Google Analytics vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google Analytics
Score 8.2 out of 10
N/A
Google Analytics is perhaps the best-known web analytics product and, as a free product, it has massive adoption. Although it lacks some enterprise-level features compared to its competitors in the space, the launch of the paid Google Analytics Premium edition seems likely to close the gap.
$150,000
per year
Optimizely Feature Experimentation
Score 8.3 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
Google AnalyticsOptimizely Feature Experimentation
Editions & Modules
Google Analytics 360
150,000
per year
Google Analytics
Free
No answers on this topic
Offerings
Pricing Offerings
Google AnalyticsOptimizely Feature Experimentation
Free Trial
NoNo
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
Google AnalyticsOptimizely Feature Experimentation
Considered Both Products
Google Analytics
Chose Google Analytics
Wix and other website platforms have a built-in analytic tool, but it is not as sufficient as Google Analytics, so I always use GA as the main source of information about sales. Most of the analytic tools on web platforms can't visualize user flows, which is very important when …
Chose Google Analytics
The entry threshold is lower and Google Analytics can be used to grant access even to technically intermediate users who can draw basic conclusions on their own.
Chose Google Analytics
We use google analytics instead of other tools for customer usage data and behavioral. It is better to connect with some sources.
Chose Google Analytics
Google Analytics is for me the default one to implement especially for business starting in analytics. The time (aka cost) of implementation is very low and it provides results in a matter of hours. The integration with the Google ecosystem is also a plus especially when …
Chose Google Analytics
Adobe analytics is better in almost all aspects except for it's complexity in implementation.
Chose Google Analytics
Microsoft Clarity is speedy, extremely tidy, and straight to the point, and it contains everything a SME would need to maintain a healthy SEO without the need for technical understanding; its UI is far superior to GA, and it also provides additional capabilities like as …
Chose Google Analytics
Webtrends as a platform is older than Google Analytics and still quite good. If you have a company that is used to using Webtrends, it's likely still a good fit for you. Google Analytics has a lower entry cost and more accessible training to new Users, so that's why I would …
Chose Google Analytics
Adobe Analytics is good but it is more suited to people who are fully and technically into reporting and the solutions it provides. Google Analytics on the other hand provides a much easier way of setting up the Analytics. Most of the data reporting, charts and visualisations …
Chose Google Analytics
Ease of use: Google Analytics is known for its user-friendly interface and straightforward setup process, making it accessible for beginners. Adobe Analytics has a steeper learning curve and requires more technical expertise.
Features: Adobe Analytics offers a more comprehensive …
Chose Google Analytics
Universal GA is free to use, offers a good amount of data, and is relatively easy to use. Other products may not offer the detail needed (Google Tag Manager), or require payment (Adobe Target)
Chose Google Analytics
Google tag manager is the best tool to use with Google analytics as it provides more in-depth analysis where users interact on the website.
Chose Google Analytics
Built-in reports are beneficial but you can create custom reports if you need more details with different dimensions and metrics it also provides insights which is just little data about your site traffic in sentence format its the best way to know which strategy you are on …
Chose Google Analytics
Adobe and Google Analytics are fairly similar. Google Analytics was more widely known among my team. Most of us have used it in some capacity in the past. It's also easy to navigate, and there are loads of free training out there on how to use the platform.
Chose Google Analytics
Google provides a wide suite of products that all tie into Google Analytics. Some that I use most often are Tag Manager, Ads and Datastudio. All of these connect directly with Analytics and allow me to accomplish my goals. For example, Ads will connect and show me what Ads are …
Chose Google Analytics
I have not used Adobe Analytics as much, but I know they offer something called customer journey analytics, which we are evaluating now.
I have used Semrush, and I find them much better than Google Analytics. I feel a fairly nontechnical person could learn Semrush in about a …
Chose Google Analytics
Adobe Analytics and Google Analytics 360 are both paid/premium options for website tracking. Though there are certain use cases when these might make sense (you operate entirely in the Adobe suite, you're a massive company/site that doesn’t mind the price tag on Google …
Chose Google Analytics
Google Analytics provides everything you need in terms of straight forward analytics needs. The tool is not very flexible compared to other software such as adobe, and if you want to upgrade to premium or add in a bunch of custom situations, that can be become very [tedious] …
Chose Google Analytics
Google Analytics had the best price (it's free for major of our clients), and it was easy to find professionals that had experience on using it.
