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Adobe Analytics Data Feed: Building Reports With Hit-Level Data

​​With the growth of revenue, the number of clients, and marketing budgets, there's an increasing need for advanced analytics tools and new means of tracking your business processes.

One such tool is Adobe Analytics. It's a comprehensive suite of marketing analytics applications that can help you answer the most complicated questions about your business.

One of the best ways to get the most out of Adobe Analytics is to use its built-in dashboards. They will help you understand how all of your marketing data comes together.

Adobe Analytics UI
Adobe Analytics UI

However, there are situations where you need to compile marketing reports outside Adobe Analytics, use third-party tools to parse marketing insights, or combine your data with other sources. 

The Adobe Analytics data feed is the solution to this issue. In this post, you'll learn how to extract raw data from Adobe Analytics with this data feed and the other options to achieve this.

What is the Adobe Analytics data feed?

According to Adobe documentation, data feeds are an export of the clickstream data received by Adobe. These data can have two different formats:

  • Standard Data Feeds
  • Custom Data Feeds

🔖 In other words, a data feed is a configuration of what data you want to extract from Adobe Analytics and where you want it to go.

The data feed's key feature is that it shares server-level event data. That means analysts can see the individual actions of each visitor on the website.

Analysts can use raw data to build dashboards in third-party tools like Tableau and merge Adobe Analytics data with other sources to get a comprehensive picture of which marketing strategies work and which ones don't.

The platform delivers updated data in hourly or daily batches, depending on the user's preferences.

How to start using data feeds

However, to use data feeds, you have to meet several requirements:

  • Set up an FTP server. Adobe Analytics can only send data feeds to server destinations. If you don't have a server destination of your own, Adobe can provide you with an FTP server and a small amount of space for your primary needs.
  • Arrange a data transfer process. Before using data feeds, you need a working implementation that sends your data to the Adobe data collection server. If you don't know how to do this, here's a step-by-step guide by Adobe.

Get all of the required permissions. Finally, you have to ensure that your account has the correct Analytics admin rights or belongs to a product profile that can access data feeds.

How data feeds work

Creating your own data feed allows Adobe Analytics to know exactly what you want to be included and where you want to send it.

When creating a data feed, you have to specify certain parameters first. 

Here's the full list of data feed fields

  • Name. The primary field of the data feed that sets its name. Each data field name should be unique within the same report suite.
  • Report suite. A report suite is a data silo that Adobe Analytics uses to generate reports. Users can create different data silos for the same report suite, provided that data feeds have different column definitions.
  • Email. Adobe Analytics automatically sends an email notifying the user that the data feed processing has been completed.
  • Feed interval. Users can choose between hourly and daily feed intervals. Hourly feeds provide fresh data each hour, while daily feeds include a single day's worth of data.
  • Delay processing. This field allows users to set a particular time before processing the data feed. The delay enables offline devices to go online and share the collected data.
  • Start/end date. Here, users specify the day they want Adobe Analytics to start processing their data feeds. When the feed reaches the end date, the platform will stop generating them until the user creates a new one.
  • Continuous feed. The last field allows users to turn on infinite feed generation. This means there's no end date, and the platform will generate data feeds until the user turns them off manually.
How to create a data feed
Data feed configuration

Where to store data feeds?

As mentioned before, users need to set up an FTP server to store their data feeds. But actually, there are other options.

SFTP (SSH File Transfer Protocol) servers could also transfer data feeds. To establish a connection, users will need an SFTP host, username, and destination.

Amazon S3 is another destination that you can use to store data feeds. Adobe Analytics can send all data directly to S3 buckets. To successfully set up a connection, you need a bucket name, an access key ID, and a secret key. Amazon S3 also has its own bucket naming requirements that you need to learn before setting up the connection.

How to extract data feeds to FTP servers
FTP server destination configuration

Even though raw data feeds are a powerful dataset that bring more flexibility and accuracy to your analytics, they require additional work. For example, here's how Adobe's documentation describes the process of defining a page view with data feeds:

Page views can be calculated by counting when there is either a value in post_pagename or post_page_url. You can use similar logic to count custom links:

post_page_event = 100 to count custom links

post_page_event = 101 to count download links.

post_page_event = 102 to count exit links.

