Real-Time Reporting Adobe Analytics API Tutorial


Adobe has a nice real-time dashboard built directly into Reports & Analytics (SiteCatalyst). It is easy to configure and get up and running quickly. According to Adobe product manager Ben GainesReal-Time reports in Adobe Analytics has become one of the most popular features in Reports & Analytics.” What you may not know is that real-time data can be accessed via Adobe’s reporting API. Adobe has a great tutorial on how to get started with the Adobe Analytics Real-Time API that you need to check out. The example was used in a lab session at past Adobe Summits and it looks like it was offered again at the 2016 Adobe Summit. If you went to Summit let me know how the session was. I haven’t attended the session before, but I was in search of a real-time API sample for Adobe Analytics and I stumbled upon this gem.

In this post I am going to walk you through Lesson 1 and Lesson 6 from this tutorial. Lesson 1 gives you the basic fundamentals needed to run a Adobe Analytics real-time report. Lesson 6 gives you a working real-time dashboard with snazzy D3.js visualizations that you can load right now in your browser. Hopefully you can use this as the foundation for building some actionable solutions that leverage real-time data at your organization. I look forward to hearing about the real-time dashboard you build or the real-time content and campaign optimization or the real-time site health alert tools. Please share what you create in the comments or @ryanpraski.

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Python Google Analytics API Over 1 mil Rows Unsampled Data + Pull Data From Multiple Profiles

How to pull large data sets with over 1 million rows from Google Analytics and avoid sampling?
How to pull the same data across multiple Google Analytics profiles?

This post provides a solution for exporting more than 10,000 rows (the example pulls over 1 millions rows) and a solution for the sampling limitations of Google Analytics. The solution uses the Google Analytics reporting API and Python. It checks for the presence of sampling before running a query and gives you the ability to break your query down into smaller 10,000 row pieces. The pieces are multiple smaller queries with shorter date ranges. All the data from the small queries are stitched together and output into a single CSV file for the full date range. With this solution you can also pull data from multiple profiles using this single Python application. If you work across multiple Google Analytics profiles with high traffic volumes and often run into sampling, then this solution should save you lots of time. If you’d like to see how to use the Google Analytics Sheets Add-on to pull more than 10k rows of data and avoid sampling check out my previous post.

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Pull More than 10k rows Unsampled using Google Analytics Sheets Add-on

How to pull a Google Analytics report with more than 10,000 rows?
How to get around Google Analytics Sampling Limitations?

Sample Google Analytics Sheet used in this tutorial (file make a copy to edit)


Google Analytics Sheet Add-on 119,421 rows of data

How do you pull a Google Analytics report with more than 10,000 rows? How do you get around Google Analytics Sampling Limitations? These were the most common questions I was asked after my recent Google Analytics reporting API Python tutorial. This new tutorial will show you how to export more than 10,000 rows using the Google Analytics Spreadsheet Add-on and how avoid the sampling limitations of Google Analytics. I also have another post in the works on how to use Python and the Google Analytics API to avoid sampling and pull even more data. Keep an eye out for the new post!

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Google Analytics Reporting API Python Tutorial

• Download Python 2
Register your application for the Analytics API in Google Developers Console 
Download the Google Python API Sample Code
Google Analytics Query Explorer
Python Code to Output Google Analytics API Query Data to CSV *save file as .py

Check out this new post on how to pull over 1 million rows of unsampled data using Python & how to pull data from multiple profiles

Check out this new post on how to pull more than 10000 rows of unsampled data using the Google Analytics Sheets Add-on.

R users check out this tutorial on how to pull your first Google Analytics R v4 reporting API query.

This guide will go through step by step instructions on how to setup Python and pull your first query directly from the Google Analytics reporting API. I will show you how to install Python on Windows and add the Google API Python library. We will create a new project in the Google Developers Console and enable the Analytics API. Next we will use a prebuilt sample Python application to get data out of Google Analytics via the API. Then I’ll walk you through how to test your own query using the Google Analytics Query Explorer. Then we will edit the Python application code to create your very own query. And finally we will pull Google Analytics data directly into Excel using Python to write a CSV file containing the Google Analytics data.

