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.
Pros
We have not been using the latest, I don't know if it's available. We have been creating variance on the portal. We have seen a recent change as well, like how you can create a variant, how experiment suggests you to do a lot of different options actually. We were able to create our own variation with very minimal changes. Selecting the content on the website. It is very easy to use actually as a developer, I need not to go pitching into every single experiment. My marketing team can simply do it without the knowledge actually.
Cons
One thing which I have predominantly noticed is my marketing team can use it. They kind of click through the content and they can change the content and how it has to present to the user. But in case if we have multiple selectors, in case if we have a selector which kind of selects multiple content on the same page, probably one of the example that we had is people wanted to change at one particular place, which ended up changing multiple places on the same webpage. And developers have to pitch in that time and we have to provide the right selected. So without a developer experience, without knowing how the HTML element works, people will not be able to do everything. Actually probably that is one of the con that I have seen.
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.
VU
Verified User
Manager in Information Technology (Information Services company, 1001-5000 employees)
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.
Pros
I think the best aspect of it is because I also manage a team that builds agent experiments, which are a little more complicated. They involve a lot of complex logic and conditions and really focuses in on certain audiences. So when we look at web experiments, the best benefits are getting things off the ground within a matter of minutes. Whereas agent experiments, there's a lot of background build involved with web experiments, we can have an idea, we can build it in web and it can be launched the same day. So it really helps us get to answers faster and make those decisions faster and then lead to other ideas for things we can do on other parts of the website.
Cons
I think because I work with both types of experiments, web and agent experiments, the sort of drawbacks come with web that it's the audience logic. Sometimes we have to identify specific customers that we want to target with the change. And a lot of it is probably down to our infrastructure on the site. It's not giving Optimizely the right level of data to target these customers. But I think, yeah, if we had a little bit more understanding of how we could get to that data through optimizing web, that would be useful for us.
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.
VU
Verified User
Manager in Marketing (Broadcast Media company, 10,001+ employees)
We use web experimentation to A/B test new functionality on our website. The goal is to improve the conversion rate.
Pros
I don't have a strong statistics background, so the thing that I really love about web experimentation is the stats engine. It makes understanding the impact of the experiments that we run a lot easier to understand. It calculates statistical significance and takes away all of the guesswork and the complicated formulas that go into that.
Cons
One of the areas that we struggle with when we are running experiments, which have a non-conversion rate objective. So things like revenue metrics where it's not a yes or no answer. We have to use other reporting tools to get information on how those metrics are being impacted. So wish there was a more easy way to track those kind of metrics. Average order value through Optimizely, that would be really, really useful.
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.
VU
Verified User
Manager in Product Management (Telecommunications company, 10,001+ employees)
At Zoom, we use Optimizely Web Experimentation to optimize user acquisition, conversion, and engagement on our website and product flows. It helps us test and iterate on pricing pages, onboarding journeys, and feature visibility to address key business challenges like increasing paid upgrades, improving activation rates, and driving monetization strategies for our freemium and Pro plans.
Pros
Easy analysis of data
Easy stat sig
Eeasy trends of data
Cons
It’s difficult to go from results to the different details about the test.
The most organization of tests is difficult to scan through
Stat sig doesn’t seem to be accurate
Likelihood to Recommend
It’s great for implementing tests or rolling out quick fixes. It’s not as good as an analysis tool since it’s only digital metrics
VU
Verified User
Contributor in Product Management (Computer Software company, 5001-10,000 employees)
We use Optimizely Web Experimentation for extensive AB testing and have been able to drive meaningful revenue growth via AB testing our end to end digital experiences including checkout. We are able to make data driven decisions with the Optimizely platform.
Pros
Easy interface
Innovative features
Integrations
Cons
NetSpring is a good add excited to see it come together
JIRA integration is manual and time consuming
n/a
Likelihood to Recommend
Ability to quickly test front end experiences
VU
Verified User
Director in Product Management (Telecommunications company, 5001-10,000 employees)
We use tOptimizely Web Experimentation to plan and execute all tests that provide meaningful insight to the health of our company and the relationship we have with every user. The analysis we derive from all the tests we have performed feed the planning and execution cycles of our team so that we can meet every quarter prepared.
Pros
Stats Engine
Personalization Campaigns
MABs
Cons
Sometimes integrations with certain tools can be a little difficult
Missing more data visualization in the results portion of tests
Can we integrate live tests results into the experiment collab?
Likelihood to Recommend
Thanks to Optimizely Web Experimentation every member of our team can contribute test ideas that can bring meaningful results and conclusions. The AI generation feature that was added to the experiment development side has been a great contribution giving us those small incremental gains that have feed our revenue model substantially.
We use Optimizely Web Experimentation to experiment on conversion optimization and new features. We are able to test strategies on specific audiences to improve our user experience
Pros
Customer support
AB testing
personalization
Cons
onboarding
learning curve can be a challenge
Likelihood to Recommend
Optimizely Web Experimentation is great for testing and rolling out new features
VU
Verified User
Analyst in Product Management (Media Production company, 201-500 employees)
Testing offers, creative, messaging and interrupt frequency to drive paywall conversions to sell digital subscriptions.
Pros
Intuitive UI
Experimentation setup and targeting without involving engineering
Easy configuration
Cons
Ability to rank experiments
Ability to see where targeting logic is overlapping across experiments
Implement a chrome extension that lets you easily QA variations
Likelihood to Recommend
Broader experiments related to content, design, messaging that can be contained to a single session. When you're looking to integrate data from other sources or follow a user across sessions, Optimizely Feature will be a much more lightweight implementation.
To identify most desirable options for the customer, test personalisation using holdback tactics and to generate insights using strategies such as painted door.
Pros
Reliable Stats engine
Integration with Opti ODP
Clean UI for managing tests
Cons
Costs are high idlf looking for entry level
Tiered pricing and features can be a little unclear
Likelihood to Recommend
The platform is well suited for testing/optimising elements such as CTAs, layouts, new componens, link destinations, new user flows - but on med/high-traffic sites.
It's less suitable for sites with low traffic, where testing can take a while to yield significant results, or for complex tests requiring heavy backend-heavy changes.
VU
Verified User
Director in Marketing (Internet company, 11-50 employees)
