and current dashboard results.

  3. Phase 3
    03

    Build the focused analytics path

    Implement event contracts, validation, ingestion, identity and cohort logic, transformations, one decision view, access, experiment annotations, alerts, traceability, and recovery.

  4. Phase 4
    04

    Validate releases and hand over

    Replay representative periods, reconcile platform and ledger totals, test schema changes and spikes, review false signals, document measures, train owners, set monitoring, and release.

Risk

What the platform must make visible

Client trust
Use server-authoritative events for balances, purchases, rewards, and competitive outcomes where possible. Treat client reports as input that may be late, absent, or manipulated.
Identity change
Document account merges, guest conversion, reinstall, cross-device, deletion, age or consent constraints, and the effect each rule has on cohorts.
Cohort and experiment bias
Name inclusion, exposure, denominator, version, time window, sample limits, and guardrails. Do not present observational association as causal lift.
Schema drift
Version producers and transformations, test compatibility before a release, monitor missing fields, and preserve historical definitions rather than rewriting old behaviour silently.

Scope and price

A focused game analytics release starts at $25,000.

Start with one title, event source, identity model, live-service decision, representative period, one role view, and an owner for instrumentation.

Use a commercial platform when standard reporting fits. Because demand and direct proof do not support this standalone page, its durable home should be the Data Analytics service.

Starting investment

Starts at $25,000

A focused release usually takes 8 to 12 weeks. More titles, platforms, real-time streams, experiments, economy ledgers, or identity paths increase scope.

No retention promise

The release improves the evidence available to a studio; it does not guarantee player behaviour, revenue, or experiment lift.

Definitions ship with the dashboard

Measures, cohorts, event versions, source freshness, and known limits remain visible to the people using the result.

Related analytics and software services

Game analytics questions

Games create versioned sessions, matches, progression states, virtual economies, rewards, purchases, live events, and server outcomes that do not map neatly to pages or funnels. Useful analysis must preserve player and session identity, content and build versions, economy sources and sinks, experiment exposure, and server-authoritative results.

Use an established platform when its event limits, retention, dashboards, exports, identity, and price fit. Custom work is justified when the decision depends on a proprietary economy, progression, matchmaking, anti-fraud, experiment, or warehouse model the product cannot express or reconcile. Benchmark the existing tool before replacing it.

It can show associations and test hypotheses across cohorts, versions, acquisition sources, first-session paths, progression, performance, and economy behaviour. Observational data alone does not prove cause. Stronger evidence comes from an explicit experiment or release comparison with correct exposure, guardrails, sample size, and interpretation.

Define versioned schemas, prefer server-authoritative events for consequential outcomes, validate ranges and order, preserve event time and arrival time, deduplicate, monitor gaps, reconcile currency and purchase totals, limit personal data, and flag impossible sequences. A dashboard should show when its source is incomplete.

A focused release starts at $25,000 and usually takes 8 to 12 weeks. It covers one event source, one identity model, one decision such as onboarding or economy balance, a bounded cohort model, one role view, monitoring, and handover. Several titles, platforms, experiments, or real-time streams increase scope.

Work with us

Bring one player decision and the events used to make it.

Share the title and platforms, event catalogue, identity model, versions, current analytics, economy or progression rules, experiments, decision owner, and the cases the dashboard gets wrong.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.