Turn your available game telemetry, player behavior, store, monetization, live-ops, match and audience data into a structured view of what is happening, where players or viewers drop off, which segments behave differently and what questions your team should investigate next.
Global delivery • Pricing and timing confirmed against data readiness and analysis depth.
Current market pricing for general freelance data analysis commonly starts around the low hundreds of US dollars for meaningful cleaning and reporting work. These entry options keep the scope focused; multi-source engineering, dashboards, predictive models and recurring analytics are quoted separately.
For one defined player, product, commercial or esports question with data already available.
For teams that need several connected views across the player journey, monetization or live-ops performance.
For multi-title, esports, dashboard, integration, predictive or ongoing reporting requirements.
Share the decision you are trying to make and what data you already have. Rudrriv can review whether a focused sprint is enough or whether the requirement needs a wider custom scope.
Generic business reporting can miss the mechanics that shape game data: event schemas change between builds, players repeat loops rather than move once through a funnel, monetization sits inside an economy, live-ops create time-bound behavior shifts and esports datasets add match, map, team, player and tournament structures.
Store exposure, campaign traffic, wishlists, installs and source or region context.
First session, tutorial, missions, levels, features, sessions and progression loops.
Return behavior, live-ops response, purchases, ads, offers, currency and payer segments.
Match performance, tournament stages, broadcast engagement, community and repeat participation.
Not every project needs every analysis type. The right set depends on the game model, business question, event definitions and available data.
Review ordered player steps and identify where progression, feature adoption or conversion drops.
Compare return behavior across install periods, builds, platforms, regions or player segments.
Analyze payer conversion, revenue cohorts, offers, transaction patterns and economy signals where available.
Compare behavior around patches, events, content drops, seasons or promotions with suitable time controls.
Structure team, player, map, round or tournament-stage performance analysis from authorized match data.
Review traffic, wishlist, campaign, install or channel data without assuming one platform’s metric equals another.
Analyze stream or video views, watch time, audience segments and content performance when data access permits.
Compare groups by behavior, spend, progression, engagement, mode, platform or other supported dimensions.
The service does not require every platform below. These are common categories that may be relevant depending on the game, publisher, team or tournament environment and the access you already hold.
Event tables, session logs, progression, inventory, economy or feature-use data supplied as exports or approved read-only access.
Exports from game analytics tools such as Unity Analytics or GameAnalytics, or equivalent event/BI systems.
Steamworks traffic or wishlist reports, app-store exports, transaction summaries and other publisher/store data.
Campaign cost, source, creative, install and conversion exports where identifiers and attribution windows are understood.
Authorized match or ranked data, including publisher APIs such as Riot developer data where the customer’s use complies with applicable terms.
Channel or content analytics such as YouTube reporting, streaming exports or event audience data where access is available.
Platform note: platform names describe possible customer data sources only. They do not imply a Rudrriv partnership or guarantee that every API, report or field is available for every project.
The first follows player behavior through a living product. The second treats competitive and audience data as structured game-event information with its own units, hierarchies and rights constraints.
Connect retention to what players actually did, which build they used and which content or economy conditions were active.
Structure competitive data around the actual unit of play—match, map, round, series, player, team or tournament stage—then connect audience signals where relevant.
The analytical work and the final deliverables are related but not the same. Scope is clearer when both are defined before the project begins.
Activities inside a standard data-ready analysis.
Formats vary by package and the nature of the analysis.
This matrix helps separate a data-analysis engagement from engineering, instrumentation or managed-analytics work that may need its own scope.
| Requirement | Status | How it is handled |
|---|---|---|
| Analyze customer-provided exports | Standard | Core scope when the data is relevant, readable and sufficient for the agreed question. |
| Data cleaning within the analysis | Standard | Reasonable preparation, filtering, reshaping and agreed joins are included according to package. |
| Cohort, funnel, segment and trend analysis | Standard | Used when the event structure and business question support the technique. |
| Dashboard build / automated refresh | Custom | Requires BI platform, data model, refresh method, access and ownership to be defined. |
| API extraction / warehouse engineering | Custom | Depends on authentication, platform terms, schema stability, infrastructure and volume. |
| Predictive churn, LTV or ML models | Custom | Needs sufficient history, target definition, validation design and an appropriate data volume. |
| SDK instrumentation / production game code | Not standard | Engineering implementation is not assumed in a normal analysis project; assess separately. |
| Guaranteed retention, revenue or match outcomes | Not included | Analysis supports decisions; it cannot guarantee product, commercial or competitive results. |
You do not need a perfect data stack, but knowing what each field means—and what changed during the selected period—can prevent misleading conclusions.
What decision will the analysis support? Which game, mode, region, tournament, campaign or player group is in scope?
Field definitions, event taxonomy, identifiers, known gaps and confirmation that the customer is authorized to share the selected data.
Patch dates, seasons, promotions, tournaments, outages or content changes that could explain shifts in behavior.
PC, console, mobile or web; relevant stores; build/version; regions; cross-play or cross-progression details when they affect comparability.
Product, game design, live ops, publishing, UA, monetization, esports, data or leadership stakeholders may define the question differently.
Identify personal data, credentials, account-level fields, platform restrictions or confidential information before files are exchanged.
The review approach is designed to reduce avoidable errors without pretending incomplete telemetry can answer questions it was never designed to capture.