Adobe Analytics data feed use cases

Now that you know how to set up a data feed, it's time to explore some use cases.

Analysts have come up with some unique ways to use data feeds with third-party tools.

Automate data flow between two software systems

For example, Frederik Werner, Senior Digital Analytics Specialist at DHL, shared his experience in processing data feeds with Apache NiFi for the Adobe Experience Platform.

Apache NiFi is a software solution that automates data flow between software systems. In his article, Frederik explains how to merge data feeds with other files and upload standardized data to the Adobe Experience Platform.

Here's the schematic flow of feeding TSV files to convert them to CSV:

How Apache NiFi works with data feeds
Processing data feeds with Apache NiFi

With standardized data on the FTP server, the last thing you need to do is connect the Adobe Experience Platform to your FTP server and map all your data.

🚀Learn how to automate the data mapping process with our list of top data mapping tools. 🚀

Visualize customer journeys 

Data feeds can also be used to visualize customer journeys with the R programming language. Trevor Paulsen, Group Product Manager at Adobe, explains how to use R and Plotly to create scatter plots based on data feeds.

How to visualize customer journey
A customer journey scatter plot


Eventually, with the help of code and analytics efforts, data feeds turn into a Sankey diagram that shows what touchpoints influence the conversion:

Conversion flow diagram for data feeds
Conversion flow diagram

Due to customizability, data feeds can be used for almost any report or analytics task. Even though they require additional time and effort for implementation, the level of visibility they provide exceeds the time it takes to set everything up.

Data feeds vs. Adobe Analytics API

Another way to get data from Adobe Analytics is the platform's API. The API gives users access to all types of data, including custom reports and profiles, as well as real-time data.

In contrast to data feeds, an API uploads individually calculated metrics and ready-made reports to any destination of the user's choice.

This approach will better suit analysts who need to automate marketing reporting outside of the Adobe Analytics UI. Users can load all of their metrics into a business intelligence tool to build custom reports with all metrics at hand.

However, extracting Adobe Analytics' reports requires coding knowledge since dashboards use jQuery, D3.js for visualization, and the JSON data format for storing analytics data.

Ryan Praskievicz shared his experience of setting up real-time reporting and building a dashboard in an external environment via Adobe's API in his blog. Here's an example of a simple real-time dashboard that Ryan was able to pull out of Adobe Analytics:

Adobe Analytics report
Extracting ready-made reports via the Adobe Analytics API

Still, if you need to make any changes to your existing report or create a new one, you'll have to use a lot of code. The more complex your dashboard, the more lines of code you'll have to write. Although this option provides great flexibility for users on how to manipulate their data, the biggest drawback of Adobe's API is that this process takes time and requires technical know-how, which not every analyst has at their disposal.

How to get real-time Adobe Analytics reports with any of the metrics you need 

Using an API

Even though APIs provide data at any level of granularity, they still lack automation. This is because you need to trigger the API each time you need new data.

There are two ways to achieve real-time reporting with APIs. The first one is to use custom code for the automation that will schedule data updates in the way the user needs, trigger the API automatically, choose the proper metrics to be included in the report, and more.

It's a good option for analysts with programming skills. However, the code requires continuous maintenance and updates in case of any changes to the API. Plus, not all analysts know how to write code and set up database connections, so that only makes things worse.

Using marketing ETLs

Another way to get real-time reports is to use ETL solutions.

🚀Learn what ETLs are and how they can help you automate your marketing reporting.🚀

Let's review ETL solutions using the example of Improvado. One of the reasons why Improvado is a great fit for Adobe Analytics is because it is an official Adobe partner. That's why Improvado can automatically extract both data feeds and calculated metrics via APIs.

So, what are ETLs all about? An ETL platform automatically extracts any data from Adobe Analytics with the help of its predefined extraction patterns. Analysts create an extraction template once, then the process runs until it's stopped manually.

How a marketing ETL can help analysts with no coding experience

Importantly, marketing ETL tools like Improvado offer users a code-free environment, meaning that analysts with no programming expertise can set up the process by themselves without help from a development team.