Chose Google Analytics
Google Analytics is easy to use and widespread, it's a must-have software for all businesses. The price point compared to other software that we use is very reasonable, and the suite of services and training tools/certifications available for free is hard for other software …
Chose Google Analytics
Adobe Analytics has been in the market for a long time; some people still know it as Omniture or SiteCatalyst. It seems that some great ideas from Google Analytics, such as Enhanced Ecommerce and the new way of measuring events with GA4, are adapted from the traditional Adobe …
Chose Google Analytics
GA will always have an advantage with data, because it's the source, but other companies do a better job of specializing in certain areas or providing better UX/UI. HubSpot is the king of the latter and SEMrush is ideal for making organic improvements based on data. Ahrefs are …
Chose Google Analytics
We have been using Adobe Analytics for a while but the system seemed to be more complex when compared to super user friendly Google Analytics. Moreover, the option to add custom metrics and dimensions is lacking in Adobe Analytics. Google Analytics is good with transactional …
Chose Google Analytics
Google Analytics is really unique so it's hard to have competitors - especially when Google Analytics is free (unless you are part of a huge company so in the case you will need the Premium version). Other products like SemRush are good as third party tools and figure out the …
Chose Google Analytics
I used Facebook Analytics for mobile and web games but Facebook Analytics was discontinued. Google Analytics is more universal and is suitable for both web and native mobile applications. Facebook Analytics is more suitable for apps and games on web and mobile. For mobile …
Optimizely Feature Experimentation
Chose Optimizely Feature Experimentation
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 …
Chose Optimizely Feature Experimentation
I wasn’t part of the team that selected Optimizely, but its integrations with other tools were a big plus for us in making our decision. It was more expensive, however.
Chose Optimizely Feature Experimentation
We have not used any other similar tools, we evaluated both Kameleoon and VWO. With the combination of price, features, and expandability, we moved forward with Optimizely Feature Experimentation.
Chose Optimizely Feature Experimentation
Google optimize is great that it is an add on to an existing Analytics implementation, but only has a web version. Optimizely has the SDK so better option for testing new features
Chose Optimizely Feature Experimentation
We selected Optimizely as it was easy to use/understand, had clearly defined SLAs for keeping the platform up and was regarded as resilient within the industry. We needed something at our point in our experimentation journey that could be used for Product testing at scale and …
Chose Optimizely Feature Experimentation
Optimizely Feature Experimentation has similar features to Amplitude. As a matter of fact it looks like Amplitude copied Optimizely. However, Amplitude did not mimic the nomenclature issues.
Chose Optimizely Feature Experimentation
When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team.

Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities.
Chose Optimizely Feature Experimentation
In other companies, all of the feature flag controls were done locally and it got messy after a while. There was no much control on who was doing what. With Optimizely Feature Experimentation, it is clear what feature flags are enabled and which ones are not. It is easier to …
Chose Optimizely Feature Experimentation
not too much experience on that to answer this question
Chose Optimizely Feature Experimentation
There is a lot more flexibility with Optimizely once you have customized the implementation and better tools.
Chose Optimizely Feature Experimentation
WebX and FeatureX work well in pair, they organically complement each other
Chose Optimizely Feature Experimentation
Simple interface and ability to create audiences and assign them to experiments.
Chose Optimizely Feature Experimentation
Optimizely offered both web experimentation (WSYWIG editor for nontechnical marketing folks) and Feature Experimentation. That made the decision easier to get max value across different stakeholder groups.
Chose Optimizely Feature Experimentation
Optimizely FX is the only tool I've used that specifically allows for testing in the back-end. Most front end tools are great for simple tests, but there comes a time when you need to go a level deeper and that's not possible with front-end tools.
Chose Optimizely Feature Experimentation
Mixpanel, Google Analytics, Hotjar and A/B Smartly
Chose Optimizely Feature Experimentation
With the Netspring acquisition I think it's closer to its competitor's features
Chose Optimizely Feature Experimentation
I prefer Optimizely Feature Experimentation to web experimentation. I think it's more straightforward to set up and as an engineer, I like being able to have more control from the code side.