Fields, types, timestamps, identifiers, duplicates and missing values.
KPI formulas, cohort rules, event meaning, time windows and exclusions.
Filters, joins, segment logic, denominator consistency and outliers.
Builds, patches, live events, campaigns, tournament stages and platform differences.
Charts, labels, assumptions, caveats and correction items before final delivery.
These are realistic situations that can create a need for analysis; they are not case studies or performance claims.
A studio sees early drop-off but needs to know which tutorial or progression step deserves investigation.
A patch, season or feature launch coincides with movement in return behavior and teams need to separate affected cohorts.
Payer conversion, offers, ad revenue or transaction behavior tell different stories across segments or time windows.
An esports team or tournament stakeholder has authorized match data but needs structured comparison across maps, roles or stages.
A publishing or marketing team needs to connect store exposure, campaign traffic, wishlist or install signals without treating them as the same metric.
Product, live ops, UA and commercial teams have separate reports and need a decision-focused synthesis rather than another dashboard of raw KPIs.
The workflow stays lightweight for a focused project and expands only when the data architecture or stakeholder complexity requires it.
Decision, game context and available data.
Confirm sources, questions, package and boundaries.
Validate fields, definitions, access and limitations.
Clean, segment, calculate and visualize.
Check calculations, context, labels and caveats.
Deliver findings, files and follow-up questions.
A clean export with stable definitions can be analyzed quickly. A project that first needs access approvals, event reconstruction, cross-platform joins or data engineering is a different engagement.
Typical entry projects are estimated at 3–5 or 5–8 working days only when the required data and definitions are available.
Gaming datasets can contain persistent player identifiers, location or device signals, account information, payment-related fields, proprietary economy data or competitive information.
Scope, access, deliverables and data limitations are clarified before the work begins so the output is designed around a real decision rather than a generic KPI dump.
It turns available game, player, store, campaign, monetization, live-ops, match or audience data into structured analysis for a defined decision. The exact scope depends on the data you can provide, the question you need answered and whether the work is product-focused, commercial, esports-focused or cross-functional.
Yes, when the underlying player or event data supports cohort and return analysis. The work can examine retention by install period, build, platform, acquisition source, progression milestone or other available dimensions. Predictive churn modeling is a separate custom scope.
Yes. With suitable event data, analysis can map ordered steps such as first session, tutorial milestones, level progression, feature adoption or purchase paths to identify where players stop, repeat or take longer than expected.
Yes, when revenue, in-app purchase, ad-revenue or transaction data is available and permitted for analysis. Scope can include payer conversion, purchase behavior, ARPU or ARPPU-style metrics, offer performance and cohort revenue views where the inputs are sufficient.
Yes, for datasets the customer is authorized to use. A scope may cover match, map, round, team or player performance, tournament-stage comparisons and selected audience or content metrics. Access rights, API terms and publisher restrictions remain important dependencies.
Typical inputs may include CSV or spreadsheet exports, SQL extracts, data-warehouse tables, game telemetry, analytics-platform exports, store reports, attribution or campaign reports, payment or ad-revenue summaries, match data and streaming or video analytics. The final source list is confirmed during scope review.
Not always. Many focused analyses can start from customer-provided exports. Read-only platform or database access may be useful for broader work, while API integration, instrumentation changes or new data pipelines should be treated as custom scope.
Provide the business question, relevant date range, data exports or approved access, a data dictionary or event definitions when available, important release or live-ops dates, platform or build context, known data limitations and a contact who can clarify business rules.
Depending on the package, deliverables can include a cleaned analysis workbook or dataset extract, KPI definitions, charts and tables, cohort or funnel views, segment findings, a concise decision-ready insight report and recommendations for follow-up analysis.
Dashboard design or automation can be scoped separately when the data model, refresh method, BI platform and access requirements are clear. The entry analysis packages focus on answering defined questions rather than building a permanent reporting stack.
Instrumentation design, SDK implementation, event-pipeline changes and production engineering are not assumed in the standard analysis scope. They can be assessed separately where the required platform, engineering ownership and technical access are clear.
Analysis starts with a readiness and validation pass covering fields, types, duplicates, missing values, date ranges, event consistency and obvious anomalies. Findings are interpreted with the known limitations documented so the output does not overstate what the data can support.
The standard model is validation and correction rather than unlimited creative revision. Consolidated feedback can be used to correct assumptions, labels or agreed calculations. New datasets, materially different questions or expanded analysis normally require a scope change.
A focused, data-ready analysis is typically planned for 3–5 working days, while a broader player and product analysis is typically 5–8 working days. Large datasets, multiple titles, unclear event definitions, new access requirements or stakeholder review cycles can extend the schedule.
The main drivers are the number and condition of data sources, data volume, cleaning effort, number of games or platforms, required metrics, segmentation depth, match or event complexity, dashboard or automation needs, predictive modeling and the number of stakeholder questions to resolve.
Only the minimum necessary data should be shared for the agreed analysis. Avoid putting sensitive datasets or credentials in the public enquiry form. During scope review, identify personal data, account access, confidential commercial fields and platform restrictions so the working method can be agreed before files are exchanged.
No. The service provides analysis, evidence, findings and decision support based on the supplied data. Outcomes depend on product changes, player behavior, market conditions, execution, competition and other factors outside the analysis itself.
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