Analysts can work in a spreadsheet-like UI with drag-and-drop functionalities, which replaces endless lines of code with user-friendly, straightforward features that enable analysts to do the same work much faster.

Improvado connected sources
Improvado extraction workflow


Furthermore, an ETL platform automatically transforms raw data. The transformation process includes data cleansing, normalization, data mapping, clearing redundant data rows, and more. Therefore, analysts can skip manual data harmonization activities and dive straight into reviewing their reports.

Improvado data transformation
Improvado data transformation

Schedule a demo call to learn how you can transform Adobe Analytics raw data using a spreadsheet-like UI tool

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How marketing ETLs handle data integration 

The accessibility of marketing and sales data is yet another feature that adds value for marketers. With Improvado, analytics teams get straightforward access to data from 300+ sources that can be merged with Adobe Analytics reports.

Improvado data connectors
Some of Improvado's data connectors

Insights from DSP platforms (Amazon DSP, Sizmek), marketing automation software (Marketo), email automation solutions (Mailchimp), CRMs (HubSpot), and other channels can be merged into a single comprehensive report. In this way, marketers get a holistic picture of the customer journey and can better track the performance of each touchpoint.

How ETL platforms solve data storage issues

As previously mentioned, data feeds can only be stored in FTP storage.  If your company uses a data warehouse to store marketing insights, an FTP server becomes a redundant node on the way to the required destination.

ETL platforms, such as Improvado, automatically push data to any data warehouse of your choice. Whether you're using BigQuery, S3, Snowflake, or any other data warehouse, the platform puts your data right where you need it.

How ETL platforms help with data visualization

Storing your insights in one place is only half the battle. The final step is to build an all-in-one dashboard that allows you to monitor your marketing efforts in real time from a single tab.

🚀Snatch up any of these 34 charts to build an insightful revenue dashboard.🚀

Advanced ETL platforms seamlessly integrate with business intelligence tools and supply them with relevant insights to keep dashboards up to date. For example, Improvado connects to Tableau, Google Data Studio, Chartio, and 10+ more visualization platforms.

🚀Get an overview of the top 30 data visualization tools to find the perfect one for your business objectives. 🚀

Companies that don't have the required expertise to build detailed dashboards may benefit from Improvado's professional services. The company's dedicated analysts help teams to set up marketing attribution, draw up coherent dashboards, and discover non-obvious opportunities to improve their campaigns.

Data feeds vs. API vs. ETL

Each of the described data processing methods has its pros and cons. We built a comparison table to help you choose the option that best suits your requirements.

Data Feed API ETL
Provides hit-level data to build precise attribution models. Extracts precalculated metrics and ready-made reports. Capable of extracting both hit-level data and ready-made reports
Stores data in FTP servers. Stores data directly in a data warehouse. Stores data directly in a data warehouse.
The extraction process is set up inside the Adobe Analytics interface. To set up the extraction, analysts should have programming expertise. Configuration is done with code. Analysts can set up the extraction process inside a spreadsheet-like UI with drag-and-drop capabilities.
Data feeds can only be merged manually with data from other sources. Reports and metrics extracted via an API require additional code or manual aggregation to integrate them with other sources. Analysts can merge data from 300+ sources in several clicks.
Insights are manually uploaded to visualization platforms. Insights are manually uploaded to visualization platforms. ETL systems sync with ETL platforms to supply dashboards with up-to-date data.
Adobe Analytics sends data feeds in daily or hourly batches. APIs need from 90 minutes to 2+ hours to deliver data (depending on the server latency). ETL systems push new information to the data warehouse each hour.
Data feeds should be cleansed and normalized before the analysis. APIs deliver ready-made reports with analysis-ready metrics. ETL systems automatically normalize any data for further analysis.


You got the data. Improvado delivers it where you need it.

If you still don't know how to export data from Adobe Analytics, Improvado can give you a hand. As Adobe's official partner, we extract ready-made reports and hit-level data to get it where it truly belongs: your data warehouse.

Improvado also works with 300+ marketing and sales connectors that you probably already use in your day-to-day analytics tasks. So, why not give it a try?

Schedule a call to learn how you can build insightful reports without manually aggregating data from disparate sources.

Export Adobe Analytics data feeds to the warehouse of your choice

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