measure game results is essential today if you create or publish mobile titles.

Following the pandemic, the sector grew strongly: Android installations increased by +22% in 2021 and UA spending reached USD 14.5 billion, with the United States contributing half.

Privacy changes on iOS and the shift in spending to Android force us to rethink what data you collect and how you use them. Here you will learn to connect goals simple with clear KPIs and practical actions.

I'll show you simple metrics like CPI, DAU/MAU, retention, and LTV, and how to interpret numbers by country, platform, and creative. You'll also see how to implement in-app events without relying on personal identifiers.

This content is educational: I encourage you to compare sources and respect privacy rules. In the end, you will know how to convert data in decisions to improve your experience users and optimize the game with a analysis practical and responsible.

Introduction: Why measuring game results today matters more than ever

Today, mobile platforms attract huge audiences and fierce competition. The Android install boom (+22% in 2021) and a global UA spend of $14.5 billion—with the US contributing nearly half—change the way you think about strategy.

In front of this volume, the data and the quality of the information makes the difference. Apple limited access to the IDFA with ATT, so attribution on iOS now relies on aggregated signals like SKAdNetwork and conversion values.

The guide takes you through a practical process. You'll learn how to define clear objectives, choose useful KPIs, and create dashboards that your team uses every day.

Context and challenges

You'll see how to combine probabilistic modeling, incrementality tests, and MMM to estimate impact with aggregated data. You'll also learn how to tailor information by market and platform without intrusive practices.

What you will get

• A map from objectives to actionable KPIs.
• Best practices for instrumentation and analysis by stage: soft launch, scaling, live ops.
• Guides to interpreting limited signals and making prudent decisions.

Define high-level objectives and align KPIs with your strategy

Start with clear goals and connect each goal to an indicator that guides a specific decision. Establishing what you're aiming for prevents you from chasing worthless numbers.

SMART Goals: From Short to Long Term

Define specific, measurable, and time-bound goals. For example: “Increase D7 retention from 12% to 15% in 2 sprints” either “Improve ARPDAU 8% in 90 days”.

Separate the tactical from the strategic: stabilize initial metrics before aiming for long-term LTV.

Target mapping → indicator → decision

Map out a simple path: goal, indicator you're monitoring, and the action you'll take if it changes.

• Reduce churn → D1/D7 retention and session length → adjust onboarding or difficulty.
• Drop in IPM by country → new creative or different segmentation.
• Feature testing → compare pre/post data and use thresholds for rollback.

Measures the number of active hypotheses To avoid noise, organize weekly reviews with managers and deadlines. This way, you'll transform the analysis into decisions that preserve the product's value and your users' experience.

Essential Acquisition Metrics: From Impressions to Useful Installs

Not all installations are equal: You need clear metrics that connect cost and quality. Here you define CPI, CPI, and eCPM and learn how to interpret each number with practical meaning.

CPI, IPM and eCPM to evaluate traffic quality

CPI is the cost per installation; use it to compare sources and geographies, but always weighted by retention and ROAS at window (D7, D14).

IPM indicates installs per thousand impressions. If it drops, review the creatives, format (video vs. playable), and platform targeting.

eCPM = revenue per 1,000 impressions. It helps you negotiate inventory and prioritize formats (interstitial vs. rewarded).

NOI rate, organic conversion and K-Factor

Look at the NOI rate and organic conversion rates side by side; an organic spike after a campaign suggests a halo and real user engagement.

“Calculate the K-Factor in social functions: if it goes up, you could reduce bids without losing volume.”

Examples by geography, platform and creatives

A high IPM in LATAM with a low CPI can offset a lower ARPU if the 7-day ROAS remains the same. Segment by creative and register variants for a good analysis.

• Compare sources with CPI and retention.
• Monitor IPM by creativity.
• Use eCPM for monetization decisions.

Retention, sessions, and engagement: the daily pulse of the game

The daily pulse of your product is detected in how they return and how much they play your usersHere you'll see how to interpret DAU, MAU, and cohort retention to make quick and actionable decisions.

DAU, MAU, and stickiness

DAU and MAU give you the active base: DAU measures daily usage; MAU, monthly audience. Calculate stickiness = DAU/MAU to know what proportion returns within the month.

If stickiness drops after a level change, review the difficulty or onboarding. Use these numbers to assess health and prioritize testing.

