Data aggregation

Modern marketers have a massive problem: their data is in 15, 50, even 500 places. Those places range from ad partners to marketing platforms to app stores to internal databases. Without bringing all or at least most of this data together to analysis, it’s hard to know how to grow optimally. The solution: data aggregation.

Data aggregation is the process of collecting, consolidating, and summarizing data from multiple sources into a unified format for analysis and decision-making. In marketing, this means integrating data from various channels such as ad platforms, analytics tools, and CRM systems to provide a comprehensive view of performance metrics, customer/user/player behaviors, and campaign effectiveness. For advertisers, it means collecting and unifying all your cost and campaign data in one place.

Bringing all your data together sounds simple.

It’s really not.

The first step is just bringing all your marketing data together — especially cost data — in one place where it can be conveniently analyzed. There are significant difficulties in achieving just that step: many tools that say they do cost aggregation or data aggregation don’t actually collect all the data and all the granularities that marketers might need.

The second massive challenge, of course, is ingesting the data in a standardized, normalized way that enables you to analyze and extract real insights.

That’s a key problem.

In the past, marketers typically aggregated data manually using spreadsheets. This process, however, requires a significant amount of tedious work to reformat, standardize, and prepare the data for analysis. (And it’s common to make small mistakes that snowball.) For modern marketers that often use numerous media channels, platforms, and campaigns for their business, this manual data aggregation process is far too inefficient for analytical purposes. Ultimately, the process of automating data aggregation enables marketers to understand and analyze the performance of each individual campaign as well as the return on investment (ROI) of their marketing efforts as a whole.

Marketers engage with audiences across many platforms and via many channels. Aggregating data ensures that their performance insights are not siloed. And that enables a holistic understanding of marketing efforts.

This comprehensive view is crucial for evaluating ROI, optimizing campaigns, and making informed strategic decisions.

One of the main use cases of data aggregation for mobile app marketers is cost aggregation, which refers to the process of collecting and analyzing all marketing spend across all channels and platforms. As mobile marketers often deal with multiple marketing platforms, media partners, and campaigns, the process of aggregating cost data can be quite challenging without the right tools, like Singular.

Obviously, the task of collecting cost data from multiple sources can be extremely time-consuming if done manually. Equally if not more challenging, however, is the process of standardizing this data into a format that facilitates comparison and analysis across platforms.

This is a crucial step since the accuracy of insights from data analysis depends heavily on the amount and quality of data used. It is important to gather high-quality accurate data and a large enough amount to create relevant results.

Having a unified and accurate picture of ad spend is essential to improving the ROI of marketing campaigns over time.

Singular’s guide to cost aggregation provides three main methods for aggregating cost data. Only one of them is idea, and it is the method Singular uses:

• Using data connectors for all media sources: With this approach, data connectors sync with each media source in order to collect and unify cost data. A direct API integration, this approach is the most accurate and can also provide the most amount of relevant data, such as ad creative, targeting, bidding, and so on.
• Using mobile attribution link parameters: Tracking links with URL parameters is another approach to mobile attribution. This approach does have several limitations as it frequently cannot pull the entirety of the campaign’s data. In addition, tracking link parameters can often have discrepancies in the data of roughly 30%, leading to inaccurate and incomplete reporting. In some cases, acquiring cost and other data via tracking links is necessary.
• Manual reconciliation in a spreadsheet: This approach is the least desirable data aggregation method. It’s time consuming, makes it hard or almost impossible to standardize data,  and generally results in human error.

In practice, the first approach of using direct API integrations is the only way to ensure 100% data accuracy and data completeness, as well as complete agreement between what your ad partners report, what you see, and what actually gets spent. Similarly, this approach automates nearly all the manual tasks associated with data aggregation, making it much more efficient for marketers to uncover actionable insights from their data at speed.

The key components of data aggregation are pretty clear.

1. Data collection: Gathering raw data from diverse sources, including social media, email campaigns, websites, and third-party platforms.
2. Normalization: Standardizing data formats and structures to ensure consistency and compatibility across datasets.
3. Integration: Combining standardized data into a centralized repository, such as a data warehouse or analytics platform.
4. Analysis: Utilizing analytical tools and techniques to extract meaningful insights, identify trends, and inform decision-making.

To help you with this process, Singular connects to literally thousands of marketing data sources and ingests advertising data in many different ways, including API integrations, CSV files, and other kinds of data imports. Singular also has industry-leading ways to standardize and normalize aggregated data without manual processes or additional coding.

Below is a the step-by-step process that Singular uses for data aggregation:

• Connect: The first step is to connect a direct API integration with a media source to a web dashboard, email, or internal BI platform. Regardless if the data source is from mobile, desktop, or even offline, it can be ingested into our data connector API.
• Transform: The next step is to take the raw data from each media source and transform it into a standardized and enriched format. By unifying data into a single platform, this allows marketers to extract meaningful insights from their cross-platform campaign data.
• Analyze: Now that the data has been standardized, it can then be analyzed within our native reporting platform. This step allows marketers to visualize, pivot, or further transform data for the most efficient analysis possible.
• Load: Finally, if you have your own internal BI solution, Singular offers the ability to load the enriched and standardized data back into a data warehouse such as BigQuery. This data can then be used within any other reporting tool such as Tableau or Domo.

In summary, Singular provides mobile marketers with the data aggregation tools they need to analyze and optimize the performance of their marketing efforts. Marketers can access their data via API, ETL, or online.

Now, you see multiple benefits:

1. Enhanced decision-making: You get a comprehensive view of marketing performance, facilitating data-driven strategies.
2. Efficiency: You reduce the time and effort required to compile and analyze data.
3. Accuracy: You minimize errors associated with manual data handling.
4. Scalability: You support the growing volume and complexity of marketing data for all your internal data clients.