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 […]
Visualizing the Customer Journey with R and Adobe Analytics Data Feeds
Follow @trevorwithdata Much has been said regarding the benefits of multi-touch or algorithmic attribution models to understanding your customers’ conversion paths, but running analyses merely looking at some numbers in a table doesn’t quite inspire insight in the same way that a well-constructed visualization can. So, in this post, I’m going to give you two great ways […]
Amp Up Your A/B Testing Using Raw Analytics Data, Apache Spark, and R
Follow @trevorwithdata Experimentation and testing (also known as A/B testing or split testing) is an essential tool in the digital information age. If you’re not sure what A/B testing is, it’s the process of comparing two versions of a web page or app screen to see which performs better. A/B testing tools such as Adobe Target, Optimizely, […]
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, […]
Attribution Theory: The Two Best Models for Algorithmic Marketing Attribution – Implemented in Apache Spark and R
Follow @trevorwithdata In my last post, I illustrated methods for implementing rules-based multi-touch attribution models (such as first touch, last touch, linear, half-life time decay, and U-shaped) using Adobe Analytics Data Feeds, Apache Spark, and R. These models are indeed useful and appealing for analyzing the contribution any marketing channel has to overall conversions. However, they […]
Multi-Touch Attribution Using Adobe Analytics Data Feeds and R
Follow @trevorwithdata One of the hottest topics in the digital marketing space has always been marketing attribution. If you’re unfamiliar with this problem space, (I’d be surprised, but) there are lots of excellent explanations out there including this one. In a nutshell, companies have a lot of marketing outlets – search, display ads, social networks, email, […]
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 […]