Google Content Experiments (discontinued) vs. Optimizely Web Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google Content Experiments (discontinued)
Score 7.3 out of 10
N/A
Google Content Experiments was a tool that can be used to create A/B test from within Google Analytics. It has been discontinued since 2019, and Google now recommends using its Google Optimize service for A/B testing.N/A
Optimizely Web Experimentation
Score 8.7 out of 10
N/A
Whether launching a first test or scaling a sophisticated experimentation program, Optimizely Web Experimentation aims to deliver the insights needed to craft high-performing digital experiences that drive engagement, increase conversions, and accelerate growth.N/A
Pricing
Google Content Experiments (discontinued)Optimizely Web Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Google Content Experiments (discontinued)Optimizely Web Experimentation
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Community Pulse
Google Content Experiments (discontinued)Optimizely Web Experimentation
Features
Google Content Experiments (discontinued)Optimizely Web Experimentation
Testing and Experimentation
Comparison of Testing and Experimentation features of Product A and Product B
Google Content Experiments (discontinued)
9.2
Ratings
11% above category average
Optimizely Web Experimentation
8.0
Ratings
3% below category average
a/b experiment testing9.00 Ratings9.00 Ratings
Split URL testing10.00 Ratings8.50 Ratings
Multivariate testing10.00 Ratings8.40 Ratings
Multi-page/funnel testing9.00 Ratings7.90 Ratings
Cross-browser testing8.00 Ratings8.10 Ratings
Mobile app testing8.00 Ratings8.00 Ratings
Test significance9.00 Ratings8.40 Ratings
Visual / WYSIWYG editor10.00 Ratings8.10 Ratings
Advanced code editor9.00 Ratings8.00 Ratings
Page surveys8.00 Ratings6.20 Ratings
Visitor recordings8.00 Ratings8.40 Ratings
Preview mode8.00 Ratings7.60 Ratings
Test duration calculator10.00 Ratings7.90 Ratings
Experiment scheduler10.00 Ratings8.20 Ratings
Experiment workflow and approval8.00 Ratings7.80 Ratings
Dynamic experiment activation10.00 Ratings7.50 Ratings
Client-side tests10.00 Ratings7.80 Ratings
Server-side tests10.00 Ratings7.20 Ratings
Mutually exclusive tests10.00 Ratings8.10 Ratings
Audience Segmentation & Targeting
Comparison of Audience Segmentation & Targeting features of Product A and Product B
Google Content Experiments (discontinued)
10.0
Ratings
16% above category average
Optimizely Web Experimentation
8.2
Ratings
4% below category average
Standard visitor segmentation10.00 Ratings8.40 Ratings
Behavioral visitor segmentation10.00 Ratings7.70 Ratings
Traffic allocation control10.00 Ratings9.10 Ratings
Website personalization10.00 Ratings7.80 Ratings
Results and Analysis
Comparison of Results and Analysis features of Product A and Product B
Google Content Experiments (discontinued)
9.9
Ratings
16% above category average
Optimizely Web Experimentation
8.3
Ratings
1% below category average
Click analytics10.00 Ratings8.80 Ratings
Form fill analysis10.00 Ratings8.00 Ratings
Conversion tracking10.00 Ratings8.70 Ratings
Goal tracking10.00 Ratings8.20 Ratings
Test reporting9.00 Ratings7.90 Ratings
Results segmentation10.00 Ratings7.70 Ratings
CSV export10.00 Ratings7.90 Ratings
Experiments results dashboard10.00 Ratings8.00 Ratings
Heatmap tool00 Ratings9.30 Ratings
Scroll maps00 Ratings8.50 Ratings
Best Alternatives
Google Content Experiments (discontinued)Optimizely Web Experimentation
Small Businesses
Convert Experiences
Convert Experiences
Score 9.9 out of 10
Convert Experiences
Convert Experiences
Score 9.9 out of 10
Medium-sized Companies
Dynamic Yield
Dynamic Yield
Score 8.3 out of 10
Dynamic Yield
Dynamic Yield
Score 8.3 out of 10
Enterprises
Dynamic Yield
Dynamic Yield
Score 8.3 out of 10
Dynamic Yield
Dynamic Yield
Score 8.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google Content Experiments (discontinued)Optimizely Web Experimentation
Likelihood to Recommend
9.0
(0 ratings)
8.7
(0 ratings)
Likelihood to Renew
7.5
(0 ratings)
9.5
(0 ratings)
Usability
-
(0 ratings)
10.0
(0 ratings)
Availability
-
(0 ratings)
10.0
(0 ratings)
Performance
-
(0 ratings)
7.3
(0 ratings)
Support Rating
8.0
(0 ratings)
10.0
(0 ratings)
Online Training
-
(0 ratings)
3.0
(0 ratings)
Implementation Rating
-
(0 ratings)
8.0
(0 ratings)
Configurability
-
(0 ratings)
6.0
(0 ratings)
Product Scalability
-
(0 ratings)
8.0
(0 ratings)
User Testimonials
Google Content Experiments (discontinued)Optimizely Web Experimentation
Likelihood to Recommend
Google Content Experiments is suited for large and small organizations, no matter your organizational goals. It is not recommended for organizations that are only interested in qualitative data, as there are other tools for receiving specific user experience feedback. It is also not recommended that you implement tests without some sort of goal in mind.
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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.
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Pros
  • When you need to measure against event-based goals
  • If you need to see how the test variations performed against secondary goals
  • Given that the the platform requires you actually code a new page with a unique URL, this tool can be good for radical redesigns.
  • Great insights into other information about your testing groups, like whether or not they're mobile, screen size, browser, or really any dimension available in GA.
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  • The Platform contains drag-and-drop editor options for creating variations, which ease the A/B tests process, as it does not require any coding or development resources.
  • Establishing it is so simple that even a non-technical person can do it perfectly.
  • It provides real-time results and analytics with robust dashboard access through which you can quickly analyze how different variations perform. With this, your team can easily make data-driven decisions Fastly.
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Cons
  • You can only optimise for one goal, so if you have several conversions, like phone call and email you need to do it manually.
