Adobe Analytics Implementation Documentation in 60 Seconds

When I was working as a digital analytics consultant, no question quite had the ability to cause belly laughs AND angst as, “Can you send me an updated copy of your implementation documentation?” I saw companies that were spending six-or-seven-figures annually on their analytics infrastructure, multi-millions in salary for employees and yet the only way to understand what data they were collecting was to inspect their JavaScript code.

Luckily for Adobe Analytics customers, the API provides a means of generating the framework for a properly-documented implementation. Here’s how to do it using RSiteCatalyst.

Generating Adobe Analytics documentation file

The code below outlines the commands needed to generate an Excel file (see example) with six tabs containing the basic structure of an Adobe Analytics. This report contains all of the report suites you have access to, the elements that reports can be broken down by, traffic variables (props), conversion variables (eVars) and segments available for reporting.

Additionally, within each tab metadata is provided that contains the various settings for variables, so you’ll be able to document the expiration settings for eVars, participation, list variables, segment types and so on. 

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
library("RSiteCatalyst")
library("WriteXLS")

#Validate that underlying Perl modules for WriteXLS are installed correctly
#Will return "Perl found. All required Perl modules were found" if installed correctly
testPerl()

#### 1. Pull data for all report suites to create one comprehensive report ####

#Authenticate with Adobe Analytics API
SCAuth("user:company", "sharedsecret")

#Get Report Suites
report_suites <- GetReportSuites()

#Get Available Elements
elements <- GetElements(report_suites$rsid)

#Get eVars
evars <- GetEvars(report_suites$rsid)

#Get Segments
segments <- GetSegments(report_suites$rsid)

#Get Success Events
events <- GetSuccessEvents(report_suites$rsid)

#Get Traffic Vars
props <- GetProps(report_suites$rsid)

#### 2. Generate a single Excel file

#Create list of report suite objects, written as strings
objlist <- c("report_suites", "elements", "evars", "segments", "events", "props")

#Write out Excel file with auto-width columns, a bolded header row and filters turned on
WriteXLS(objlist, "/Users/randyzwitch/Desktop/adobe_analytics_implementation_doc.xlsx",
         AdjWidth = TRUE, BoldHeaderRow = TRUE, AutoFilter = TRUE)

The only “gotchas” to keep in mind when using the script above is that the user running this will only receive data for report suites they have access to (which is determined by Admin panel setting within Adobe Analytics) and that you need to have the WriteXLS package installed to write to Excel. The WriteXLS package uses Perl as the underlying code, so you’ll need to validate that the package is installed correctly, which is done using the testPerl() function in the package.

This is pretty bare-bones, no?

After you run this code, you’ll have an Excel file that has all of the underlying characteristics of your Adobe Analytics implementation. It’s important to realize that this is only the starting point; a great set of documentation will contain other pieces of information such as where/when the value is set (on entry, every page, when certain events occur, etc.), a layman’s explanation about what the data element means and other business information so your stakeholders can be confident they are using the data correctly. Additionally, you might consider creating a single Excel file for every report suite in your implementation. It’s trivial to modify the code above to subset each data frame used above for a single value of rsid, then write to separate Excel files. Regardless of how your structure your documentation, DOCUMENT YOUR IMPLEMENTATION! The employees that come after you (and your future self!) will thank you.

EDIT 2/4/2016: Thanks to reader @CSitty for pointing out the R code became a little stale. The documentation generating code should now work again for RSiteCatalyst versions >= 1.4 and WriteXLS >= 4.0 (basically, any current version as of the time of this update).

  • RSiteCatalyst Version 1.4.16 Release Notes
  • Using RSiteCatalyst With Microsoft PowerBI Desktop
  • RSiteCatalyst Version 1.4.14 Release Notes
  • RSiteCatalyst Version 1.4.13 Release Notes
  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • ODSC webinar: End-to-End Data Science Without Leaving the GPU
  • PyData NYC 2018: End-to-End Data Science Without Leaving the GPU
  • Data Science Without Leaving the GPU
  • Getting Started With OmniSci, Part 2: Electricity Dataset
  • Getting Started With OmniSci, Part 1: Docker Install and Loading Data
  • Parallelizing Distance Calculations Using A GPU With CUDAnative.jl
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS