Optimizely Feature Experimentation vs. PostHog

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 -

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For small, expanding, and rapidly growing startup teams where it's crucial to understand who your audience is and how they work with your product, PostHog is ideal. You have to have very technical thinkers across the engineering and product teams. It worked well at the company I'm at because our product managers are technical. It may not suit larger organisations or enterprises where you're looking for a nice, clean UI

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• 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.

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• 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

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Competitive landscape

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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

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It's easy to get started with it. But given how many features it offers, the UI often gets confusing. The UI interface is also quite slow - on both Safari and Chrome on my MacBook Air M2.

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Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams

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It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it

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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

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PostHog is better suited for engineering teams

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had troubles with performance for SSR and the React SDK

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• We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.

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• not really any ROI just the ability to safely release features. It provides intangible value.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info