GA360 is a great tool, and for the enterprises that can afford and justify the cost, probably the best analytics tool they can invest in. At the same time, many companies don’t have the budget to pay upwards of $150k for an analytics tool.
Category: Case Study
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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!
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.