Google Analytics vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google Analytics
Score 7.9 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 8.7 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
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
Features
Google AnalyticsOptimizely Feature Experimentation
Web Analytics
Comparison of Web Analytics features of Product A and Product B
Google Analytics
8.2
Ratings
2% above category average
Optimizely Feature Experimentation
-
Ratings
Lead Conversion Tracking7.50 Ratings00 Ratings
Bounce Rate Measurement8.50 Ratings00 Ratings
Device and Browser Reporting8.50 Ratings00 Ratings
Pageview Tracking8.00 Ratings00 Ratings
Event Tracking7.00 Ratings00 Ratings
Reporting in real-time10.00 Ratings00 Ratings
Referral Source Tracking8.00 Ratings00 Ratings
Customizable Dashboards8.50 Ratings00 Ratings
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Google AnalyticsOptimizely Feature Experimentation
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Score 9.0 out of 10
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Score 8.7 out of 10
Medium-sized Companies
Siteimprove
Siteimprove
Score 10.0 out of 10
GitLab
GitLab
Score 8.7 out of 10
Enterprises
Optimal
Optimal
Score 9.0 out of 10
GitLab
GitLab
Score 8.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google AnalyticsOptimizely Feature Experimentation
Likelihood to Recommend
8.2
(0 ratings)
8.9
(0 ratings)
Likelihood to Renew
9.0
(0 ratings)
4.5
(0 ratings)
Usability
7.5
(0 ratings)
7.3
(0 ratings)
Availability
10.0
(0 ratings)
-
(0 ratings)
Performance
10.0
(0 ratings)
-
(0 ratings)
Support Rating
7.0
(0 ratings)
-
(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.
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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
  • 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
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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
  • 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!
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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
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.
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Competitive landscape
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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
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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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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.
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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
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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.
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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
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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.
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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
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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.
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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

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