Optimizely Web Experimentation Reviews and Ratings

Use Cases and Deployment Scope

We use this product to run AB tests for multiple BU's

Likelihood to Recommend

To run tests on inside the funnel. Less useful on landing pages at the top as there can be a slight flashing.

Use Cases and Deployment Scope

We are a large organisation with multiple domains in multiple markets. Optimizely is used for A/B testing and personalisation across all sites. We can test variations of landing pages, CTAs, and forms to increase lead generation and sales.

Likelihood to Recommend

It is easy to launch experiments and personalisation at speed. The software has allowed us to increase testing velocity since we adopted it. However there is a lot of customisation required and we still rely on google analytics for accurate reporting with Optimizely metrics not considered as reliable.

Use Cases and Deployment Scope

Optimizely Web experimentation is the primary tool I use to validate front-end and workflow changes. But beyond the day-to-day, I've leaned on it for more ambitious projects like an ongoing experiment with AI-driven diagnostic recommendations. I'm coordinating with our analytics team to pipe experiment data into Power BI. We've also partnered with Kin + Carta to implement a workaround that links Optimizely events with some of our older reporting systems.

Likelihood to Recommend

It worked so well when we wanted to try different client onboarding flows in our referral portal. We ran one test that compared a guided wizard versus a simplified one-page form. Optimizely gave us quick clarity on which version reduced abandonment rates. This is gold in b2b. On the less appropriate side, when we attempted a multi-system experiment tied to our EHR integration, the complexity made it difficult to track accurate outcomes without pulling in external partners.

Use Cases and Deployment Scope

We primarily use it to provide different variance to the user to understand which content is more attracting users, how users are actually using it. We are creating multiple variants actually on a page that's on the website. And we are experimenting which one will be working and we are taking from the results and we are creating the content based on the results actually.

Likelihood to Recommend

Well suited, we have even took this particular option to go with experiment when we wanted to build a new page. Going with creating the blocks on the code takes you weeks, a month actually to build the entire page. So we would build the front end, we'll just put through the experiment and we kind of make it available on the website and to see how it is performing. And then we slowly take into the process of creating the content as in the code actually. So that is what one of the approach. So to do that experiment is the best thing that optimistic has given before with Open, it's going to very easy from now on. Sometimes it is very hard for the marketing team. If we put in multiple content on the same page, it is probably clumsy for them to understand how the content is actually and how you need to clear ensure that the tracking is linked or how you set up the HTML code. If you're putting in a bunch of a bulk of HTML code into an experiment on the variant, probably it might be not readable actually. So that is one of the, I would say you cannot build a big HTML and put it on the experiment just if you have a page and if you wanted to play around with it or put in a small plugin on experiment, that will be very suitable.

Use Cases and Deployment Scope

We do a lot of data analysis on our e-commerce journeys and one of our biggest, I guess, goals is conversion rate. So we use Optimizely Web to understand the insight that we're getting through either focus groups or other experiments, whether we should roll out a particular change or whether we should add additional information. And whether that basically enhances the journey enough to give us a better conversion uplift.

Likelihood to Recommend

It's value for money in terms of comparing, because I do this all the time, we compare how long it takes to build and optimize the agent experiment compared to optimize the web and our win rate for optimize the web experiment is actually a lot higher than optimize the agent, and that's purely because it's so simple to use and we can get results really fast, which leads to, like I said, other ideas of things we can test and the roadmap we can just get through a lot quicker being able to layer different experiments. So it's definitely something I would recommend.

Use Cases and Deployment Scope

We use web experimentation to A/B test new functionality on our website. The goal is to improve the conversion rate.

Likelihood to Recommend

I think it can serve the whole spectrum of experiences from people who are just getting used to web experimentation. It's really easy to pick up and use. If you're more experienced then it works well because it just gets out of the way and lets you really focus on the experimentation side of things. So yeah, strongly recommend. I think it is well suited both to small businesses and large enterprises as well. I think it's got a really low barrier to entry. It's very easy to integrate on your website and get results quickly. Likewise, if you are a big business, it's incrementally adoptable, so you can start out with one component of optimizing and you can build there and start to build in things like data CMS to augment experimentation as well. So it's got a really strong a pathway to grow your MarTech platform if you're a small company or a big company.

Use Cases and Deployment Scope

I'm using Optimizely web experimentation to make our websites perform measurably better rather than relying on guesswork. It helps me to prove value quickly. I often set up custom event tracking, like monitoring engagement with embeded calculators or interactive product filters so we can measure deeper behaviors, not just clicks

Likelihood to Recommend

I see it as both a dev tool and a client facing value add. Every time we pitch optimization work, we now frame it as a continous project rather than one of site fixes. That shift has helped us win big

Use Cases and Deployment Scope

We are using Optimizely for experimentation. We have mainly been testing to optimise checkout conversion and have been leveraging these findings to obtain buy in to test other areas of the site.

The tool has been excellent in ensuring we build stable and performant tests, and the service from the CSM has been excellent.

A big challenge we have is velocity due to very little resources. We have used Rapid X, but are now looking to leverage an Optimizely partner such as creative cx to ramp up velocity to obtain more learnings and build a test and learn culture

Likelihood to Recommend

It’s very easy to build tests with very little technical dependencies i.e we are not tied to our internal release.

The web feature is great to quickly validate concepts without investing too much time.

We are looking to move to an agency model, so the web tool should easily allow the agency build tests on the client side.

Use Cases and Deployment Scope

We use Optimizely Web Experimentation across our websites to optimise our customer journeys, helping to drive more donors, volunteers, campaigners, service users and more. Experimentation makes it easy to create consistent experiences across multiple pages.

Likelihood to Recommend

If your website has plenty of traffic and you have the resources to run a CRO programme, you will struggle to find a more robust tool to run your experiments.

Use Cases and Deployment Scope

We use Optimizely web for A/B experimentation on our app and website

Likelihood to Recommend

I think its a great platform that's easy to use and simplifies testing and experimentation!