If you haven’t yet heard of Adobe’s Customer Journey Analytics (CJA), it’s a mighty step up from the old world of digital analytics that many of us come from – and it’s completely transforming the way companies analyze and think of their customers’ end to end experience. CJA is powerful in many ways because it […]
Category: Data Processing
How to Setup a Data Lake and Start Making SQL Queries with Adobe Analytics, AWS S3, and Athena
Follow @BikerJaredThe phrase “big data” is used so often it’s almost trite. These days, nearly all large enterprises have established a data science or data integration practice that is used for analysis projects. In my experience, however, many smaller companies (or often smaller teams within large enterprises) have yet to adopt any sort of big data […]
Build Your Own Cross-Device Marketing Attribution with Apache Spark and R
Follow @trevorwithdata Over my last few posts I’ve been focusing on how to do better marketing attribution using Adobe Analytics Data Feeds and Apache Spark coupled with R. You can read all about those attribution techniques here: Multi-Touch Attribution Using Adobe Analytics Data Feeds and R The Two Best Models for Algorithmic Marketing Attribution That said, […]
How to Use Classifications With Adobe Analytics Data Feeds and R
Follow @trevorwithdata Adobe Analytics Classifications is one of the most useful and popular features of Adobe Analytics, allowing you to upload meta-data to any eVar, prop, or campaign that you may be recording in Adobe Analytics. Classifications are useful when you need to do things like: Classify your marketing campaign tracking codes into their respective marketing […]
Using Secondary Sort to Enhance Adobe Data Feed Processing in Hadoop
In my last post, I described the basics for processing Adobe Analytics Click Stream Data Feeds using Hadoop. While the solutions outlined there will scale remarkably well, there is a more memory efficient way to do it. Having this flexibility is nice if you have lots of CPU cores available but not as much ram. […]
Introduction to Processing Click Stream Data Feeds with Hadoop and Map/Reduce
In an earlier post, Matt Moss showed how to process data feed data using an SQL database. This can be useful in a pinch when you have a smaller amount of data and need an answer quickly. What happens though when you now need to process the data at a large scale? For example, you […]
Using Adobe Analytics Data Feeds and SQL for Basic Reporting
Three DB SQL’s walk into a NOSQL bar. A little while later… they all walked out, because they couldn’t find a TABLE… Joking aside, online marketers frequently use analytics tools like Adobe Analytics, but find that the granularity and accessibility of the data in the tool doesn’t meet their needs. A few examples: Loading Adobe Analytics […]
Parsing Products and Events in ClickStream Data Feeds
A lot of companies that I’ve worked with are initially confused when processing Adobe Analytics Data Feeds. The data comes out of Adobe Analytics in TSV format and you’d naturally expect that the data is ‘flat’ (meaning just rows and columns). Unfortunately, this isn’t the case. Columns like ‘post_product_list’ and ‘event_list’ are lists of data that […]
Dealing with Special Characters When Parsing Adobe Analytics Data Feeds
Adobe Clickstream Data Feeds are the most granular way to view your analytics data. They effectively contain all the information that Adobe Analytics needs to build its reports. Having a good understanding of how to use these feeds will allow you to use Analytics data in ways that aren’t possible through LiveStream, the Web Services […]
Visitor Level Aggregations Using R and Adobe Analytics Data Feeds
Follow @trevorwithdata Visitor level aggregations (or as I like to call them, “visitor rollups”) are one of the most useful and meaningful things you can do with an Adobe Analytics data feed. If you ever want to do cluster analysis to find interesting marketing segments, propensity modeling to find likely converters, or product affinity analysis for cross […]