Discover how integrating ChatGPT and R for automating analytics data quality checks can streamline your analytics process, ensuring reliable insights for informed decision-making. Follow this step-by-step guide to improve data quality and save time with this innovative approach.
How to Create Alerts for Adobe Analytics Using R and Slack
Intelligent alerting has been one of the most popular features of Adobe Analytics since its release years ago. It’s impossible to keep an eye on your data 24/7, and alerts are an excellent way to prevent missing out on important trends in your metrics over time. And while there are lots of amazing things you […]
How to Get Data From R into the Adobe Experience Platform
In my last post, I shared how CJA and the new cjar library from Ben Woodard changed the game for analyzing event data in R. However, it’s often not enough to only analyze data – you have to be able to do something with it. In this post, I’m going to show you how to […]
Customer Journey Analytics and R: How to Escape SQL Hell With cjar
In my last post, I illustrated how to use R with the Adobe Experience Platform (AEP) Query Service to query your raw data with analysis-supercharging tools like dbplyr without having to know any SQL. While I’m a big fanboy of R and dbplyr, if I’m honest, there are some things that SQL and dbplyr just […]
A Guide to Using R with the Adobe Experience Platform Query Service
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 […]