Learn about data aggregation and its importance for businesses. This article covers essential techniques, benefits, and best practices to help you make informed decisions with your data.
Data aggregation is the process of consolidating and summarizing large amounts of raw data into a more digestible format. Once the aggregation process is complete, the data is placed in a central repository like a data warehouse where team members can easily access and use it for analysis, marketing campaigns, and decision-making.
The aggregation process typically consists of three stages:
Data collection. The data aggregation tool retrieves raw data from different sources, such as apps, websites, IoT devices, etc.
Processing. After the collection is complete, the tool processes the data. Some tools also use predictive analytics to make trends or performance forecasts.
Presentation. The data aggregator presents the data in a summary format, such as statistics, a report, or a customizable dashboard.
One example is clinical data aggregation, which researchers use to evaluate the pharmaceutical trials of a new drug. Aggregated clinical data makes it easier to monitor safety and spot trends in patients’ reactions. In the case of financial data aggregation, it enables a person to see all of their financial information (bank accounts, credit cards, loans, investments) in one place for easier money management.
A single platform to collect, unify, and connect your customer data
Customers interact with businesses in a myriad of ways: visiting your website to learn more about a product, discovering a promotion via email, or visiting a physical location. Being able to connect each of these interactions into a unified view of the customer puts businesses at a significant advantage – but how, exactly, do you go about doing this?
With data aggregation, you can glean insights from different customer touchpoints and use them to guide future business decisions.
The four most common types of data aggregation are:
Time aggregation collects all data from a single source during a set period of time. If you want to know how many customer purchases were driven by email campaigns over the past six months, you would use time aggregation.
Spatial aggregation retrieves data from multiple data sources during a set period of time. Measuring the conversion rate of your ads across desktop, mobile, and apps over the past three months requires spatial aggregation.
Manual data aggregation means exporting data file by file and then manually uploading it into a platform for aggregated data. Since this aggregation method is time-consuming, it’s suitable for organizations with small data sets.
Automated data aggregation involves a software program that extracts and presents the aggregated data with little to no human intervention.
If raw data is a puzzle piece, then aggregated data is the completed puzzle, providing you with the insights you need to steer your business in the right direction. As such, data aggregation benefits organizations in three important ways.
Data aggregation helps businesses measure the efficiency of their marketing campaigns, identify changes and patterns in customer behavior, and implement strategies for sales.
Instead of relying on siloed data for important decision-making, businesses can consider all of the relevant metrics before investing in new product development, new marketing channels, customer retention campaigns, etc.
For example, a business that wants to prioritize one social media platform for its marketing activities in the next quarter can consult the aggregated data from each platform. By comparing KPIs, the marketing team can select the one with the highest chance of success.
Aggregating and hosting data in a single platform also improves access to accurate data. Instead of using spreadsheets or different apps to track data, which can create data silos, it’s all accessible in one dashboard.
According to a Forrester report, “managing data and sharing insights that drive actions across organizational silos” are the biggest challenges businesses face in meeting their sales and marketing goals. Data silos create confusion and arguments over whose data is correct, but these issues can be solved by aggregating data from all sources into a single platform.
Platforms that aggregate and present data in a summarized format also enable employees to self-serve information, which reduces your team’s reliance on the IT department to retrieve it. From sales to product development, every department can quickly retrieve data assets and be more efficient in their workflows.
The data aggregation process is industry-agnostic, with existing use cases in retail, healthcare, and many other verticals. A common thread throughout these use cases is personalization, thanks to the wealth of actionable insights that data aggregation provides.
In retail, data aggregation can help businesses understand the complete customer journey and double down on the marketing strategies that drive sales. Since customers connect with retailers via online and offline channels, data aggregation maps these interactions in a unified view.
Bonobos, a clothing retailer, used data aggregation to link engagement with Facebook ads to in-store purchases. With Twilio Segment’s customer data platform (CDP), the retailer was able to merge online and offline customer data and verify how many sales were driven by the Facebook campaign.
In healthcare, data aggregation tools improve the quality of care by facilitating personalization. Since patient data typically sits in separate systems, both online and offline, healthcare providers struggle to get a holistic overview of their patients and provide the care they need.
Health company Chopra Global also leveraged Segment to unify customer data and use these insights to build new features on its app. It was able to track trends across its user base and prioritize features that would have the most impact instead of being influenced by a minority of users who were more open with their feedback.
Banking, lending, insurance, and other financial services are no longer tied to the physical channel, with many providers embracing digital solutions. With more digital touchpoints, banks and other financial service providers also collect more data they can aggregate and use to tailor their products.
Insurance provider Toggle relied on Segment’s CDP to connect customer data from all its sources, including its website, analytics, customer support, and marketing automation tools. This helped them get an overview of the customer journey, build detailed customer profiles, and launch personalized marketing and email nurture campaigns.
Aggregating data comes with risks and challenges you should be aware of and proactively address.
When you use a third-party data aggregation tool, it needs to be equipped with advanced security features to minimize the risk of attackers gaining access to your data. Encryption protocols, regular security testing, and time-bound access are a few must-have features.
To get trustworthy results, the data aggregation software must receive clean, accurate, and consistent raw data. So before implementing a data aggregator, it’s essential to develop a good tracking plan that will save you time on data validation and help you prevent low-quality data from influencing your organization.
Data aggregation software must be able to integrate with all the tools you use to manage data, such as Google Analytics, customer relationship management, and marketing automation tools. With a lack of integration capabilities, silos will remain and leave you with an incomplete picture of your data.
The Segment CDP allows startup and enterprise companies to make the most of their customer data.
As opposed to a data management platform (DMP), the CDP aggregates first-party customer data from all touchpoints to provide a unified overview of your customer. (The main difference between DMPs and CDPs is that the former deals with second and third-party data while the latter typically handles first-party customer data.)
After unifying your data, Segment can also forward it to different destinations, such as a sales funnel builder, to provide a high level of personalization and power all your tools with the same high-quality data.
An example of data aggregation in action is compiling customer data across online and offline channels to better understand what drives the purchasing decisions of different demographics.
The main types of data aggregation are time, spatial, manual, and automated.
The difference between time vs. spatial aggregation lies in whether it collects data from one source (time) or multiple sources (spatial). The difference between manual vs. automated aggregation is straightforward — the former relies on data scientists to manually aggregate the data, while the latter relies on software to complete the process.
Data processing is a part of the data aggregation process. Some data aggregators use machine learning and predictive analytics during processing to glean insights from the raw data.