AB Tasty vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
AB Tasty
Score 8.8 out of 10
Mid-Size Companies (51-1,000 employees)
AB Tasty is a SAAS application created for e-marketers that enables them to optimize their website and conversion rate without technical knowledge. They can test several versions of their pages to identify which one has the biggest impact on their business objectives, e.g. click-through rate on a call to action button, add-to-cart rate, global conversion rate of their website.N/A
Optimizely Feature Experimentation
Score 8.7 out of 10
N/A
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.N/A
Pricing
AB TastyOptimizely Feature Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
AB TastyOptimizely Feature Experimentation
Free Trial
NoNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
AB TastyOptimizely Feature Experimentation
Features
AB TastyOptimizely Feature Experimentation
Testing and Experimentation
Comparison of Testing and Experimentation features of Product A and Product B
AB Tasty
8.1
Ratings
1% below category average
Optimizely Feature Experimentation
-
Ratings
a/b experiment testing8.20 Ratings00 Ratings
Split URL testing7.30 Ratings00 Ratings
Multivariate testing8.20 Ratings00 Ratings
Multi-page/funnel testing9.00 Ratings00 Ratings
Mobile app testing5.00 Ratings00 Ratings
Test significance8.30 Ratings00 Ratings
Visual / WYSIWYG editor10.00 Ratings00 Ratings
Advanced code editor8.30 Ratings00 Ratings
Page surveys5.00 Ratings00 Ratings
Preview mode7.50 Ratings00 Ratings
Test duration calculator10.00 Ratings00 Ratings
Experiment scheduler9.20 Ratings00 Ratings
Client-side tests9.80 Ratings00 Ratings
Server-side tests7.00 Ratings00 Ratings
Mutually exclusive tests9.20 Ratings00 Ratings
Audience Segmentation & Targeting
Comparison of Audience Segmentation & Targeting features of Product A and Product B
AB Tasty
8.5
Ratings
0% below category average
Optimizely Feature Experimentation
-
Ratings
Standard visitor segmentation9.20 Ratings00 Ratings
Behavioral visitor segmentation7.50 Ratings00 Ratings
Traffic allocation control9.20 Ratings00 Ratings
Website personalization8.30 Ratings00 Ratings
Results and Analysis
Comparison of Results and Analysis features of Product A and Product B
AB Tasty
8.3
Ratings
1% below category average
Optimizely Feature Experimentation
-
Ratings
Heatmap tool7.30 Ratings00 Ratings
Click analytics7.60 Ratings00 Ratings
Scroll maps8.50 Ratings00 Ratings
Conversion tracking8.20 Ratings00 Ratings
Goal tracking9.00 Ratings00 Ratings
Test reporting8.30 Ratings00 Ratings
Results segmentation8.00 Ratings00 Ratings
Experiments results dashboard9.20 Ratings00 Ratings
User Ratings
AB TastyOptimizely Feature Experimentation
Likelihood to Recommend
9.0
(0 ratings)
8.9
(0 ratings)
Likelihood to Renew
7.7
(0 ratings)
4.5
(0 ratings)
Usability
9.0
(0 ratings)
7.3
(0 ratings)
Support Rating
7.3
(0 ratings)
-
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(0 ratings)
Product Scalability
-
(0 ratings)
5.0
(0 ratings)
User Testimonials
AB TastyOptimizely Feature Experimentation
Likelihood to Recommend
A/B Tasty tool allows us do easy testing without burdening our limited developer resources all the time. Reports are simple enough to interpret. Support has been excellent and proactive, also the onboarding was successful. It is also an advantage that we have the possibility to drive all our traffic to the test versions rather than being limited to specific user amount per month. We can recommend A/B Tasty for testing purposes, as the platform keeps it’s promises, doesn’t require too much technical knowledge and the support is excellent.
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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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Pros
  • Easy setup - Simple script insertion into site header.
  • Intuitive interface - It took very little training for our team to understand how to start running A/B tests. The reporting is much more comprehensive, yet easily digestible than other platforms we have used or considered. Confidence scores, flexible KPI inclusions/tracking allow us to understand the results or non-result quickly and with clarity to make decisions on next steps.
  • Customer support - Our CSM and tech support are always helpful and proactive when a question or issue arise. Our CSM keeps us on track when an idea might have dropped off the map due to other priorities, and brings great ideas to the table. We enjoy our monthly touchbases with her when we get to see new functionalities, how other clients successfully used them, and brainstorm ways we can use them for our experience.
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  • Splitting traffic between variants and enabling you to scale up or down the amount of traffic in each one
  • Giving a standardised report that you can share with a huge number of users
  • Showing a large variety of results/metrics you can then dive into
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Cons
  • Sometimes when you're preparing a test the editor "crashes" and you need to re open it. It's just a matter of having to go back to the main dashboard a few times during your preparation.
  • Maybe to have the option to have pre-build templates of pages, buttons and assets that we can use to test experiments.
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  • Difficult integration if your data is not front end
  • Costly MAU model needs to be based on experiments not on site visits
  • It's not easy to understand how to build an Experiment
  • Onboarding team is more focused on punching through their slides and not focused on your needs or understanding.
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Likelihood to Renew
AB Tasty's tool as well as the support team completely met my goals on A/B Testing. Editing a test is really easy and AB Tasty made the marketing team free to launch nearly any test. Reporting is also easy to set up and give us the information needed to keep improving transformation on landing pages and forms
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Competitive landscape
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Usability
The tool’s usability is excellent, with a smooth and intuitive interface. It is continuously updated with new features and improvements aimed at enhancing the user experience. Setting up experiments and personalizations is straightforward, thanks to a well-structured process that even less experienced users can easily follow. There are sometimes small issues with the QA section which slows the QA process but it is so much better now compared to 2024.
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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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Support Rating
Support is good, but it would be better if it was quicker or if AB Tasty provided a quicker SLA. At times, you require stuff urgently but AB Tasty support isn't as quick as I personally like it to be.
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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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Online Training
There is a vast amount of training available on their online platform, including their university area of which you can get certified for (and then share this with your connections on platforms such as Linkedin). There is also specific hand created training which can be provided by your customer success manager if that is requested by yourself.
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No answers on this topic
Implementation Rating
No answers on this topic
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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Alternatives Considered
We selected AB Tasty mostly because we got a sense that their dev and customer service teams were going to go above and beyond to help us out. We were right! The cost was also a factor as they came in a small bit lower, but cost wasn't the only factor.
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In previous companies I've used Monetate which is a similar A/B testing kind of feature experimentation engine that is very similar from my memory, but again, back to the point of these new features of the analytics engine and Opal, it kind of cuts it above Monetate from my experience. Obviously Monetate may have improved since when I lost use it, but from what I can see, yeah.
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Scalability
No answers on this topic
had troubles with performance for SSR and the React SDK
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Return on Investment
  • This past year we improved our new visitor conversion rates by 73% with very little increases in our ad spend. We've been able to test quickly and infer how those experiments impacted lead generation. Experimentation is all about continually learning and testing.
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  • 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.
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ScreenShots

