Aggregation

What is Aggregation in Mobile Analytics?

In mobile analytics and advertising, aggregation refers to the process of collecting, combining, and summarizing data from multiple sources into a single, unified view. Rather than analyzing data in isolated silos, aggregation brings different data points together so teams can understand performance across the full picture.

Aggregation is a foundational concept in mobile measurement. Attribution platforms, analytics dashboards, and reporting tools all rely on some form of aggregation to make data usable at scale.

What Gets Aggregated

In mobile app analytics and user acquisition, aggregation commonly applies to:

• Ad spend data: Combining cost figures from multiple ad networks (see: cost aggregation)
• Атрибутивные данные: Summarizing installs, events, and revenue across campaigns, networks, and time periods
• Monetization data: Combining in-app purchase revenue and ad revenue from multiple sources into a single revenue view
• User data: Grouping users into cohorts based on install date, channel, or behavior for analysis
• Campaign data: Rolling up performance metrics like impressions, clicks, installs, and ROAS across ad sets and campaigns

How Aggregation Works in Practice

Data aggregation typically happens at the platform level, where the analytics or attribution tool collects raw data from multiple sources (ad networks, SDKs, APIs, and internal systems) and processes it into structured reports.

For example, a mobile team running campaigns on five different networks might see:

• Five separate cost reports from each network
• Five different sets of attribution data

Aggregation combines these into a single dashboard view, where the team can see total spend, total installs, and blended ROAS across all five networks without manually compiling anything.

Aggregated Data vs. Raw Data

It is useful to understand the difference between aggregated data and raw data:

• Исходные данные is individual-level or event-level data that has not been processed or summarized. It contains the most granular detail but requires more work to analyze.
• Агрегированные данные is summarized across a defined dimension, such as campaign, date, country, or ad network. It is easier to read and compare but may obscure patterns that only appear at the raw level.

Both have their place. Aggregated data is useful for dashboards and high-level reporting. Raw data is useful for deeper analysis, custom reporting, and building data pipelines. Tools like Tenjin's DataVault and Raw Data Exporter support teams that need access to both.

Aggregation and Privacy

In the context of mobile measurement, aggregation also has a specific meaning related to user privacy. Privacy frameworks such as Apple's SKAdNetwork (SKAN) use aggregated reporting by design, meaning individual user-level data is not passed back to advertisers. Instead, campaign performance is reported as aggregated conversion data.

This type of privacy-preserving aggregation is increasingly common across the mobile ecosystem and requires analytics platforms to work with summarized signals rather than individual user events.

Why Aggregation Matters for Growth Teams

Without aggregation, mobile growth teams face a fragmented data environment. Data lives across networks, platforms, and tools, in different formats and at different levels of detail. Aggregation makes it possible to:

• Compare performance across channels in a single view
• Report on total spend, revenue, and ROAS accurately
• Spot trends and anomalies that would not be visible in siloed data
• Make faster, better-informed decisions

Связанные термины

• Агрегация затрат
• Гранулированные / необработанные данные
• Атрибуция
• SKAdNetwork
• Метрики мобильного маркетинга
• DataVault

Frequently Asked Questions About Aggregation

What does aggregation mean in mobile analytics? Aggregation means combining data from multiple sources or dimensions into a single summarized view. In mobile analytics, this typically applies to ad spend, attribution data, monetization data, and campaign performance.

Why is data aggregation important for mobile growth teams? Mobile campaigns run across many networks and platforms. Without aggregation, teams work with fragmented data that is hard to compare. Aggregation creates a single view of performance so teams can make faster and more accurate decisions.

What is the difference between aggregated data and raw data? Raw data is individual or event-level data in its unprocessed form. Aggregated data has been summarized across a dimension such as campaign, date, or country. Raw data provides more granularity. Aggregated data is easier to read and compare at a high level.

How does aggregation relate to attribution? Attribution platforms aggregate installs, events, and revenue by campaign, network, or channel so teams can see which sources are driving results. This aggregated attribution view is what connects spend data to outcomes.

What is privacy-preserving aggregation? Privacy-preserving aggregation means reporting campaign performance as summarized data rather than individual user-level events. Apple's SKAdNetwork uses this approach. Aggregated signals protect user privacy while still giving advertisers a view of campaign-level performance.

What is the difference between aggregation and cost aggregation? Cost aggregation is a specific type of aggregation focused on ad spend data. General data aggregation covers any type of performance data, including installs, events, revenue, and user behavior. Cost aggregation is one part of a broader aggregation strategy.

How does Tenjin support data aggregation? Tenjin's dashboard aggregates attribution, cost, and monetization data across sources so teams can see performance in one place. For teams that need to go deeper, DataVault and Raw Data Exporter provide access to granular and raw data for custom analysis.