Retention and churn rate per day 1/7/30

Measures retention by cohort: D1, D7, and D30 by country and version. Retention is the percentage of active players relative to the initial cohort.

A drop in D1 usually points to issues with onboarding or how you present the first level. Churn reflects the monthly loss rate; compare the two to understand the dynamics.

• Session duration: short sessions = friction; very long = possible fatigue.
• Segment the number of new vs. returning users to see if improvements affect the experience without harming monetization.
• Cross-reference sessions with the funnel (tutorial, first IAP, first ad viewed) to prioritize UX adjustments.

Detect dropout spikes at specific levels and adjust progression or rewards.

Simple and effective monetization: ARPU, ARPPU, ARPDAU and LTV

Monetization doesn't need complexity to be effective: Start with clear formulas that connect revenue to product and UA decisions.

• ARPU = total revenue / users.
• ARPPU = total revenue / paying users.
• ARPDAU = total revenue / DAU.
• LTV = ARPU × half-life (in days or periods).

Average user income and time of first purchase

ARPU gives you a global view of the worth per user, but it doesn't take into account that few pay. Segment with ARPPU to see the real willingness to pay.

The timing of your first purchase is key. If you're late, try soft offers on the first purchase. level or bundles that improve the user experience without putting pressure.

ROAS per time window and its responsible reading

Calculate ROAS by windows (D1, D7, D30) and compare it to projected LTV. Don't scale only by short ROAS if the recovery is long term gets worse.

Some practical guidelines:

• Use ARPDAU for daily readings sensitive to promotions; interpret spikes with caution.
• Analyze the rate of paying users by source and evaluate ads if a source has good engagement but low conversion.
• Review daily increases or decreases and confirm whether they are due to testing, updates, or seasonality before changing your strategy.

Remember: These metrics are meant to guide you, they don't guarantee ROI. Use them in conjunction with others. data to make prudent decisions in your game.

In-app instrumentation: events, funnels, and rich parameters

Well-designed events reveal where you're losing users at each stage. Start by mapping the key flow and record conversions between steps so you don't guess behaviors.

What events to measure by gender and size

It records basic events: completing the tutorial, completing a level, interacting with an ad, and making a purchase. In larger titles, it adds metrics such as score, time per level, and friction zone.

Conversion by stages: tutorial, key level, IAP, ads

• Flow Typical: tutorial → first level completed → first ad viewed → first IAP. Measure the conversion between each step.
• Enriching events with parameters (level, difficulty, time spent, currency spent) improves the analysis and segmentation.
• By genre: In Puzzle, prioritize level completion and hint use; in Social Casino, register for spins, jackpots, and VIP packages.
• Mark key levels as sticking points and observe if the rate of progress drops; add rewards or adjust difficulty if necessary.

Practical advice: Link rewarded events to progress to maintain user experience and convert advertising interaction into game value.

Privacy-Friendly Measurement: ATT, SKAdNetwork, and MMM

Protecting privacy doesn't prevent you from gaining useful signals for optimization. You must accept limits like ATT and adapt your strategy to match. data aggregates, modeling, and secure collaborations.

SKAN values and post-installation windows

Plan SKAN conversion metrics that capture early signals: 24-hour retention, initial revenue, or tier progression. Document assumptions and thresholds.

Adjust timers to maximize information without creating excessive postback latency.

Probabilistic modeling and incrementality

Complements SKAN values with probabilistic modeling to estimate channel performance when there is no deterministic attribution.

Use incrementality tests and an MMM model to measure top-down impact and redistribute budget between platforms and markets.

Data Clean Rooms and good practices

Data Clean Rooms allow you to share signals with partners without exposing your data. usersThey are a practical solution for joint analysis and compliance.

• Don't rely on a single source: combine SKAN, modeling and MMM.
• Register and communicate the worth and the time of each signal.
• Prioritize privacy: Explain boundaries to your team and avoid promises of perfect attribution.

Your data stack: warehouse, pipelines, and MMP

Your data architecture will determine how much you can scale without breaking the budget. Consider capacity, latency, and governance before choosing.

BigQuery, Redshift, Snowflake and costs

Evaluate Google BigQuery, Amazon Redshift, Snowflake, or Oracle based on volume, cost per query, and compliance requirements. Each option offers trade-offs between price, performance, and ease of management.