AB Tasty Screenshots

Screenshot of the reporting interface.Screenshot of the dynamic widget library.Screenshot of AB Tasty's WYSIWYG editorScreenshot of the campaign management dashboardScreenshot of targeting criteria, available so users can target specific user segments

Optimizely Feature Experimentation Screenshots

Screenshot of Feature Flag Setup. Here users can run flexible A/B and multi-armed bandit tests, as well as:

- Set up a single feature flag to test multiple variations and experiment types
- Enable targeted deliveries and rollouts for more precise experimentation
- Roll back changes quickly when needed to ensure experiment accuracy and reduce risks
- Increase testing flexibility with control over experiment types and delivery methodsScreenshot of Audience Setup. This is used to target specific user segments for personalized experiments, and:

- Create and customize audiences based on user attributes
- Refine audience segments to ensure the right users are included in tests
- Enhance experiment relevance by setting specific conditions for user groupsScreenshot of Experiment Results, supporting the analysis and optimization of experimentation outcomes. Viewers can also:

- examine detailed experiment results, including key metrics like conversion rates and statistical significance
- Compare variations side-by-side to identify winning treatments
- Use advanced filters to segment and drill down into specific audience or test dataScreenshot of a Program Overview. These offer insights into any experimentation program’s performance. It also offers:

- A comprehensive view of the entire experimentation program’s status and progress
- Monitoring for key performance metrics like test velocity, success rates, and overall impact
- Evaluation of the impact of experiments with easy-to-read visualizations and reporting tools
- Performance tracking of experiments over time to guide decision-making and optimize strategiesScreenshot of AI Variable Suggestions. These enhance experimentation with AI-driven insights, and can also help with:

- Generating multiple content variations with AI to speed up experiment design
- Improving test quality with content suggestions
- Increasing experimentation velocity and achieving better outcomes with AI-powered optimizationScreenshot of Schedule Changes, to streamline experimentation. Users can also:

- Set specific times to toggle flags or rules on/off, ensuring precise control
- Schedule traffic allocation percentages for smooth experiment rollouts
- Increase test velocity and confidence by automating progressive changes