Overview

This article explains how Product Report data is collected, calculated, and interpreted.

Understanding these principles is essential for correctly analyzing metrics and avoiding incorrect conclusions.

User counting and GDPR

Why users are counted separately across apps

In Product Report, users are counted separately for each application.

This means that if the same person uses two different apps, they will be counted as two different users.

This is required by GDPR and privacy regulations.

How it works

Each app assigns its own unique user identifier.

These identifiers are not shared or linked across apps.

As a result:

• one app → accurate unique users
• multiple apps → sum of users without deduplication

Example

If:

• 5 users play only App A
• 3 users play only App B
• 2 users play both

Then:

• App A → 7 users
• App B → 5 users
• Total (combined) → 12 users

Key takeaway

• Unique users are accurate per app
• Cross-app deduplication is not possible
• This behavior is expected and required

Active Users: AU vs AUAL

Overview

Product Report provides two types of active user metrics: AU and AUAL.

They are calculated differently and may produce different results.

AU (Active Users)

Users with any recorded activity.

• counted on every day with activity
• includes all user interactions

AUAL (AU App Launch)

Users who launched the app.

• counted only on app launch
• activity after launch is attributed to the same day

Example

A user:

• opens app at 23:55
• continues activity after midnight

→ AU: counted for two days
→ AUAL: counted for one day

When to use

• use AU → general activity
• use AUAL → strict daily audience

A/B test data interpretation

Product Report shows A/B test data based on campaign interaction.

This means:

• you see interactions related to a specific test
• not full user activity within that test

For full A/B analysis, use A/B Test Cohort Report.

Bonus and game economy data

Bonus data is not automatically filtered for anomalies.

This is because:

• each game has its own economy
• only you know what values are normal

Recommendation

Use filters such as:

• Bonus Value
• Bonus Type
• Level
• Game Mode

to define valid ranges for your game.

Install data limitations

Install data in Product Report:

• is not intended for cohort analysis
• may differ from MMP reports
• is available for a limited time window

When to use

Use Product Report installs for:

• operational monitoring
• campaign interaction analysis

For cohort analysis, use UA Cohort / Predict Reports.

Refunds and subscription renewals

Overview

Refunds are deducted in total revenue metrics.

This means you see net revenue.

Important limitation

Refund data does not include campaign metadata.

This means refunds cannot be attributed to:

• campaigns
• creatives
• levels

How data is shown

• total > includes refunds
• breakdowns > exclude refunds

Key takeaway

• totals > accurate net revenue
• breakdowns > comparable but not net

Data freshness window

Product Report includes only events received within 7 days after they occurred.

Why this matters

Some users play offline.

Their data may be sent later and excluded from the report.

Example

A user plays offline for a week and sends data later.

> events older than 7 days are not included

Key takeaway

• report shows fresh data
• delayed events are excluded
• differences with other systems are expected

Deduplication and data cleaning

Ad revenue data is:

• deduplicated
• cleaned from duplicates and errors

This improves accuracy but may differ from raw data.

Campaign hierarchy (Advanced SDK)

Campaigns may have hierarchy:

• parent
• nested

Metrics are not additive between them.

Installs vs MMP

Installs in Product Report represent confirmed installs:

• user installed
• user opened the app

Why numbers differ

MMP may count installs without app activity.

Product Report counts only active users.

How to validate

Use:

• Days After Install = 0
• Active Users

Retention explanation

Retention measures how many users return after installation.

Important limitation

Retention cannot be calculated using a single filter.

It requires combining multiple cohorts.

Example insight

Retention can be analyzed by level to understand player behavior.

Important notes

• Product Report is not a cohort report
• metrics may differ across reports
• some data is delayed or limited
• interpretation requires context