Plan for cold storage, date/country partitioning, and compression to control costs at scale.

MMP and cross-platform attribution

An MMP connects campaigns with post-conversion behavior across mobile, PC, and console. This way, you consolidate events and avoid duplication between platforms.

• Design pipelines with AWS Kinesis/Glue or Azure Data Factory and define clear event schemas.
• Integrate daily/monthly active user counts with ad spend to view performance by cohort in a single dashboard.
• Document your data strategy so that product and UA speak the same language.

Recommendation: test small, measure consultation costs and adjust the solution.

For practical guides on attribution and analytics in titles, see the guide measurement & analytics.

How to measure game results with actionable dashboards

Actionable dashboards transform raw data into daily tasks for your team. A good dashboard gives you a quick overview of your health and allows you to act without wasting time.

KPIs by view: high-level, product, UA, and monetization

Design focused views and avoid cluttering them with excess contentEach panel must answer a specific question.

• High level: DAU/MAU, stickiness, total revenue and ROAS by day and country.
• Product: funnel tutorial → key level → IAP/ad, duration of sessions and cohort retention.
• UA: CPI, IPM, eCPM, NOI rate, and organic contribution; add cost per result and trend.
• Monetization: ARPU, ARPPU, ARPDAU, LTV and payer rate, separated by source and geography.

Early warnings: retention drops and revenue anomalies

Set statistical thresholds on your data to detect rapid drops or unexpected spikes.

Validate false positives before acting and limit notifications to those clearly responsible.

“Alert: D7 drop > 20% in a new cohort — check onboarding and tier changes.”

Prioritize panels that facilitate decisions and convert information in actions to improve your experience users and optimize the game.

Cohorts, segmentation and insights by user groups

Creating clear cohorts allows you to compare how different people respond. groups after a change. This way, you avoid random conclusions and obtain actionable signals for both the product and the UA.

Source, country, device and version

Build cohorts by installation date and by source to compare retention and ARPU day 7/30 fairly.

Segment by country and device to identify opportunities: markets with low CPI but good performance can scale.

Pre/post feature comparisons and effects on usage flow

• Compare pre/post feature over the same time range and with similar volume to isolate effects.
• Analyze the number of users by client version and decides when to force an update if a bug affects monetization.
• Get insights of average user income by segment to adjust local prices and offers.
• Check sessions by group (new vs. veterans) and adapts the difficulty or rewards according to behavior.

Includes data of behavior and performs a analysis simple before scaling. This is how you turn content quantitative in decisions that improve the game and the experience of your users.

A/B testing and controlled experiments to improve user experience

Controlled experiments are the most reliable way to turn hypotheses into action. With a responsible experimental method, you reduce risks and obtain data that support your decisions.

Hypothesis, sample size and success metrics

Formulate concrete hypotheses and define the metrics primary before launching. Example: “Reducing difficulty from level 3 will increase D1 retention by 1 pp”.

• Calculate sample size per cohort and set a minimum duration; avoid cutting tests due to daily fluctuations.
• Define success metrics and guardrails to prevent ARPDAU or payer rate from worsening.
• Use dashboards in time real and periodic reviews to detect biases and anomalies.

Retention vs. Revenue: Trade-offs and Decisions

A more generous experience typically increases retention but can reduce average user revenue. Address this trade-off with clear goals and windows for day either term.

Document each experiment and replicate it on other platforms or geographies before scaling. This way you balance the strategy and protect the experience of your users.

“Prioritize tests that measure engagement, progress, and basic ROI; small changes can have a big impact.”

Game economy: sources, sinks and their impact on indicators

Internal economics defines how incentives influence your users' daily behavior. If you balance currencies and prices well, you maintain engagement and value. Otherwise, currency inflation reduces purchasing intent.

Balance of rewards and dynamic pricing

Map the main sources: quests, daily rewards, and IAPs. Identify the sinks: purchases, fees, and crafting. Too many sources without attractive sinks devalues the game. worth.

• Test AI-powered dynamic pricing on small cohorts and compare retention and ARPPU.
• Adjust gradual rewards to avoid spikes that upset the balance.
• Measure daily impact after changes to prevent content inflation or burnout.

