Google Optimize and Optimize 360 will no longer be available after September 30, 2023. Your experiments and personalizations can continue to run until that date. Any experiments and personalizations still active on that date will end.
In order to not lose your data, you should act on exporting it now!
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.
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.
If you have any experience with JavaScript-based A/B testing and/or personalization tools you know that flicker can be a real headache.
In case your tool of choice is Google Optimize, you should be using their official anti-flicker snippet to minimize the flicker effect.
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 client, we always start by figuring out what their current system consists of — and in many cases, it’s a real headache. Not to mention, when we ask the client about a specific tag or goal that they set up 6 months ago, they don’t remember anything — and the name they chose isn’t helping much either.
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.
FOOC or Flash of Original Content is a situation in Javascript based A/B testing where the experiment is causing an element on the page to flicker when changing from original to the variation.
It has been a known problem for as long as front-end A/B testing solutions have been around. In this article, we take a look at why you should care and if it’s somehow possible to solve it.
If you run A/B tests using Optimizely, you have probably worked with its preview links. There’s a good chance that you never really thought about the individual parameters in these links, is this the case with you?
In this article I am going to share some of the best tips for Optimizely preview links that are going to make your work easier and save you a good amount of time.
At Reflective Data, we believe that A/B testing is one of the best uses for all the data you gather in your digital analytics system. Of course, there are unlimited other uses like spotting when something is broken or calculating the ROI for your latest campaign but these are mostly just acknowledging what has happened. A/B testing is (should be) taking an action based on that data.
In this article, we are going to give you a comprehensive overview of how to get started with A/B testing, including the prerequisites, tools, and methodologies. We are even going to give you some test ideas to get you started! In case you are already running A/B tests, I’d still suggest you take a look how others (we) approach the problems involved.
Around eight months ago, we signed up for Google Optimize invite list. We finally received our access around four months later and we have now been actively using it for three months.
So, what’s our verdict? Is it going to replace other tools like Optimizely or VWO? In this article, we are going to tell you exactly what we think about Google Optimize.
