AB Tasty vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
AB Tasty
Score 8.0 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 7.5 out of 10
N/A
Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into 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
NoYes
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
AB TastyOptimizely Feature Experimentation
Top Pros
Top Cons
Features
AB TastyOptimizely Feature Experimentation
Testing and Experimentation
Comparison of Testing and Experimentation features of Product A and Product B
AB Tasty
8.4
20 Ratings
2% below category average
Optimizely Feature Experimentation
-
Ratings
a/b experiment testing9.120 Ratings00 Ratings
Split URL testing10.013 Ratings00 Ratings
Multivariate testing8.617 Ratings00 Ratings
Multi-page/funnel testing8.615 Ratings00 Ratings
Mobile app testing7.94 Ratings00 Ratings
Visual / WYSIWYG editor7.516 Ratings00 Ratings
Advanced code editor8.66 Ratings00 Ratings
Page surveys5.03 Ratings00 Ratings
Preview mode9.17 Ratings00 Ratings
Test duration calculator9.01 Ratings00 Ratings
Experiment scheduler9.04 Ratings00 Ratings
Client-side tests8.97 Ratings00 Ratings
Mutually exclusive tests8.22 Ratings00 Ratings
Audience Segmentation & Targeting
Comparison of Audience Segmentation & Targeting features of Product A and Product B
AB Tasty
7.2
19 Ratings
20% below category average
Optimizely Feature Experimentation
-
Ratings
Standard visitor segmentation8.218 Ratings00 Ratings
Behavioral visitor segmentation6.516 Ratings00 Ratings
Traffic allocation control6.219 Ratings00 Ratings
Website personalization8.217 Ratings00 Ratings
Results and Analysis
Comparison of Results and Analysis features of Product A and Product B
AB Tasty
7.5
20 Ratings
13% below category average
Optimizely Feature Experimentation
-
Ratings
Heatmap tool7.38 Ratings00 Ratings
Click analytics7.616 Ratings00 Ratings
Scroll maps8.58 Ratings00 Ratings
Conversion tracking9.119 Ratings00 Ratings
Goal tracking8.619 Ratings00 Ratings
Test reporting5.820 Ratings00 Ratings
Results segmentation6.118 Ratings00 Ratings
Experiments results dashboard7.17 Ratings00 Ratings
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User Ratings
AB TastyOptimizely Feature Experimentation
Likelihood to Recommend
9.0
(51 ratings)
7.4
(18 ratings)
Likelihood to Renew
7.7
(8 ratings)
8.0
(1 ratings)
Usability
-
(0 ratings)
9.0
(1 ratings)
Support Rating
7.3
(2 ratings)
-
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
-
(0 ratings)
5.0
(1 ratings)
User Testimonials
AB TastyOptimizely Feature Experimentation
Likelihood to Recommend
AB Tasty
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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Optimizely
Optimizely Feature Experimentation works really well for setting up feature flags with an easy UI for turning them on and off or ramping up a gradual rollout. It also works really well to set up split tests where you can split your traffic by percentage as well as almost any custom data attribute you wish to define. This is more for robust features and less for visual changes - Optimzely Edge or Web are better suited for that.
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Pros
AB Tasty
  • Customer-focused. Over the past year, AB Tasty has worked with us to overcome many obstacles (e.g. tracking hurdles, ITP issues, etc.). The team always finds a solution in a timely manner to ensure we're able to execute on experiments.
  • Reliability. One of the biggest hurdles earlier this year were the ITP regulations set out by Apple. Over 40% of our ad traffic was Safari users. AB Tasty was using 3rd party cookies but quickly migrated over to a local session storage solution that circumvented the issue.
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Optimizely
  • Its ability to run A/B tests and multivariate experiments simultaneously allows us to identify the best-performing options quickly.
  • Optimizely blends into our analytics tools, giving us immediate feedback on how our experiments are performing. This tool helps us avoid interruptions. With this pairing, we can arrive at informed decisions quickly.
  • Additionally, feature toggles enable us to introduce new features or modifications to specific user groups, guaranteeing a smooth and controlled user experience. This tool helps us avoid interruptions.
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Cons
AB Tasty
  • Sometimes the platform is a little glitchy and requires a refresh.
  • Would be great to be able to see the experiments and projects on a timeline with their status and main result to have an immediate view of ongoing and completed projects.
  • Email notification for when an experiment reaches a certain confidence level.
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Optimizely
  • Splitting feature flags from actual experiments is slightly clunky and can be done either as part of the same page or better still you can create a flag on the spot while starting an experiment and not always needing to start with a flag.
  • Recommending metrics to track based on description using AI
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Likelihood to Renew
AB Tasty
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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Optimizely
No answers on this topic
Usability
AB Tasty
No answers on this topic
Optimizely
All features that we used were pretty clear. They have a good documentation
Read full review
Support Rating
AB Tasty
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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Optimizely
No answers on this topic
Implementation Rating
AB Tasty
No answers on this topic
Optimizely
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
AB Tasty
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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Optimizely
Optimizely Feature Experimentation is better for building more complex experiments than Optimizely Web. However, Optimizely Web is much easier to kickstart your experimentation program with as the learning curve is much lower, and dedicated developer resources are not always necessary (marketers can build experiments quickly with Optimizely Web without developers' help).
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Scalability
AB Tasty
No answers on this topic
Optimizely
had troubles with performance for SSR and the React SDK
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Return on Investment
AB Tasty
  • Since we use AB tasty our conversion rates have significantly increased on the Watch pages.
  • We can report positive comments about the evolutions of our interfaces, from our expert users. And so improved customer satisfaction.
  • The solution allows us to validate or invalidate without taking major risk while gaining in reactivity, our proposals relook pages.
  • Commitment rates are much higher, multiplied by 2 or 3, when we implement customizations that are relevant. These customizations are very relevant insofar as we are governed by the seasonality of events as a media site.
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Optimizely
  • Experimentation is key to figuring out the impact of changes made on-site.
  • Experimentation is very helpful with pricing tests and other backend tests.
  • Before running an experiment, many factors need to be evaluated, such as conflicting experiments, audience, user profile service, etc. This requires a considerable amount of time.
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ScreenShots

AB Tasty Screenshots

Screenshot of Reports: Quickly interpret results with a robust reporting interfaceScreenshot of Dynamic Widget Library: Nudge visitors into actionScreenshot of AB Tasty Editor: Easy-to-use WYSIWYG editorScreenshot of Dashboard: Manage all your campaigns in one placeScreenshot of Targeting Criteria: Target specific user segments

Optimizely Feature Experimentation Screenshots

Screenshot of AI Variable suggestions: AI helps to develop higher quality experiments. Optimizely’s Opal suggests content variations in experiments, and helps to increase test velocity  and improve experiment qualityScreenshot of Integrations: display of the available integrations in-app.Screenshot of Reporting used to share insights, quantify experimentation program performance using KPIs like velocity and conclusive rate across experimentation projects, and to drill down into the charts and figures to see an aggregate list of experiments. Results can be exported into a CSV or Excel file, and KPIs can be segmented using project filters, experiment type filters, and date rangesScreenshot of Collaboration: Centralizes tracking tasks in the design, build, and launch of an experiment to ensure experiments are launched on time . Includes calendar, timeline, and board views in customizable views that can be saved to share with other stakeholdersScreenshot of Scheduling: Users can schedule a Flag or Rule to toggle on/off,  traffic allocation percentages, and achieve faster experimentation velocity and smoother progressive rolloutsScreenshot of Metrics filtering: Dynamic event properties to filter through events. Dynamic events provide better insights for experimenters who can explore metrics in depth for more impactful decisions