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.
It helps you understand how effectively your campaigns perform, which traffic sources bring higher-value users, and 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.
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.
This is the core principle of the report — all metrics are aligned to user lifetime after installation.
- 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.
To access the UA Report in Magify:
Magify > Analytics > Reports > UA Report
The report combines two types of metrics:
- Fact — actual data collected from sources
- Predict — forecasted values generated by ML models
Predict metrics allow estimating future cohort performance at early stages, before full data is available.
Predict metrics are recalculated as additional fact data becomes available.
Metrics can be divided into:
- accumulated (lifetime) metrics
- fixed-period metrics (D0, D1, D7, etc.)
The report is built on data from external services and internal systems, combined at the user level.
Main sources:
- MMP (Adjust / AppsFlyer) — installs, attribution, traffic sources
- Transaction Validator (Magify / RevenueCat) — purchases and refunds
- Ad mediation (AppLovin MAX / LevelPlay) — ad monetization
- Magify SDK — sessions, user activity, and events
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 IDs or names.
| Filter | Description |
|---|
| Date | The user install date used to define the cohort |
| Application | The app for which the data is collected |
| Media Source | The traffic source that generated the install |
| Campaign | The advertising campaign associated with the install |
| Promo Creative | The specific creative or ad associated with the install |
| Creative Type | The type or format of the creative |
| Platform | The platform of the device (iOS or Android) |
| Country | The country of the user |
| Traffic Type | The type of traffic (e.g. UA, Organic) |
| Week | The install week of the cohort |
| Month | The install month of the cohort |
| Adset | The ad group associated with the install |
| Device | The device model used by the user |
| Device Type | The type of device (e.g. phone, tablet) |
| OS Version | The operating system version of the device |
| Promotion Campaign ID | The unique identifier of the campaign |
| Campaign Assignee | The current owner of the campaign |
| Campaign Initial Assignee | The initial owner of the campaign |
| Promotion Campaign Type (new) | The type or classification of the campaign |
| Creative Producer | The person responsible for the creative |
| Creatives Link | A link to the creative asset |
| Creative Tags | Tags assigned to the creative |
| Creative Kind | The category or type of creative |
| Creative Parent | The parent creative in the hierarchy |
| Creative Duration | The duration of the creative |
| End Card Localization | The localization of the end card |
| End Card Name | The name of the end card |
| End Card Type | The type of the end card |
| Is Ec | Indicates whether an end card is present |
| Creative Localization | The localization of the creative |
Metrics labeled as D{x} represent cumulative values over X days after install.
Examples:
- D7 Revenue — revenue within 7 days
- D30 Revenue — revenue within 30 days
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.
| Name | Description |
|---|
| Impressions | The total number of ad impressions |
| Clicks | The total number of clicks |
| CTR | The click-through rate (Clicks / Impressions) |
| Installs | The total number of installs |
| Cost | The total cost of traffic acquisition |
| CPI | The cost per install (Cost / Installs) |
| CPM | The cost per 1000 impressions (Cost / Impressions * 1000) |
| CPC | The cost per click (Cost / Clicks) |
| IPM | Installs per 1000 impressions (Installs / Impressions * 1000) |
These metrics represent the total revenue generated by the cohort and its components. Total revenue consists of multiple monetization sources:
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
Predicted metrics are derived from predicted revenue:
- Predicted ARPU = Predicted Revenue / Installs
- Predicted ROAS = Predicted Revenue / Cost
- Predicted ARPPU = Predicted Revenue / Paying Users
| Name | Description |
|---|
| Act. Rev. before ref. D{x} | The actual cumulative cohort revenue before refunds, net of platform fees and taxes |
| Act. Revenue D{x} (Net) | The actual cumulative cohort revenue after refunds |
| Act. Revenue D{x} (Ref.) | The total amount of refunds within the cohort |
| Pred. Revenue D{y} → D{x} (Net) | The predicted revenue increase between two time horizons, where D{y} is the starting point and D{x} is the target horizon |
| Act. ROAS D{x} (Net) | Return on ad spend calculated as Revenue / Spend |
| Pred. ROAS D{x} (Net) | Forecasted ROAS based on predicted revenue. This metric uses the latest available D{y} and is updated as new data becomes available |
| Act. ARPU D{x} (Net) | Average revenue per user (Revenue / Installs) |
| Pred. ARPU D{y} → D{x} (Net) | Forecasted ARPU based on predicted revenue |
| ROI | Return on investment ((Revenue - Spend) / Spend) |
| Act. Ad Revenue D{x} | The actual cumulative ad revenue of the cohort |
| Pred. Ad Revenue D{y} → D{x} | The predicted ad revenue growth between two time horizons |
| Act. Ad ROAS D{x} | ROAS calculated using ad revenue |
| Pred. Ad ROAS D{x} | Forecasted ROAS based on predicted ad revenue. This metric uses the latest available D{y} and is updated over time |
| Act. Ad ARPU D{x} | Average ad revenue per user |
| Pred. Ad ARPU D{y} → D{x} | Forecasted ad ARPU |
| Act. IAP Rev. before ref. D{x} (Net) | The actual cumulative IAP revenue before refunds |
| Act. IAP Revenue D{x} (Net) | The actual cumulative IAP revenue after refunds |
| Act. IAP Revenue D{x} (Ref.) | The total IAP refunds |
| Pred. IAP Revenue D{y} → D{x} (Net) | The predicted IAP revenue growth between two time horizons |
| Act. IAP ROAS D{x} | ROAS calculated using IAP revenue |
| Pred. IAP ROAS D{x} | Forecasted ROAS based on predicted IAP revenue. This metric uses the latest available D{y} and is updated over time |
| Act. IAP ARPU D{x} | Average IAP revenue per user |
| Pred. IAP ARPU D{y} → D{x} (Net) | Forecasted IAP ARPU |
| Act. Subs Rev. before ref. D{x} (Net) | The actual cumulative subscription revenue before refunds |
| Act. Subs Revenue D{x} (Net) | The actual cumulative subscription revenue after refunds |
| Act. Subs Revenue D{x} (Ref.) | The total subscription refunds |
| Pred. Subs Revenue D{y} → D{x} (Net) | The predicted subscription revenue growth between two time horizons |
| Act. Subs ROAS D{x} | ROAS calculated using subscription revenue |
| Pred. Subs ROAS D{x} (Net) | Forecasted ROAS based on predicted subscription revenue. This metric uses the latest available D{y} and is updated over time |
| Act. Subs ARPU D{x} | Average subscription revenue per user |
| Pred. Subs ARPU D{y} → D{x} (Net) | Forecasted subscription ARPU |
These metrics include all types of purchases and are used to evaluate campaigns focused on acquiring paying users.
