Cohort Report

1. Dashboard Overview

The Cohort Report helps advertisers gain deeper insights into their campaign/offer performance. By leveraging key metrics such as ROAS, Conversions, CPI, and Spend, advertisers can optimize their campaign strategies and improve long-term profitability. This report enables advertisers to monitor performance and adjust budgets accordingly to maximize overall return on investment (ROI).

While similar in purpose to the Performance Monitor Report, the Cohort Report introduces several enhanced dimensions. These include support for breakdowns by Report Type, Target Goal Window, Target Goal Type, and Postback Type. At the same time, it removes metrics that are less relevant to ROAS, such as impressions and clicks, while adding highly relevant indicators like Devnum, IAA+IAP Rev, and IAA+IAP ROAS.

🪄 Summary of Differences: Added Dimensions in the Cohort Report: Report Type Target Goal Window Target Goal Type Postback Type IAA+IAP Rev IAA+IAP ROAS Devnum (IAA, IAP, IAA+IAP) Removed Dimensions in the Cohort Report: App Name (Sub-source) Impressions, Clicks, eCPM, CPC, CTR, CVR, IVR

 

🪄Summary of [Report Type] Differences in Cohort Report: Calendar Day  For example, D0 refers to data generated within the natural calendar day of the user’s installation. If a user installs the app at 11 PM on July 1st, their D0 ROAS will be considered mature at midnight on July 2nd (end of July 1st as a calendar day). Recommended for advertisers using Appsflyer: Choose Calendar Day for data analysis. Cohorts For example, D0 refers to the full 24 hours starting from the moment of installation. If a user installs the app at 11 PM on July 1st, their D0 ROAS will not be mature until 11 PM on July 2nd. Recommended for advertisers using Adjust, Singular, etc.: Choose Cohorts for data analysis.

 

2. Key Features

Within this report, advertisers can:

✅ Filter Data: Filter by date/week, country/region, target goal window, package name, app ID, offer name, and more.

✅ View Data: Access ad performance by day or by week, with aggregated data based on the selected time range.

✅ Compare Data: Compare campaign performance across different dates to evaluate ROAS trends over time.

✅ Export Data: Export report data for further in-depth analysis.

 

3. Key Metrics Explanation

📌 IAA (In-App Ads Revenue): Refers to revenue generated from in-app advertising.

📌 IAP (In-App Purchase Revenue): Refers to revenue generated from in-app purchases.

📌 ROAS (Return on Ad Spend): Measures the return on ad spend (ROAS), indicating the revenue generated from advertising investments.

 

4. Common Use Cases

I. Viewing Data by Day

Advertisers can customize the date range to query data and view detailed daily performance metrics. This enables you to gain deeper insights into daily campaign performance and fluctuations, allowing for the timely identification of issues and the optimization of advertising strategies, ultimately leading to continuous improvement in overall campaign effectiveness.

• Metric Selection

  • Using D7 Purchase TCPE as an example, to view the number of unique paying users on D7, you can directly refer to D7_Devnum (IAP). With this metric, you can calculate:

    • Actual D7 TCPE Bid = Spend / D7_Devnum (IAP)

    • D7 Unique User Purchase Rate = D7_Devnum (IAP) / Conversions

    • D7 Average Revenue per Paying User = D7 IAP Rev / D7_Devnum (IAP) 

• Data Explanation

  • Based on the chart above, the selected and displayed data represent the quality metrics of a specific ad unit, including installs, spend, D7 ROAS, D7_Devnum (IAP), and more.

II. Viewing Data by Ad Type

Breaking down campaign data by Ad Type is an efficient and practical analysis method that helps advertisers gain a comprehensive understanding of performance differences across various ad formats. This approach enables more accurate evaluation of each ad type’s effectiveness, helping identify top-performing ad types and optimize the overall strategy to improve return on investment (ROI).

• Metric Selection
  • Select the desired time range and the Ad Type dimension. You may also choose more granular dimensions, such as Offer name or Ad type, depending on your analysis needs.
• Data Explanation

  • Based on the chart above, the selected and displayed data represent conversions, spend, and ROAS performance broken down by ad type.

III. Viewing Data by APP ID

By viewing data at the sub-source level, you can gain deeper insights into the performance differences across various traffic sources, providing a solid foundation for optimizing your campaign strategy. In the report, simply select the relevant Offer Name and check Location, Supply Package Name, and APP ID to view the performance of each sub-source. Based on this data, you can further enhance overall campaign performance by blocking low-quality sub-sources or applying other optimization measures.

• Metric Selection
  • Select a time range along with the [Location], [Supply Package Name], and [APP ID] dimensions. You may also include more granular dimensions such as [Offer Name] or [Ad Type] as needed.
• Data Explanation

  • Based on the selected dimensions in the chart above, the displayed data represents conversions, spend, ROAS performance, and the number of D7 unique paying user devices across different sub-sources.