  • It seems not to work that well for pages with lower amounts of traffic - not great for new or niche sites.
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  • The results view is dense and difficult to package easily for leadership, and when filtering by segment it's hard to read comparative outcomes without clearing or swapping filters
  • The organization of experiments and statuses is a cluttered list and the search is limited in use - would love to see that improve with time
  • There are so many other MarTech products out there, would love to see more dedicated integrations so we don't have to invest in something like Zapier or Tray to build hacky automations
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Likelihood to Renew
Content Experiments just makes it is simple and easy to implement A|B tests. We will be evaluating other tools in search of a more robust system for multivariate and cross-page testing, such as Optimizely or Visual Website Optimizer. However, for basic testing, you can't really beat it.
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Because it's an incredible and essential tool for my line of work as a conversion optimization specialist. Really couldn't do my job nearly as effectively without it. It's paid for itself many times over and I feel like I'm only beginning to unlock the tools potential.
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Usability
No answers on this topic
Optimizely Web Experimentation's visual editor is handy for non-technical or quick iterative testing. When it comes to content changes it's as easy as going into wordpress, clicking around, and then seeing your changes live--what you see is what you get. The preview and approval process for sharing built experiments is also handy for sharing experiments across teams for QA purposes or otherwise.
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Reliability and Availability
No answers on this topic
I would rate Optimizely Web Experimentation's availability as a 10 out of 10. The software is reliable and does not experience any application errors or unplanned outages. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
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Performance
No answers on this topic
I would rate Optimizely Web Experimentation's performance as a 9 out of 10. Pages load quickly, reports are complete in a reasonable time frame, and the software does not slow down any other software or systems that it integrates with. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
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Support Rating
Using the free tool, overall "live support" is limited. However, there are plenty of online resources to get started. If you need handheld support, it is best to upgrade the service or hire a developer through one of Google's partner agencies. There could be more support for understanding what makes a test useful or not.
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They always are quick to respond, and are so friendly and helpful. They always answer the phone right away. And [they are] always willing to not only help you with your problem, but if you need ideas they have suggestions as well.
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Online Training
No answers on this topic
The tool itself is not very difficult to use so training was not very useful in my opinion. It did not also account for success events more complex than a click (which my company being ecommerce is looking to examine more than a mere click).
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Implementation Rating
No answers on this topic
The implementation through the tag management system took a bit of trial and error at first, mostly due to the asynchronous nature of the TMS. We had to manipulate the implementation to assure that the Optimizely code was written to the page at the right time to allow the experiment content load in the browser without showing any of the original content first. We also needed to make some adjustments to the TMS code to get the integration with Site Catalyst timed appropriately.
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Alternatives Considered
Google Website Optimizer was a better product but has been discontinued. We have also used Test and Target , which has more features but we have been doing fine with Google Content Experiments. Most testing situations can be handled with Google Content Experiments.
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The ability to do A/B testing in Optimizely along with the associated statistical modelling and audience segmentation means it is a much better solution than using something like Google Analytics were a lot more effort is required to identify and isolate the specific data you need to confidently make changes
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Scalability
No answers on this topic
It's incredibly flexible and adapts well to organizations of all sizes, whether you’re running a single site or managing multiple departments and platforms. The ability to deploy experiments seamlessly across different environments is a huge plus, especially for growing businesses. While it’s highly scalable, the last point would depend on the right team leveraging its full potential.
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Return on Investment
  • Doing good experiments/Optimize has helped to take out the guesswork of the things we want to implement.
  • We have done fairly complex changes such as changing navigation and managed to see improvements outcomes immediately before we have to request developer.
  • Our teams have become more data centric in how they approach changes.
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  • we saved money by not implementing certain copy/design
  • we learned that customers from different states react different to a variation
  • we are slowly learning where conversion happens and where to fix the frictions
  • Testing shorter vs longer journeys increased funnel conversion in some states - we avoided implementing this nationwide
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ScreenShots

