What is data aggregation?
Data aggregation is the process of bringing all your marketing data together in one place where it can be conveniently analyzed. While there are significant difficulties in just achieving that step, the real challenge is ingesting the data in a standardized, normalized way that enables you to analyze and extract real insights.
In the past, data was typically aggregated manually using spreadsheets. This process, however, requires a significant amount of tedious work to reformat, standardize, and prepare the data for analysis. 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 ROI of their marketing efforts as a whole.
Uses of data aggregation
One of the main use cases of data aggregation for app businesses is cost aggregation, which refers to the process of reconciling all marketing spend across 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.
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. As import.io highlights in regards to standardizing data:
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. In particular, our guide to cost aggregation provides three main methods for aggregating cost data, including:
- 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. With a direct API integration, this approach is often the most accurate and can also provide the most amount of relevant data, such as 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 they may not be able to pull the entirety of the campaigns data. In addition, tracking link parameters can often have discrepancies in the data of roughly 30%, leading to inaccurate and incomplete reporting.
- Manual reconciliation in a spreadsheet: Finally, this approach is the least desirable data aggregation method as it’s both time consuming, can be difficult to standardize data, and can result 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. 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.
How Singular facilitates data aggregation
Singular connects to thousands of marketing data sources and ingests it in many different ways, including API integrations and ETL. Singular also has industry-leading ways to standardize and normalize aggregated data without 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.