Google Analytics 4 (GA4) is not yet fully GDPR compliant. Google is working to make GA4 compliant with the GDPR, but it is a complex process that is still ongoing.
At Reflective Data, we built a solution that enables companies to use GA4 in the EU safely.
Author: Silver Ringvee
Silver Ringvee is an expert in both traditional digital analytics and user behavior analysis.
Besides analytics, Silver also has a strong experience in A/B testing and web development.
Over the years, he has worked on over a hundred websites helping them become more profitable using various data-driven solutions.
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.
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.
Is your marketing data currently under the control of multiple vendors and platforms? Do you have to mix together siloed data and reporting tools to answer questions about marketing ROI and your customer’s journey? It’s time to take control of your data!
Cloud databases (i.e. BigQuery) and various data management tools have made it possible for marketers to build data pipelines without a big investment in hardware, software, and custom development.
In this article, we will walk through the steps of gathering data from Google Analytics, Google Ads, Google Search Console, Facebook, CRM and several other sources into Google BigQuery data warehouse and making it the single source of truth for all marketing data.
The number of marketing tools an average business uses has grown rapidly. Besides one or two analytics platforms there’re a few ads platforms, CRM, CMS, several social media platforms, an email system and probably a few more tools and platforms.
All of those tools are supposed to make our work as marketers, business owners or data analysts easier and more effective. In reality, though, you will end up with a bunch of silos – systems that don’t really communicate well with each other and almost never agree on any of the important metrics.
Data silos create confusion and disagreement between teams, leading to a situation where, at the end of the day, no-one knows which tool or numbers to trust.
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.
This blog post is aimed for anyone planning to build a data pipeline or upgrade their current setup.
An end-to-end analytics data pipeline is a secure and reliable mechanism that is responsible for feeding your business with valuable data that can be used for reporting, analysis, machine learning or any other activity that requires accurate data about your business.
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.
While it might look like a normal WordPress blog post (like all the previous posts on our blog), you are actually looking at a Jupyter Notebook.
In this first proof-of-concept blog post/Notebook, we are showing you how Notebooks work and a few cool things you can accomplish with them.