Optimizely Web Experimentation Screenshots

Screenshot of AI-Powered Experimentation with Opal:

- Instant Test Ideas: Generates high-quality A/B test ideas based on any goals and audience insights.
- Smarter Experimentation: The AI can suggest impactful variations, reducing guesswork and increasing test velocity.
- More Than Just Ideas: From hypothesis generation to analyzing results, Opal helps optimize every stage of the experimentation process.Screenshot of the Web Experimentation Visual Editor :

- Tweak experiments using the visual editor or dive into custom code when needed.
- Modify elements, update styling, or add dynamic behaviors.
- Ensure perfect variations while keeping control over every detail of the experiment.Screenshot of AI Content Suggestions:

- Generates copy variations to supercharge experiments.
- The AI suggests high-impact messaging for tests when hovering over a field.
- AI-powered content suggestions help skip the brainstorming process.Screenshot of Advanced Audience Targeting:

- Delivers personalized experiences by targeting users based on behaviors, attributes, and real-time conditions.
- Defines precise audience segments using first-party data, geolocation, and device type.
- Can test and optimize for different audience groups to maximize impact and engagement.Screenshot of Custom Templates in the Visual Editor:

- Offers pre-built templates for common test setups.
- Standardized variations and maintains brand integrity with reusable templates.
- Templates can be customized visually or tweak them with code for full flexibility.Screenshot of the Web Experimentation Results Page:

- Data visualizations help interpret experiment performance.
- Displays which variations are winning with built-in statistical significance calculations.
- Results can be filtered by audience segments, events, and conversions to uncover key trends.