Announced first at Adobe Summit 2022, this new open source R package for the Adobe community harnesses the power of CJA and R together. This post introduces the functions and benefits of cjar for the analytics team using Customer Journey Analytics.
Adobe Summit 2022 covered a lot of ground, and Trevor Paulson’s presentation, “Turn it Up to 11! Customer Journey Analytics Tips and Tricks,” got everyone revved about Customer Journey Analytics (CJA). Using a perfectly well-timed transition into an 80s rock band theme, Paulson delivered a series of fast-paced demonstrations that revealed incredible opportunities the new features will put into the analyst’s tool belt. (See our Top Ten for Adobe Summit 2022, including a hilarious video review from Jim Gordon, Cory Watson, and special guests.)
Towards the end of the presentation, Paulson showed how to extend the analyst’s playlist to include powerful tools like the R programming language. He introduced cjar, an open source (read FREE) R package for the Adobe community to harness the power of CJA and R together into a life transforming drum solo that would take any analyst’s game to the next level!
What is cjar?
Cjar, the new R package on CRAN, is similar to the existing R package, adobeanalyticsr (https://adobeanalyticsr.com), for Adobe Analytics. This similarity will be an additional boost to productivity, since most analysts will be able to quickly transition a lot of their reports and analysis into the new CJA framework.
What does cjar do?
What is Customer Journey Analytics (CJA)?
CJA is one of the newest analytics tools by Adobe in its new suite of solutions based on the incredibly versatile and powerful Adobe Experience Platform. These tools dramatically enhance the analyst’s ability to explore, visualize, and communicate data from a much wider array of data sources. We’ll cover CJA more in depth in an upcoming blog post.
What are the benefits of using cjar?
- More than 50,000 rows
Currently the user interface is capable of downloading the first 50,000 rows of a specific dimension. While the API limits a query from pulling more than 50,000 rows at a time, it is really easy to pull the rest of these rows by changing the “page” argument in an API call.
- Multidimensional breakdowns
The free-form table is great for seeing dimensional breakdowns, but it is next to impossible to effectively analyze data by comparing more than three or four dimensions when they are broken down by another dimension. The R package has a function named ‘cja_freeform_table’ that seamlessly returns a table with multiple dimensional breakdowns for much better analysis and exploration.
- Metadata audits
Using the API, it is much easier to do audits on the elements in a dataview. You are able to pull a list of all available dimensions, metrics, filters, calculated metrics, usage logs, and projects just to name a few.
Whose lives will cjar improve?
As the developers of cjar, please make sure to let us know if we can help get your team started using it, and what creative solutions you are able to create with the different functions. We are always looking for more ways to extend the functionality of the package, so if you have some ideas, let us know! Better yet, submit a pull request on the github account!
