Analytics, LiveOps& monetization.

We spend a couple of days studying the game, playing through the experience and reviewing what is already in place. Within a few days, you receive an honest assessment and prioritized insights based on the game and the evidence available.

Identify the essentials for this stage, improvements worth testing and work that can wait. Consider the player experience, existing systems, data quality, economy, monetization, acquisition, budget and team capacity together.

Review where onboarding, retention, progression, resource balance or acquisition economics may be limiting results. Explain what the available data supports, what remains uncertain and which changes or tests should come next.

Work through what a new player should see, learn and do first. Map tutorial steps, early progression and the first meaningful success, then define where you need to observe confusion, friction or drop-off.

Decide which analytics, attribution, BI, error monitoring and monetization services your game needs at this stage. Review ownership, access, pricing, usage limits and integration effort so the setup fits your team and budget.

Design a practical tracking plan for first launch, tutorial steps, level starts and completions, sessions, resource changes, purchases and ad interactions. Define event attributes, player and session identifiers, and checks that confirm the data is arriving correctly.

Turn the first data into an onboarding funnel, progression view, retention cohorts, resource balances and revenue breakdown. Choose the essential KPIs and reports for the decisions your team faces now, with clear metric definitions.

Separate initial revenue from evidence of sustainable returns. Review acquisition spend, relevant fees and operating costs, cohort age and payback assumptions. Flag reporting discrepancies, small samples and projections that need more observation.

Agree on a test budget, questions to answer and criteria for continuing, investigating or stopping. Establish a review routine so the team knows what to measure next and how to evaluate the first results before increasing commitment.

Recommend the critical KPIs, reports, analytical events and attributes for your game. If your team is new to data collection, help define the tracking plan, implementation priorities and validation process from the beginning.

Analyze onboarding, retention, sessions, engagement, progression and event participation. Break down funnels and cohorts to understand where players struggle, disengage or return, and which segments need a closer look.

Bring resource income, spending and balances together with rewarded ads, interstitials and purchases. Examine differences by player segment, progression stage and cohort, and make economy and monetization trade-offs visible.

Build data models, scheduled reports and quality checks inside your database and private BI environment. Define a shared view of overall game health, metric ownership, refresh schedules and alerts for important changes.

Design custom reports for desktop and mobile. Set up approved summaries and alerts through Telegram, Slack, WhatsApp or email, depending on the channels and integrations available to your team. You choose the recipients, content and delivery rules.

Take on the hands-on analytical and technical work, consult on a specific problem, or help your team build the system themselves. Document calculations, event definitions and investigation steps so the work remains usable after handover.

Plan event mechanics, calendars, rewards and dependencies. Work through how events overlap, share resources and affect each other, with a coherent experience across the schedule.

Recommend segmentation attributes and eligibility rules. Design the event management panel, configuration structure and controls your LiveOps manager needs to plan, launch and monitor events.

Work through deployment, tracking, configuration checks and operating procedures. Prepare the technical specifications and hands-on workflow for your LiveOps manager or an AI agent, with clear review and approval steps.

Calculate resource income and spending across progression, events, offers, purchases and rewarded ads. Compare player segments and identify where balances accumulate or run short.

Investigate unintended resource loops, weak sinks, exploitable mechanics and overlapping rewards. Check whether events or rewarded video supply enough resources to undermine progression or purchases.

Work through reward budgets, prices and event interactions. Set out assumptions, scenarios and measurement plans so the team can evaluate an economy change after release.

Audit mediation, placements, configuration and revenue tracking. Review app-ads.txt, store and developer-page links, and consistency across the systems involved. Document setup checks and operating practices for the team.

Analyze how rewarded video affects resource supply, purchase motivation and IAP revenue. Look for excess rewards and signs of cannibalization across payer groups, progression stages and event participation.

Review interstitial timing, frequency and placement against sessions, progression and retention. Identify problematic points and define experiments that measure the combined effect on ad revenue, purchases and player lifetime.

Connect MMP and BI reporting, align cohort windows, time zones, currencies and revenue definitions, and investigate discrepancies. Define which figures to use for each acquisition decision and which limitations to keep visible.

Review the events and values sent to advertising networks, their timing, coverage and quality. Align those signals with the outcome you want, while checking consent requirements and the risk of optimizing for a misleading proxy.

Assess acquisition cost, retention, ad and IAP revenue, and payback by cohort and segment. Distinguish observed results from forecasts, account for immature cohorts, and define when to scale, investigate or stop.

Combine ad and purchase revenue with retention and cohort age. Choose a time horizon, segment by market and acquisition source, and document the data behind the estimate.

Separate measured revenue from projected revenue. Use scenarios, revisit assumptions as cohorts mature and compare earlier forecasts with actual outcomes to understand where the model is reliable.

Compare acquisition costs and the costs relevant to your project with expected revenue and payback timing. Identify the retention, monetization or acquisition constraints that need to change for profitable growth.