Chose Optimizely Feature Experimentation
we wanted a shift with the tool that helps us with managing our data
Chose Optimizely Feature Experimentation
We haven't evaluated other products. We have an in-house product that is missing a lot of features and is very behind from making the test process easier.

Instead of evolving our in-house product with limited resources, we decided to go with Optimizely Feature Experimentation …
Chose Optimizely Feature Experimentation
Overall, Optimizely Feature Experimentation is an industry leader in terms of experimentation across web and mobile. For apps I would say amplitude does slightly a better job as it is tailored to that niche.
Chose Optimizely Feature Experimentation
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 …
Chose Optimizely Feature Experimentation
Optimizely Feature Experimentation is less of a point solution than LaunchDarkly, so LD has a few extra features, but Optimizely offers a much greater solution for experimentation, personalization etc.
Chose Optimizely Feature Experimentation
Feature experimentation is much more robust and allows more granular control over the decisions you want to make. While Optimizely Feature Experimentation is nice and can be delivered via Optimizely Feature Experimentation's UI, its still not ideal because its brittle and can …
Features
Google AnalyticsOptimizely Feature Experimentation
Web Analytics
Comparison of Web Analytics features of Product A and Product B
Google Analytics
8.4
Ratings
4% above category average
Optimizely Feature Experimentation
-
Ratings
Lead Conversion Tracking8.10 Ratings00 Ratings
Bounce Rate Measurement8.40 Ratings00 Ratings
Device and Browser Reporting9.20 Ratings00 Ratings
Pageview Tracking9.10 Ratings00 Ratings
Event Tracking8.40 Ratings00 Ratings
Reporting in real-time7.90 Ratings00 Ratings
Referral Source Tracking8.60 Ratings00 Ratings
Customizable Dashboards7.90 Ratings00 Ratings
Best Alternatives
Google AnalyticsOptimizely Feature Experimentation
Small Businesses
StatCounter
StatCounter
Score 9.0 out of 10
GitLab
GitLab
Score 8.8 out of 10
Medium-sized Companies
Siteimprove
Siteimprove
Score 9.1 out of 10
GitLab
GitLab
Score 8.8 out of 10
Enterprises
Optimal
Optimal
Score 9.0 out of 10
GitLab
GitLab
Score 8.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google AnalyticsOptimizely Feature Experimentation
Likelihood to Recommend
8.6
(0 ratings)
8.2
(0 ratings)
Likelihood to Renew
9.0
(0 ratings)
4.5
(0 ratings)
Usability
7.4
(0 ratings)
7.6
(0 ratings)
Availability
10.0
(0 ratings)
-
(0 ratings)
Performance
10.0
(0 ratings)
-
(0 ratings)
Support Rating
7.0
(0 ratings)
3.6
(0 ratings)
Online Training
10.0
(0 ratings)
-
(0 ratings)
Implementation Rating
9.0
(0 ratings)
10.0
(0 ratings)
Configurability
6.0
(0 ratings)
-
(0 ratings)
Ease of integration
10.0
(0 ratings)
-
(0 ratings)
Product Scalability
10.0
(0 ratings)
5.0
(0 ratings)
Vendor post-sale
10.0
(0 ratings)
-
(0 ratings)
Vendor pre-sale
9.0
(0 ratings)
-
(0 ratings)
User Testimonials
Google AnalyticsOptimizely Feature Experimentation
Likelihood to Recommend
Honesty, there is no reason that a company wouldn’t want to implement Google Analytics. The regular version is completely free, is very easy to configure, and provides immense volumes of website data. There are also tangible benefits to the other Google tools it can connect to, and it integrates with any BI/data platform that you might use. The only time I’d advise not using standard Google Analytics is if you’ve purchased Google Analytics 360.
Read full review
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 -
Read full review
Pros
  • Multiple reports to see website use and behavior
  • Allows you to customize reports with days, weeks, months, and years
  • You can build out a dashboard to easily view stats from multiple websites in one place
  • You can share analytics reports via the dashboard, automatically emailed PDFs or in other formats
Read full review
  • 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
Read full review
Cons
  • While raw data is nice to have, I do wish there was an easier way to provide reports from Google Analytics directly. Something that could answer questions straight-forward for people.