Friction signals: failing, completing, abandoning levels

Label start, fail, and complete events by level. These data They show you where users abandon or become frustrated.

Links economics to engagement: Meaningful sinks like cosmetics or progression increase satisfaction without pushing purchases.

“Detects friction by level and acts quickly: small corrections maintain retention.”

Use these insights to adjust without breaking the user experience or game balance.

Data quality, fraud and security

The quality of your data defines how much you can trust the product's decisions. A small bias in the channeling can falsify indicators and generate wrong actions.

Fraudulent installs vs. in-app fraud

There are two distinct problems. Fraudulent installs (bots, click injection) account for ~2% of gaming volume.

In-app fraud is another vector: fake events and invalid purchases. On average, it affects ~11% on Android and ~13% on iOS.

Don't expect to eliminate it completely; Your goal is to reduce their impact and isolate reliable signals.

Anomaly detection and consistency checks

Implement controls that verify consistency between events, purchase receipts, and server activity.

• Difference fraudulent in-app fraud installation to prioritize actions.
• Validates receipts by platform and registers rejection to adjust income.
• Monitor conversion rate, ARPDAU, and the number of unusual events by source.
• Enable anti-cheat policies and security signals on servers to reduce the impact on the community.
• Periodically review your sources and partners; document incidents and adjust block lists without generalizing.

“Early detection and rapid response protect both revenue and the trust of your users.”

AI tools already help detect cheating in titles like Valorant and PUBG. It combines rules, machine learning, and manual controls for a analysis cash.

Differences between mobile, PC and console games in measurement

The way signals are collected and combined varies greatly between mobile, PC, and console. On mobile, measurement is typically more standard and prioritizes SDKs and MMPs. On PC and console, on the other hand, you integrate your own telemetry, stores, and platform services.

Data fragmentation and cross-platform solutions

A common challenge It's about consolidating data without invading privacy. You must unify identity by user and device using pseudo-anonymous keys and a common data model.

Adjust metrics based on platform: Sessions are longer on PC, engagement varies, and concurrent user patterns can be extreme (e.g., esports events like LoL Worlds attract millions of viewers).

• Use an MMP and ETL pipelines to reconcile campaigns and conversions.
• Define a consistent success metric per channel and align it for the long term.
• Normalizes events to compare cohorts without mixing incompatible signals.

“Consolidating cross-platform telemetry allows you to extrapolate learnings without losing context.”

By working this way, you can transfer insights across platforms wisely and improve the product for more users in each environment.

Operational roadmap: from analysis to impact

To turn analysis into impact, you need a clear and repeatable operational plan. A good roadmap defines who does what, when and with what data, and prevents ideas from remaining in documents.

Work rituals: weekly and quarterly reviews

Organizes job with fixed rhythms. Conduct weekly reviews focused on a few KPIs and active hypotheses.

Hold a monthly meeting for retros and cleanup data. Each quarter defines objectives linked to long term and check trade-offs.

• Weekly: Quick decisions and experiment tracking.
• Monthly: pipeline cleanup and backlog prioritization.
• Quarterly: objectives, budget and strategy adjustments.

From insight to action: prioritization and monitoring

Turn an insight into a concrete task. Follow this flow: insight → experiment → decision → deployment → post-measurement.

Maintain a backlog prioritized by impact and effort. Define clear responsibilities and deadlines for each item and avoid unmanaged task pile-ups.

1. Assign a number and owner to each share.
2. Define success metrics and window of term.
3. Document learnings and share with teams to improve flow.

Practical advice: Integrate dashboards into reviews and run continuous A/B tests under the discipline of live ops. If you want to see another approach to the roadmap, check out this one. reverse roadmap.

“Close the loop: Don't just analyze; prioritize, test, and deploy with those responsible.”

Conclusion

The combination of creative intuition and clear metrics drives consistent growth. When you combine creativity and analysis, you make decisions that improve the experience without losing focus.

Use data Practical solutions and pipelines (warehouses, MMPs) alongside methodologies such as MMM and incrementality to make decisions with fewer granular signals. This way, you integrate reliable information and reduce uncertainty.

Design with the mindset long term: Healthy retention, balanced economy, and prudent steps toward success. Protect your users and the game respecting rules and privacy.

Explore trends responsibly. Compare figures, validate sources, and turn that content into actions that sustain growth and trust in your product and your community.