| Name | Description |
|---|
| PU D{x} (Net) | The number of paying users in the cohort by day D{x} |
| PU Revenue D{x} (Net) | The total revenue generated by paying users |
| CVR (Install → PU) D{x} | Conversion rate from install to paying user |
| ARPPU D{x} | Average revenue per paying user |
| Act. Purchases per PU D{x} | The average number of purchases per paying user |
| CPA PU D{x} | Cost per paying user (Cost / Paying Users) |
These metrics include only subscription-related events and are used to evaluate campaigns focused on subscription conversions.
| Name | Description |
|---|
| Trial Starts D{x} | The number of trial starts within the cohort |
| Trial Conversions D{x} | The number of users converted from trial to paid |
| Paid Starts D{x} (Sub) | The number of first payments without a trial |
| Renewals D{x} | The number of subscription renewals |
| CVR (Install → Trial Start) D{x} | Conversion rate from install to trial |
| CVR (Trial → Paid) D{x} | Conversion rate from trial to paid |
| CVR (Install → Paid) D{x} | Conversion rate from install to paid |
| CPA Trial Start D{x} | Cost per trial start |
| CPA Trial Conversion D{x} | Cost per trial conversion |
| CPA Paid Start D{x} | Cost per first payment |
These metrics include only one-time purchases and are used to evaluate campaigns focused on in-app purchases.
| Name | Description |
|---|
| IAP Purchases D{x} (Net) | The number of IAP purchases excluding refunds |
| IAP Purchases D{x} before ref. | The number of IAP purchases before refunds |
| IAP Purchases D{x} (Ref.) | The number of refunded IAP purchases |
| IAP PU D{x} | The number of users who made IAP purchases |
| CVR (Install → IAP) D{x} | Conversion rate from install to IAP purchase |
| IAP ARPPU D{x} (Net) | Average revenue per IAP paying user |
| IAP Purchases per PU D{x} | The average number of purchases per user |
| CPA IAP D{x} | Cost per IAP paying user |
These metrics are used to track user retention and understand how users return to the app over time.
Retention may differ depending on the data source:
| Name | Description |
|---|
| Retention D{x} SDK | Calculated as D{x} / D0, where D{x} is the number of users active on day x and D0 is the number of users active on the install day, based on SDK session data |
| Retention D{x} MMP | Calculated as D{x} / D0, where D{x} is the number of users active on day x and D0 is the number of users active on the install day, based on MMP data |
These metrics are used to estimate future cohort performance based on early data.
Predictions are calculated for the following horizons: D1 / D7 / D30 / D90 / D180 / D360 / D720
| Name | Description |
|---|
| Pred. Total Revenue D{y} → D{x} (Net) | The predicted cumulative cohort revenue at the target horizon D{x}, based on data available at D{y} |
| Pred. ARPU D{x} | The predicted average revenue per user |
| Pred. ROAS D{x} | The predicted return on ad spend |
| Pred. ARPPU D{x} | The predicted revenue per paying user |
| Prediction Error | (Predicted Revenue - Actual Revenue) / Actual Revenue |
The model uses a baseline + residual prediction approach.
Predictions are calculated at the user level (client_id) and then aggregated at the report level.
Uses early-stage revenue:
Predicts additional revenue beyond baseline.
The model uses early user data:
- revenue progression
- behavioral patterns
- contextual attributes (country, app, source, device)
Based on this, it predicts:
- Total Revenue
- Ad Revenue
- IAP Revenue
- Subscription Revenue
Derived metrics:
- Predicted ARPU
- Predicted ROAS
- Predicted ARPPU
- Prediction Error
- At least 1000 users within the analyzed time window (depending on the prediction horizon)
- Availability of early revenue (D1 / D7 / D30)
- D1 Revenue > $100 recommended
- Historical data improves prediction accuracy
- early performance evaluation
- campaign scaling decisions
- traffic quality comparison
The report is used to monitor performance and optimize acquisition campaigns.
Analysis typically starts at the overall app level and can be broken down into traffic sources, campaigns, and geographies.
Common metrics:
- Spend
- CPI
- ARPU
- ROAS
- Retention
- payer conversion
Weekly analysis is recommended due to strong seasonality in traffic acquisition.
- Week 1 cohort: ROAS D7 = 15%
- Week 2 cohort: ROAS D7 = 25%
This reflects differences in cohort performance across time.
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