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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.

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.

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.