Author: Jason Dolan

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.

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.

Over the years, we have helped companies of all sizes and from various industries to collect, process and make use of digital data.

Something we should have started doing a long back is sharing the success stories of our clients. We’ve done so many cool things together and for our clients that these stories are definitely worth sharing, and reading.

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 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.