Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.
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Statsig
Score 8.9 out of 10
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Statsig is a feature management with feature flags, pulse, holdouts, from the company of the same name in Bellevue.
We selected Optimizely as it was easy to use/understand, had clearly defined SLAs for keeping the platform up and was regarded as resilient within the industry. We needed something at our point in our experimentation journey that could be used for Product testing at scale and …
Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -
This is clearly a platform built around experimentation first, and it shows. In this way Statsig is way ahead of the competition of products I've used previously! It's more data science focussed which makes configuration of new experiments complex with a learning curve.
It is easy to use any of our product owners, marketers, developers can set up experiments and roll them out with some developer support. So the key thing there is this front end UI easy to use and maybe this will come later, but the new features such as Opal and the analytics or database centric engine is something we're interested in as well.
Would be nice to able to switch variants between say an MVT to a 50:50 if one of the variants is not performing very well quickly and effectively so can still use the standardised report
Interface can feel very bare bones/not very many graphs or visuals, which other providers have to make it a bit more engaging
Doesn't show easily what each variant that is live looks like, so can be hard to remember what is actually being shown in each test
Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code
For the most part it is pretty easy to use. - There are some quirks with the javascript SDK (getExperiment().getValue?). - The Events vs. Metrics design pattern is complex, and creating new Metrics from Events can be frustrating if you are trying to use event metadata - It's really frustrating not to be able to link Static IDs (before a user signs up) to User IDs, in order to follow users all the way through onboarding, or to log events that occur for signed in users when you are exposing the experiment to users before they've signed up
When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team. Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities. If you are searching for an experimentation tool and personalization all in one... then maybe these comparison change and Optimizely turns to expensive. In the same way... if you want a server side solution. For us, it will be a challenge in the following years
We have a huge, noteworthy ROI case study of how we did a SaaS onboarding revamp early this year. Our A/B test on a guided setup flow improved activation rates by 20 percent, which translated to over $1.2m in retained ARR.
