Case Study: Solving The Discrepancy Between A/B Testing Tool, Google Analytics and Backend Data For a Large E-Commerce Business

Working with skewed data can be worse than having no data at all. This is why we’re always promoting all sorts of analytics audits and making sure all data sources agree with each other. At the very least, you should know why the numbers in different tools don’t match (i.e. analytics doesn’t include offline sales but backend does).

Our client in this case study contacted us with a quite specific problem. They were running a decent CRO program with 4-6 A/B experiments running every month. The problem they had with the program, though, was that the numbers they saw in their testing tool Optimizely, Google Analytics and backend didn’t match. In fact, there was a ~35% discrepancy overall.

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[Medium] Hit-Level Unsampled Google Analytics to BigQuery Without 360

Google Analytics and BigQuery, two tools that both the major players in their respective segments. Yet, there is no way to easily send raw hit-level data from one to another.

In this article, originally posted on medium.com, we’re going to walk through the reasons you might want to access raw Google Analytics data in BigQuery and a few solutions that will get you there.

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How to Avoid Google Analytics Sampling and Data Limits?

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.

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How to Query and Analyze Google Analytics Data with BigQuery

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.

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Unsampled Hit-Level Google Analytics Data Without 360

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.

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Working with Google Analytics Data Using Python and Jupyter Notebooks

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.

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Data You Should Be Tracking Using Google Analytics Custom Events

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.

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