  • I would appreciate "helpful hints" or a cheat sheet of some sort, so when quickly searching for something such as time on a certain page, I can find it quickly.
  • I really don't have a third point!
Read full review
  • 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.
Read full review
Likelihood to Renew
Having used Google Analytics for the last 9 years, I have no intention of discontinuing my service. Google Analytics is a fantastic product that provides me with almost everything I could wish for. The positives in this product outweigh any negatives that you might find. I can not think of a single reason to not immediately start using Google Analytics for your business.
Read full review
Competitive landscape
Read full review
Usability
Google Analytics provides a wealth of data, down to minute levels. That is it's greatest detriment: find the right information when you need it can be a cumbersome task. You are able to create shortcuts, however, so it can mitigate some of this problem. Google is continually refining Analytics, so I do not doubt there will be improvements
Read full review
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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Reliability and Availability
We all know Google is at top when it comes to availability. We have never faced any such instances where I can suggest otherwise. All you need is a Google account, a device and internet connection to use this super powerful tool for reporting and visualising your site data, traffic, events, etc. that too in real time.
Read full review
No answers on this topic
Performance
This has been a catalyst for improving our site's traffic handling capabilities. We were able to identify exit% from our sites through it and we used recommendations to handle and implement the same in our sites. We have been increasing the usage of Google Analytics in our sites and never had any performance related issues if we used Analytics
Read full review
No answers on this topic
Support Rating
The Google reps respond very quickly. However, sometimes they can overly call you to set up an apportionment. I'm very proficient and sometimes when I talk to reps, they give beginner tutorials and insights that are a waste of time. I wish Google would understand my level of expertise and assign me to a rep (long-term) that doesn't have to walk me through the basics.
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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
love the product and training they provide for businesses of all sizes. The following list of links will help you get started with Google Analytics from setup to understanding what data is being presented by Google Analytics.
  1. How to Use Google Analytics for Beginners – Mahalo’s how-to guide for beginners.
  2. A beginner’s guide to Google Analytics – A free eBook walking you through Google Analytics from setup to understanding what data is being presented.
  3. Getting to Know Your Google Analytics Dashboard – The title says it all! This is a brief post with one goal: to introduce you to the Google Analytics dashboard.
  4. Google Analytics for Beginners: How to Make the Most of Your Traffic Reports– This guide doesn’t cover setup, but it does a great job of helping you to better understand the data being presented.
  5. Google Analytics Video Tutorial 1: Setup – A video presentation that walks you through Google Analytics setup.
  6. Google Analytics Video Tutorial 2: Essential Stats – A video presentation that introduces you to some of the most important data being presented in Google Analytics.
Read full review
No answers on this topic
Implementation Rating
Make sure to put the tracking code on every page. Ideally this would be part of a template or "include" so you can update the code on all pages (or at least within pages of the same category) at once.
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It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it
Read full review
Alternatives Considered
I have not used Adobe Analytics as much, but I know they offer something called customer journey analytics, which we are evaluating now. I have used Semrush, and I find them much better than Google Analytics. I feel a fairly nontechnical person could learn Semrush in about a month. They also offer features like competitive analysis (on content, keywords, traffic, etc.), which is very useful. If you have to choose one among Semrush and Google Analytics, I would say go for Semrush.
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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.
Read full review
Scalability
Google Analytics is currently handling the reporting and tracking of near about 80 sites in our project. And I am not talking about the sites from different projects. They may have way more accounts than that. Never ever felt a performance issue from Google's end while generating or customising reports or tracking custom events or creating custom dimensions
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had troubles with performance for SSR and the React SDK
Read full review
Return on Investment
  • Great for visualizing website drop-off pages to theories and test update/iterations.
  • Bounce rates on pages to pinpoint bugs and issues.
  • Inaccuracy can lead to incorrect conclusions and decisions around CRO.
  • Segments can be very useful for validating split testing, providing a free tracking of variation vs. control - great ROI.
Read full review
  • We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.
Read full review
ScreenShots

Optimizely Feature Experimentation Screenshots

Screenshot of Feature Flag Setup. Here users can run 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