Category: Data Pipeline

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.

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.

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.

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.