Dynamic Yield is presented as an AI-powered Experience Optimization platform that delivers individualized experiences at every customer touchpoint: web, apps, email, kiosks, IoT, and call centers. The platform’s data management capabilities provide for a unified view of the customer, to allow the rapid and scalable creation of highly targeted digital interactions. Marketers, product managers, and engineers use Dynamic Yield for: Launching new personalization…
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Optimizely Feature Experimentation
Score 8.3 out of 10
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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.
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.
For us, it is well suited for personalization. Since we are hospitality brand, we have different rooms sales inclusion based on different segmentation like Mem or Non-mem, Global or UAE, we have to personalize our landing pages accordingly so that we show the relevant information to relevant audience. The inactivity pop up box and newsletter signup popups work good for us. It does not work well in some scenario like Dynamic Yield offers built-in analytics focused on campaign and test performance, but it’s not a replacement for tools like GA4, Adobe Analytics. It lacks deep funnel tracking or complex reporting capabilities.
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 -
Provide fantastic support, both in relation to strategy/best practice and troubleshooting.
An easy to use interface, as a user who is relatively new to Dynamic Yield I find that it is an intuitive platform to use.
The ability to segment and drill down on data allows for really specific insights which, whilst not necessarily being leveraged on a testing basis, can be super valuable from a greater marketing perspective.
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.
Brand templates could need complex CSS/custom code.
We'd like to see a little "i" next to specific labels, which elaborates on what is meant. For example, when I hover over "Dynamic allocation," I get something like "An advanced form of A/B testing where the best-performing variations receive higher traffic."
Jargon (for example, for audience targeting) can be overwhelming for new users; therefore, clearer, user-friendly explanations are needed.
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
implementation took a long time but also, DY has really proven that they are transforming and adapting their platform to be more user friendly and the right technology choice for their brand or company
Setting up strategies, audiences, and experiences is simple and fast. It is incredibly easy to modify the appearance of your site and optimize every aspect with the Dynamic Yield Personalizations. However, while the data visualization on an experience level is easy to modify and analyze, exporting the data in meaningful ways is time consuming.
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
Overall, the support is very good. If you are a partner (my case), they assign you a customer success manager, that helps a lot. Also, there is a technical person to provide support to the partners, again a great help.
My only "complain" is that with some complex issues, the support may delay in providing you with a solution. Sometimes that can cause some tension with your client.
Dynamic Yield provides far more capability and ready-to-go templates for small-medium sized businesses, as well as decent API implementation for businesses who want to have a deeper integration. The ease of implementation and faster time-to-market is why we chose Dynamic Yield.
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
Most tests have had a positive impact on either revenue or conversion rate - quite often in double digits.
Dynamic Yield has also helped us to stop some particular initiatives through direct interaction with the customer base via questionnaires or by a test proving negative quicker than rolling out a permanent feature.
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.
