SQL is the most popular language for professionals to communicate with databases and query data. Google Analytics is the most popular tool for digital analytics. How come there’s no way to query Google Analytics data using SQL? In this article, we’ll explore the solutions.
Google Analytics, while being by far the most popular tool in its segment, does have a few limitations that can make this, otherwise nearly perfect tool, unsuitable for a large number of companies.
The main limitations of Google Analytics are related to sampling and data collection limits. Most affected are companies that can’t afford the premium 360 version of Google Analytics (~150k/year) but still have a good amount of traffic visiting their websites. In general, Google Analytics properties with >1M sessions/month or >10M hits/month are being affected by some heavy sampling and data collection limits.
In this article, we’re going to cover the different types of limitations present in the free version of Google Analytics and provide solutions/workarounds to all of them. Oh, and the solution, in most cases, does not include buying the 360 version.
BigQuery is an extremely powerful tool for analyzing massive sets of data. It’s serverless, highly scalable and integrates seamlessly with most popular BI and data visualization tools like Data Studio, Tableau and Looker.
Working with Google Analytics data in BigQuery has mostly been a privilege of those having a 360 version of Google Analytics. Its hefty price tag, though, has made that list quite short.
Google Analytics is a really good tool for marketing-focused digital analytics. And by far the most popular one in this segment. With some custom setup, you can also use Google Analytics for tracking SaaS and other web apps & products.
Two of the most common shortcomings of Google Analytics that most of the more advanced users experience, though, are the lack of hit-level granularity and sampling. In this article, we are taking a look at some of the ways you can overcome these shortcomings without spending a fortune on Google Analytics 360.
Enhanced Ecommerce is one of the most powerful and flexible features of Google Analytics. Its flexibility, though, leaves a lot of room for errors in the setup.
In this article, we are covering everything you need to know about the problem of duplicate transactions, a root cause of skewed data in many Google Analytics instances.
Python is a programming language with virtually limitless functionalities and one of the best languages for working with data. Jupyter Notebooks, on the other hand, is the most popular tool for running and sharing both your Python code and data analysis.
Putting Python and Notebooks together with Google Analytics, the most popular and a really powerful tool for tracking websites, gives you almost like a superpower for doing your analysis.
Out of the box, Google Analytics already tracks a bunch of really useful data points. What the default setup lacks, though, is context and events that are specific to your website and business.
Custom Events provide a perfect solution for adding context and tracking more specific user actions. In this article, we are giving you a good amount of ideas for custom events you should implement on your own and/or your clients’ websites.
Visitors rage clicking on certain elements on your website is a good indicator of a UX error. For example, people may click on a blue text that is not a link or on an image that has no click functionality.
I thought it would be nice to combine a list of our blog posts that Google Analytics has shared on social media.
First of all, thank you Google Analytics for sharing our content with your audience!
In case your tool of choice is Google Optimize, you should be using their official anti-flicker snippet to minimize the flicker effect.