Possible use cases for a data warehouse are virtually limitless and depend on what kind of business you run. In this article, I’m providing some of the more common ways together with examples of how to benefit from having a data warehouse.
While the new version of Google Analytics, the GA4, comes with the native BigQuery export feature available in the free version and some of the other quotas aren’t as tight anymore, GA4 still has a fair share of limitations we need to account for.
The limit I’m covering in this post is set on the BigQuery export. More specifically, the GA4 to BigQuery native export feature has a limit of 1 million hits per day. Luckily, there are some ways around it.
No doubt that Google Analytics is the most popular tool when it comes to website analytics. Even though it’s free for most users, it does have some serious limitations. In this article, we’re going to figure out what’s the best alternative to Google Analytics.
At Reflective Data, we’ve worked with companies big and small. This means we have seen all levels of maturity when it comes to the infrastructure and knowledge around data pipelines and data warehouses.
Some of the most challenging projects have been enterprises with quite some infrastructure, legacy pipelines, and of course, opinions. Smaller businesses are just starting to adopt the concept of having all of their data stored in a data warehouse but many enterprises have been doing this for a decade!
Long-term metrics like customer lifetime value (LTV) and churn can be so much more insightful and lead to better results when optimized for when compared to the more basic metrics like transactions or revenue. Yet, these metrics are often ignored or at least not involved in the analysis and optimization processes enough. One of the reasons is that it’s quite difficult to track them using common analytics and testing tools like Google Analytics and Optimize.
In this article, we are going to explore some of the ways we can leverage Google Analytics to track churn, LTV and other really useful metrics.
Data protection and privacy rules are getting tougher all over the world. This is especially true for the European Union and even more so for some specific industries. Including finance, medical and others that handle sensitive information about their users.
While Google Analytics has been making some improvements in the privacy area and is GDPR compliant, this is not enough for many businesses and industries.
At Reflective Data, we’re often working with companies that are under close monitoring of their regulators. To help them out, we’ve built custom solutions that allow storing Google Analytics within the European Union or sometimes even completely locally.
Using Google Analytics Parallel Tracking and a custom data pipeline for Shopify, we managed to get all necessary data in BigQuery for more advanced analysis and reporting.
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.
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.
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.