Pardot iFrame Form Tracking with Google Analytics


This guide shows you how to track Pardot form submissions completed in an iFrame in Google Analytics. The method described allows you to properly attribute the acquisition traffic source and medium to the conversion- the Pardot lead form submission in Google Analytics. You can also see what page on your site the Pardot iFrame lead form was submitted on using this tutorial.

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Google Analytics R Tutorial


This is a tutorial on how to use R to directly connect to and extract data from Google Analytics using the Google Analytics Reporting API v4. This is meant to be a simple example and assumes no prior knowledge or experience with R, APIs or programming.

I’ve included a video that walks you through each step of the process. You will be up and running in a few minutes. By the end of this tutorial you will be able to:

•Extract page view data for your top pages from Google Analytics Reporting API to R.
•Create a line graph showing session trended by day using R.

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Digital Analytics Data Quality- Your First Lesson


Make sure you are very confident in the accuracy of your data before you present it to stakeholders.

Eager Young Digital Analyst Meet the Data Quality Mousetrap

Almost 9 years ago when I was first starting out in my career in digital analytics I was tasked with an analysis of an internal search tool. It was one of those sites that had a bunch of filtering options for search. The visitor could select sizes, colors, product categories, and many other product attributes. I dove head first into the web analytics tool and started to explore the data. I quickly found what I thought was a smoking gun. According to the data, the insight I discovered was that the only internal search filtering option that visitors used was the color filter.

I presented two recommendations in my PowerPoint analysis:
1) Simplify the filtering and just keep the color filter
2) Highlight the other filtering options like size, product category etc. to get visitors to use other filters

Read the story of how I found my career as a digital analyst on Craigslist.

Digital Analyst Meet the Debugger

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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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