Let’s be honest, most companies don’t really think about nomenclature when it comes to setting up their tags, goals or A/B testing experiments. And that creates a horrible mess that will steal your teams valuable time and makes sure no-one really knows what’s going on. At Reflective Data, when we start working with a new […]
I guess it’s more or less true with every industry, especially with those related to the internet. There are two kinds of service providers, those who promise the world but hardly deliver anything useful, and those who actually put their hearts in and do whatever it takes to deliver something the client really needs.
While today I’m focusing on digital analytics implementations, perhaps most of you can share the experiences from general web development. I’ve been a part of over 50 web projects, from zero to something and here’s something I’ve seen way too often.
Running a basic A/B test is easy, but you know what else is easy? – Misinterpreting the results.
In this post, we are covering the common statistics terminology and models, and taking a closer look at the different methods of calculating A/B testing related metrics.
Depending on whether you are using the free or 360 version of Google Analytics you get 20 or 200 custom dimensions and metrics to work with.
When used correctly, these custom definitions can be one of the most useful custom features in Google Analytics. They allow you to tailor your analytics to meet your needs and to match your KPI-s.
One of the most common problems related to custom definitions has been that people don’t know what exactly are the dimensions and metrics they should be tracking.
Google Analytics, undoubtedly an industry leader in digital analytics, comes with a decent list of features available out of the box. Naturally, every website is different and so are their key objectives.
Tracking the performance of those key objectives is exactly where Google Analytics goals come into play. In this article, we are covering how to track the popular user actions as goals in Google Analytics.
Google Analytics’s visual interface is great for getting a quick overview and basic data exploration. Often times, in order to find useful insights, you need to take a deeper look and the visual interface just don’t cut it anymore.
In case you are like me, and many other data-driven marketers/analysts, you like working with spreadsheets. Luckily, pulling your Google Analytics data into Google Spreadsheets is easier than you might think.
If you’ve ever worked with Google Analytics API, you are probably familiar with the Query Explorer. What many users don’t know about is that Google also has a similar tool called Request Composer. The main difference between the two is that while Query Explorer is built on top of Reporting API v3 the Request Composer […]
Apache Superset is a modern, enterprise-ready business intelligence web application that makes it easy to visualise large datasets and build complex dashboards. At Reflective Data, we are using Apache Superset to monitor all data going through our platform with minimum latency. This allows us to easily combine data from different databases and every analyst can […]
Your website probably has Google Analytics for tracking general usage, maybe it even has a tool like Reflective Data for more user-focused data like heatmaps and form analytics. This is great but for some reason, most companies are not using on-site polls on their websites.