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How to Add 100 Users to Adobe Analytics in Seconds


Website to add multiple users
Sample CSV file to upload to the website

The Problem: How to add 100 users to Adobe Analytics as efficiently as possible?

A client recently requested that I add 100 new users to an Adobe Analytics account. I didn’t want to go in and add each user one by one via user management in the administrative console. It could take an hour or more to fill in the required fields for each of the 100 users. Administrative upkeep and user management is tedious work that is prone to errors. There had to be a better way.

The Solution: Use a web application that leverages the Adobe Analytics API

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How I found my Career in Digital Analytics on Craigslist 7 Years Ago


Semphonic Staff at XChange 2010 Monterey California

Digital Analytics is an amazing field. I am so lucky to have found something so interesting so early in my career. Like most web analytics professionals I didn’t plan on being a web analyst. In fact before I started searching for a new job in spring of 2008 I had never heard of web analytics. In my 7 year web analytics career I’ve been fortunate to work for Semphonic and for the last 2 years plus Ernst & Young following the acquisition of Semphonic in March 2013. Thankfully Gary Angel and Joel Hadary hired me in July 2008. I’ve been lucky to have Paul Legutko as my mentor, manager and my professor of digital analytics for the entire 7 years. When people ask me about Paul I describe him as the professor that you wished you had in college: smart, patient, funny, willing to take the time to answer your questions and truly an amazing teacher. (Before web analytics Paul spent time teaching and doing post-doctoral research at the University of Michigan and Stanford University). I can’t believe it has been 7 years. Before too much more time passed I wanted to make sure to recount how I got into digital analytics. I responded to Semphonic’s Craiglist job post and the rest is history. Here is the story.

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Format Percent Change Red & Green- Excel & Google Sheets for Digital Analytics: Tips & Tricks

If an increase is good and a decrease is bad format percent change like this
[Green]▲ 0.00%;[Red]▼ -0.00%

If an increase is bad and a decrease is good format percent change like this
[Red]▲ 0.00%;[Green]▼ -0.00%

Download Excel Sample

Google Sheet Sample

You are putting together a report or dashboard and you want to make it simple to understand changes in metrics. By adding color and arrows to a percent change you can help better visualize the meaning of the data. This post shows you how to add green and red color and up and down arrows to highlight changes in metrics in Excel and Google Sheets.

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From Web Analytics to Scheduling Meetings?

Digital Analytics Association NYC Symposium 2015

Last week I attended my first Digital Analytics Association (DAA) Symposium in New York City. The theme of the Symposium was “Beyond Measure” how we’re using our own and others’ data in ever-more imaginative ways to create new products, experiences and possibilities for consumers and businesses. This theme really resonated with me. In our day to day work as digital analysts we are often very heads down. We focus on implementing, reporting and analyzing digital data, but less time thinking about bigger picture uses of data. The keynote by Dennis Mortensen of a personal assistant that let’s you schedule meetings via email using artificial intelligence was inspiring. This post will tell Dennis’ story about going from founding two digital analytics companies to founding a company that schedules meetings.

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Format Time on Page- Excel & Google Sheets for Digital Analytics: Tips & Tricks

• Download Excel Sample
• Google Sheet Sample

4.33 -> 04:20 Adobe Analytics
179.04 -> 02:59 Google Analytics

When you pull metrics like average time on page from digital analytics tools the number format looks like this: 4.33

I remember scratching my head the first time I saw this in Adobe Analytics (then Omniture). Was the other dot in the colon missing and did this mean 4:33 or 4 minutes 33 seconds? Or did this actually mean 4 minutes 0.33 * 60 seconds (0.33 multiplied by 60) which equals 4 minutes 19.8 seconds?


Average Time on Page in Adobe Analytics Report & Analytics

Drum roll please… it means 4 minutes 0.33 * 60 seconds or 04:20. For those of you who were able to convert 4.33 from an a number with a decimal to time in your head please pat yourself on the back and let me know in the comments. For the rest of us, this post will show how to convert a number with decimal time to minutes and seconds time formatting using Excel and Google Sheets.

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