Archive for the ‘Google Analytics’ Category
Google Analytics export to BigQuery is great for getting at the raw session-level data of Google Analytics. But, it’s only for GA Premium (GAP) subscribers. If you have other reasons to need GAP – like increased sampling limits, DoubleClick integration, or additional custom dimensions — and you have the money to spend, GAP is a great option.
Raw GA data?
But what if you’re not a GAP subscriber? Can you still get the raw, session-level data?
In a word: no (at least not from GA). All of the data in GA reports and in its associated reporting APIs is aggregated data. You can create and export reports full of dimensions and metrics, but there’s no report that can give you all of the information for each session the way BigQuery can. (more…)
As we look towards the end of this year and the beginning of 2015, consider how a training in Google Analytics, Google AdWords, or Google Tag Manager may help your career! Choose from seven different cities in the first quarter, ranging from Boston to San Francisco, with stops in Chicago and Denver along the way.
With trainings in cities around the country, we hope you can find a location that is easy to travel to and fun to explore!
Whether you’re just starting out in a new field or looking to get a deeper understandable of the tools you’re currently using, we have a class for you.
Learn how to better collect and analyze your data with our Google Analytics series, futureproof your website with the flexible Google Tag Manager, or drive qualified traffic to your site through paid search with our Google AdWords trainings.
Choose an option below to learn more about the specific topics we cover and decide which trainings would be right for you!
Google Analytics Google AdWords Google Tag Manager
As analysts and marketers, we always want to track positive performance metrics and conversions in Google Analytics. However, tracking errors is also important to monitor the health of your site and keep track of signals indicating a negative user experience.
Accessing this data gives us a better idea of what’s causing users to get lost and wander into the dark, unattached voids of your domain. Knowing where these problem spots are makes it easier to fix internal links or set redirects.
I’ll show you different ways to view where people are hitting these error pages and where they are coming from, either through your existing setup or by using Google Tag Manager to fire events or virtual pageviews. (more…)
On July 30th, 2014, Google Analytics announced a new feature to automatically exclude bots and spiders from your data. In the view level of the admin area, you now have the option to check a box labeled “Exclude traffic from known bots and spiders”.
Most of the posts I’ve read on the topic are simply mirroring the announcement, and not really talking about why you want to check the box. Maybe a more interesting question would be why would you NOT want to? Still, for most people you’re going to want to ultimately check this box. I’ll tell you why, but also how to test it beforehand. (more…)
It’s now easier than ever to track and compare performance between articles and blogs. While Google Analytics shows you pageviews and other key metrics, frequent content comparisons are made difficult by the shifting time frames.
How can I compare a blog post that was published this month vs. a blog post that was posted last month? Sure, we can run two different reports, pull it into Excel and start crunching the numbers, but there’s gotta be a better way!
Enter Cohort Analysis. You may have heard this term thrown around before, usually in relation to users on your site and when they first became users. The idea here is to group users or sessions into common groups, like who first visited in January or first-month visitors. Avinash and Justin Cutroni both love cohorts, so obviously we should, too!
In this case, we’re going to use Google Tag Manager to put content into cohorts so we can analyze how they performed in similar time frames. We’ll pass these into Google Analytics as Custom Dimensions so they’re available for analysis. It’s actually much easier than it sounds! (more…)
Segments are one of the most powerful features of Google Analytics, and they are often useful for zeroing in on the sets of users who are most valuable to us.
One way of looking at potentially valuable users is to look at the frequency with which they visit the website. Let’s look at a couple of ways to do that in GA.
In this blog post, I evaluate several of the numerous (and potentially overwhelming) options for the processing and reporting of Google Analytics data. The default Google Analytics web interface is great for quick ad hoc data exploration, but limited for deeper analysis and the development of automated reports.
Whether we’re mining for hidden trends or trying to report on hard-to-extract dimensions, there are a number of third-party tools out there can that help ease the burden.
In the first half of this article, I explain the difference between the two types of Google Analytics data: what’s available from the standard interface and what’s available through the BigQuery export.
The second half of this article is an evaluation of three different solutions for processing, visualizing, and reporting on Google Analytics/GA BigQuery data. I evaluate these three solutions (ShufflePoint, Tableau, and R) based on objective features and my subjective scoring of performance.
I only evaluate three data processing solutions in this article. Think I missed a good one? Let me know!! We all have a different background in data analysis tools, and I would love this conversation to continue in the comments section.
Have you ever tried to use the “plot rows” feature in Google Analytics and it literally falls flat?
It happens because you can’t keep the chart from graphing the metric total. That thick blue line across the top of your chart flattens everything else. It keeps the size of the chart static, rendering it useless.
Wouldn’t it be great if you could graph only the rows you want and the chart would dynamically resize?
Here’s the key to turning those flat, plotted rows into dynamic data visualizations: motion charts. (more…)
Did you ever want micro-level geographic information inside Google Analytics? What if you really need “street level” knowledge about your users; like where are they, what neighborhood are they in? Often, when we talk and write about Google Analytics we’re thinking about the big guys. National or even International traffic, filtering by country, comparing one region to another. We’re thinking macro, not micro.
I wrote previously comparing DMA areas to gain insight, but that’s really only helpful if you have a true national or bigger presence. What if you’re just a local Seattle business, and don’t really have much call for looking at traffic outside the Seattle-Tacoma metro area?
Well, first thing you should do is think about taking our Seattle Google Analytics, AdWords, and Tag Manager Training (shameless plug). Second, read on…
Seattle is actually ahead of the game when it comes to data, which is the real reason I’m using them as an example. The city has a Chief Technology Officer, and data.seattle.gov was started in 2010 as a central hub for all local Seattle data. In fact, a number of businesses claimed that the use of this local data helped them with their businesses.
How so? Well, if you’re a local business then the traffic from, and information about, the Queen Anne neighborhood of Seattle might be more important to you than Downtown or Riverview.
But how can you use Google Analytics to help you on this sort of granular level? Also what if you DO care about national level data, but you care about it on a very granular local level as well, maybe looking for interest in your brand to help place billboards, or expand your franchising? The truth is that you can’t, at least not right out of the box. But with a few very easy additions, you can start getting some great local data that can let you make street level decisions about your business in Google Analytics. (more…)
By far the most common issue I’ve come across with ecommerce sites; duplicate transactions can inflate revenue and ecommerce metrics, altering your attribution reports and making you question your data integrity.
When talking about where to put the ecommerce tracking code, Google suggests the following for Universal Analytics:
… If successful, the server redirects the user to a “Thank You” or receipt page with transaction details and a receipt of the purchase. You can use the analytics.js library to send the ecommerce data from the “Thank You” page to Google Analytics.”
The missing step here is to ensure that either A) the user cannot access the page more than once or B) you have logic in place to make sure the transaction is only sent once. The biggest issues I’ve seen are when this receipt page is automatically emailed to the customer, with the ability for them to return as frequently as they please, each time sending a duplicate transaction.