NCS UA Report

Overview

UA Report provides a unified view of acquisition cost, user behavior, and monetization, allowing you to evaluate not just traffic volume, but its actual value and payback over time.

Unlike event date-based reports (such as Product Report and Custom Events), where metrics are analyzed based on the date an event occurred, the cohort approach groups users by install date and enables analysis of their behavior over time — from the moment of acquisition.

This allows tracking revenue, retention, and user value dynamics throughout the entire user lifecycle.

The report helps evaluate campaign efficiency, compare traffic quality, and understand how quickly users start generating revenue.

The report helps answer key questions:

• How quickly users start generating revenue
• Whether advertising campaigns are profitable
• Which traffic sources bring higher-value users
• How user behavior and monetization evolve over time

All data in the report is grouped into cohorts — users who installed the app within a selected time period.

Unlike event date-based reports, all metrics in UA Report are grouped by user install date and analyzed relative to the install day (D0). Because of this, cohort metrics should not be directly compared with day-based reports.

What is a cohort?

A cohort is a group of users who share a common characteristic, most commonly the app install date.

In UA Report:

• cohorts are defined by install date
• all metrics are calculated relative to the install day (D0)

A cohort should not be confused with a segment. A cohort groups users by a specific point in time (e.g. install date), while a segment represents a broader group of users based on shared attributes.

Cohort analysis allows tracking how user behavior and monetization evolve over time after acquisition.

How to find the report

To access the UA Report in Optiflow:

Optiflow Platform > Analytics > Reports > UA Report

How data is structured

This is the core principle of the report — all metrics are aligned to user lifetime after installation.

Key principles of the report:

• each row represents a cohort of users grouped by install date
• all metrics are calculated relative to D0 (install day)
• metrics labeled as D{x} represent cumulative values over X days after install
• unless stated otherwise, all values are cumulative

Data is updated daily and available for up to 720 days since install. Aggregation is performed based on selected filters.

The report reflects factual metrics calculated from acquisition, monetization, and user activity data.

Metrics can be divided into two groups:

• lifetime cohort metrics collected up to the current day
• fixed-period metrics (D0, D4, D7, etc.)

Glossary

Data sources

The report combines data from multiple sources at the user level.

It includes:

• acquisition data (impressions, clicks, cost) from UA grabbers
• install and attribution data from MMPs
• revenue data (ads, IAP, subscriptions) from SDK and app stores

The install date is used as the reference point for cohort formation.

Purchase and subscription data are collected and/or validated through app stores, while user activity and ad revenue are tracked via the Magify SDK.

All monetary values are converted to USD for consistency. Store fees are excluded from revenue metrics.

Main sources

• UA Grabbers — cost, impressions, and clicks
• MMP (Adjust / AppsFlyer) — installs, attribution, traffic sources
• Transaction Validator (Magify / RevenueCat) — purchases and validation
• Ad Mediation (AppLovin MAX / LevelPlay) — ad revenue
• Magify SDK — sessions, user activity, and events

Key report capabilities

Cohort analysis

Provides retention and revenue analysis across user cohorts from install date up to D360.

UA campaign performance evaluation

Supports comparison of media sources, campaigns, and creatives using CPI, ROAS, and ROI metrics.

Revenue analysis

Provides detailed breakdowns of net and gross revenue by monetization type (ads, IAP, subscriptions) over time.

Purchase conversion analysis

Includes metrics for paying users (PU), conversion to paying users (Install > PU), and CPA analysis.

Flexible breakdowns

Supports analysis across multiple dimensions, including country, platform, traffic type, device, and more.

Filters / Dimensions

Dimensions are structured from higher-level groups to more granular ones:

Media Source > Campaign > Promo Creative > Creative Type > Adset > Ad

Not all traffic sources support the same structure. Depending on the source, Adset and Promo Creative fields may contain publisher names or publisher IDs.

Metrics

Metrics labeled as D{x} represent cumulative values over X days after install.

General metrics

These metrics provide a high-level overview of traffic acquisition performance and are calculated for all users within the cohort.

They are used to evaluate the efficiency of user acquisition campaigns.

Revenue metrics

These metrics represent the total revenue generated by the cohort and its components.

Total Revenue D{x} = Ad Revenue D{x} + IAP Revenue D{x} + Subs Revenue D{x}

Revenue metrics are available in different formats:

• before ref. — before refunds
• Net — after refunds
• Ref. — refunds only

Paying users analysis

These metrics include all purchase types and are used to evaluate campaigns focused on acquiring paying users.

Subscription analysis

These metrics include only subscription-related events and are used to evaluate campaigns focused on subscription monetization.

IAP analysis

These metrics include only one-time purchases and are used to evaluate campaigns focused on IAP monetization.

Retention analysis

These metrics are used to analyze user retention and understand how users return to the app over time.

Common analysis scenarios

Media source and campaign performance

Used for:

• allocating budget across traffic sources
• comparing advertising channel efficiency

Key metrics

• Installs
• CPI
• Spend
• Act. ROAS D7 / D30
• Retention D7 SDK
• CVR (Install > PU) D7
• CPA PU

Recommended breakdowns

• Media Source
• Country
• Platform

Campaign and creative optimization

Used for:

• identifying effective campaign and creative combinations
• validating audience acquisition hypotheses

Key metrics

• CTR
• CVR (Click > Install)
• CPI
• Installs
• Act. ARPU D7
• Act. Ad Revenue
• Act. IAP Revenue

Recommended breakdowns

• Campaign
• Promo Creative
• Creative Type

Paying user and purchase conversion analysis

Used for:

• evaluating traffic quality from a monetization perspective
• calculating the cost of acquiring paying users

Key metrics

• PU D7 / D30
• CVR (Install > PU) D7
• ARPPU D7 / D30
• CPA PU D7 / D30

Recommended breakdowns

• Media Source
• Campaign
• Country

Technical details and important notes

1. Cohort methodology

All metrics in the report are accumulated relative to the user install date.

Example:

A user installs the app on January 1 (D0). A purchase made on January 5 will be attributed to the January 1 cohort and included in Act. Revenue D4.

2. Net vs Gross metrics

• metrics marked as (Net) already account for refunds
• metrics marked as before ref. show values before refunds
• metrics marked as (Ref.) show refund amounts

By default, the report displays metrics without refund deductions because they provide the broadest monetization overview.

Additional metrics can be enabled through Edit Table depending on the application monetization model.

3. Retention: SDK vs MMP

The report includes two retention types:

• Retention D{x} SDK
• Retention D{x} MMP

MMP retention is based on attribution provider data.

SDK retention is based on events collected by the Magify SDK and better reflects actual in-app engagement.

4. Install attribution and traffic sources

Install data is provided by the configured MMP.

If an install cannot be attributed, dimensions such as Media Source or Campaign may contain values like organic or unknown.

Organic installs always have zero spend.

MMP attribution data may be updated retroactively, which can lead to small changes in historical data.

Use Case

The metrics and filters in this report are used to monitor performance and optimize user acquisition campaigns.

Analysis usually starts at the overall application level and can then be broken down by traffic source, campaign, or geography.

Most commonly used metrics:

• Spend
• CPI
• ARPU
• ROAS
• Retention
• paying user conversion metrics

Historical analysis is especially important because it helps identify when performance changes occurred and correlate them with campaign updates or traffic source changes.

Weekly analysis is recommended because traffic acquisition typically has strong weekly seasonality.