The Quickest & Easiest Way to Extract Data from Adobe Analytics

by Michael Finn

Adobe Analytics is a powerful tool to help your organization understand customer behavior on your website and other digital channels. But anyone who has worked within the platform knows how complex it can be. One of the biggest challenges is finding a way to quickly, securely, and reliably integrate clickstream data from Adobe Analytics with your enterprise big data environment. Not to mention, clickstream (or behavioral) data is one of most complex and difficult types to deal with – there a high volume, thousands of semi-structured data elements, and different levels of abstraction (sessions, views, products, and events).

Extracting Your Adobe Analytics Data

Adobe offers two ways to get your data out of their system: batch and streaming.

Batch is more accurate and reliable (though there will inevitably still be episodes of missing or corrupted data), but sacrifices timeliness. The default frequency is every 24 hours, and the most frequent time that data can be synced is once every 4 hours.

The streaming interface, on the other hand, is near instantaneous, which is key for use cases such as fraud detection. However, the reliability factor just isn’t there. Reporting for this data will almost always vary significantly from Adobe Analytics reporting tools.

Without an easy, reliable, and fast way to integrate clickstream and enterprise data, it’s impossible to build a full view of the customer journey, which in turn makes it impossible to fully optimize your digital marketing efforts. Many organizations try to build their own solutions (a costly and time-consuming proposition) and still end up with subpar results.

Introducing the Adobe Analytics Adapter

The Adobe Analytics Adapter enables organizations to quickly and easily extract their raw Adobe Analytics clickstream data feed into their enterprise big data environment and do analytics on it. The Adapter automatically imports the data schema for your enterprise (including any custom fields you’ve created), ingests your data, and keeps it automatically up-to-date. It combines the speed of streaming with the accuracy of batch, and has built-in monitoring and alerts for errors and discrepancies to ensure maximum reliability. The Adapter connects to your Adobe Analytics account using an API, in order to automatically recognize event enrichments, lookups, and product strings with your customizations.

And who can beat the 99.9% match rates vs. Adobe Analytics?

What’s more, all the data can be synthesized on an individual level (including historical data) with an Identity Graph tool.

Watch how Dixons Carphone transforms Adobe Analytics data

Recently, leading electronics and mobile retailer, Dixons Carphone, was looking to improve their add-to-basket rates for online customers by personalizing product recommendations for each user. To accomplish that, they first needed to make sure their clickstream data feed from Adobe Analytics was structured in a usable way and able to be integrated with their enterprise data. When they deployed the Adobe Analytics Adapter, they instantly got tables architected with a standard schema that were available for analysis and queries right away. As a result, they’re now able to combine multiple data sources into their big data environment to build customized machine learning modeling with their data, as well as connect the model results with their activation channels (e.g., websites, optimizations tools, CRM, etc.). Chris Ward, a Data Scientist at Dixons Carphone, says Syntasa has been “really invaluable in speeding up our time-to-value with using Google Cloud Platform, in terms of the architecture of the Adobe Analytics data.”

Learn more about how an Adobe Analytics Adapter can help you.

Download the product sheet to discover the use cases, benefits and workflow for the ADOBE ANALYTICS ADAPTER.


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Tags:  Analytics

Michael Finn

VP of Product Marketing at Syntasa

Mike is VP of Product Marketing at Syntasa (a Marketing AI Platform loved by Marketers, Data Scientists, and Data Engineers for its ability to unlock real value from their enterprise and clickstream data).

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