Fullstory vs. Optimizely Web Experimentation vs. Quantum Metric

It was extremely useful in identifying places in our product where things weren't functioning, or where it looked like action was available to the end user but in reality, it was not and therefore caused lots of confusion. It doesn't help as much in other scenarios to see what catches a user's Eye or where they go if they leave our application as well as an actual in-person interview would help with eye-tracking software.

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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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Quantum Metric is a true professional, and I love the level of insight and industry knowledge they bring to the table. We use it at the departmental level, including marketing, customer service, and IT. Session replay allows our data consumers to derive insights faster and easier than digging through data. It lets us see or understand how users feel and work to enhance those feelings. The quality of support and the time to respond are also noteworthy. They have great coverage, but the learning curve is very steep and requires a lot of technical support and hand-holding.

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• Tracks users' online experiences from start to finish.
• Makes it easy to share a customer's experiences with the rest of my team.
• Allows me to use different methods to find evidence of a customer's experience.
• Shows page insights via segmentation that help me know where to improve my site.
• Tracks error clicks and dead clicks for quick insights into site improvements.
• Integrates with HelpScout for easy access to customer problem-solving.

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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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• Identifying user pain points and frustrations. Quantum Metrics has a data point called Rage Click which shows when a customer has clicked multiple times back to back on a particular section of the website.
• Replaying a session to see everything that is loading on the front end to the customer, as well as the backed end of the website, has been critical in troubleshooting the experience.
• Heatmaps are a awesome tool we have found very useful in showing engagement with different content on the page, how far user scroll & drop off and to see a split side by side view of the same page in an a/b test.

• The ramp-up time to learn the entire product can take a little while. There are just so many absolutely wonderful tools and different ways to look at the same type of information that it can take a new user a few weeks to understand and then more time to master. However, FullStory has a ton of training resources to help with that! A lot of my own teammates have taught themselves FullStory through those training resources.
• The UX of the product is totally wonderful, but there are tiny things that make getting to certain parts of the product slightly more challenging (like clicking into a modal in a modal). Sometimes buttons or links are placed out of my field of view, but those are minor things I notice because I work in UX.

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• JavaScript is hard to implement sometimes especially for JQuery elements
• ROI reporting should be part of the overall experimentation reporting
• CMP integration: where we can easily show status of test on the HPT request
• CMS integration
• More widgets like social proof banners, etc.

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• Quantum could share some use cases implemented by similar industries in accordance with the key problems as inputs.
• We should be able to see some of the most common solutions that could be integrated within our product based on the step of the user journey I am at.
• Expanding capabilities to integrate with high-level tools integrated.

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It's been a phenomenal tool for us; every department that uses it has found something new and unexpected that it can do that they're really excited about. Even if we *only* used it for bug triage, it would be worth our time and money. The fact that we can use it for so many other things as well--gauging how customers interact and use our site, identifying UI problems, etc.--is above and beyond

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I rated this question because at this stage, Optimizely does most everything we need so I don't foresee a need to migrate to a new tool. We have the infrastructure already in place and it is a sizeable lift to pivot to another tool with no guarantee that it will work as good or even better than Optimizely

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Quantum is a nice tool and is user friendly however I believe there always room for improvement. We have experienced minor issues with a few sessions which were solved by Quantum support reps in a timely manner and some of the dashboards are not as robust as other tools we use

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Because everything seems to be easy to find and it doesn't take long at all to learn how to use the product

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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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For a new user, it's pretty intuitive to onboard and start doing the basic functionalities. But QM has a lot of functionalities which can be leveraged by more team members (especially when you don't have analysts dedicatedly using this) if further enhancements to usability are made.

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We've never had issues with availability

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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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It seems the performance is really good and always loads quickly

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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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They have been overall pretty responsive and proactive. The tool is pretty straightforward to use. Most of the questions we have had to work with them on our how to use new features or adjust our integration to ensure we are gathering all the data from all of our tools.

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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.

I've been very impressed with the support Quantum Metric has provided. Our amazing Customer Success team has provided excellent service and has gone above and beyond in helping us use and understand the tool. We hold weekly calls with multiple teams and QM has been proactive in bringing things to our team's attention and making suggestions. The support has been one of the most important aspects of having QM and has allowed us to make great strides in improving how we use data and user research in our work.

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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).

To successfully implement FullStory, it is important to plan your implementation carefully by defining your goals and user segments. You should also use tags and filters to analyze your user data, train your team to use FullStory effectively, and use FullStory in conjunction with other analytics tools to get a more complete picture of your user's behavior. By following these tips, you can gain valuable insights into your users' behavior and experiences, and use this information to improve your website or application.

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In retrospect: - I think I should have stressed more demo's / workshopping with the Optimizely team at the start. I felt too confident during demo stages, and when came time to actually start, I was a bit lost. (The answer is likely I should have had them on-hand for our first install.. they offered but I thought I was OK.) - Really getting an understanding / asking them prior to install of how to make it really work for checkout pages / one that uses dynamic content or user interaction to determine what the UI does. Could have saved some time by addressing this at the beginning, as some things we needed to create on our site for Optimizely to "use" as a trigger for the variation test. - Having a number of planned/hoped-for tests already in-hand before working with Optimizely team. Sharing those thoughts with them would likely have started conversations on additional things we needed to do to make them work (rather than figuring that out during the actual builds). Since I had development time available, I could have added more things to the baseline installation since my developers were already "looking under the hood" of the site.

Smartlook is software that records users on a website
and mobile app. Finding useful information within thousands of recordings is
made quick and easy with features that help you accomplish this. I have chosen FullStory because it is easier to use and has more advanced features compared to Smartlook.

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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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We have used - as an organization - multiple products that each fill a roll or task Quantum Metric provides...however I think there are very few tools or SaaS solutions out there that bundle so much into one solution. QM was better than the replay tool another group was utilizing (Mouseflow) because with our contract we could capture and review way more replays as well as have those replays married to actual, quantifiable data. From an analytics point, is so much easier to install event tracking as opposed to our basic Google Analytics implementation. However, I would still use GA as a primary record for measuring overall site performance since QM doesn't have robust product sales tracking. At one point we did review a competitor called Content Square. They seemed very focused on heat mapping.

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Full story was easy to scale with the growth of our teams and organisation

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We can use it flexibly across lines of business and have it in use across two departments. We have different use cases and slightly different outcomes, but can unify our results based on impact to the bottom line. Finally, we can generate value from anywhere in the org for any stakeholders as needed.

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• Triage for front end bugs - FullStory can sometimes help you spot a bug in action much more quickly than trying to recreate it on your own
• Observing customers use new features and understanding why a new feature may be performing better or worse than expected

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• We're able to share definitive annualized revenue projections with our team, showing what would happen if we put a test into Production
• Showing the results of a test on a new page or feature prior to full implementation on a site saves developer time (if a test proves the new element doesn't deliver a significant improvement.
• Making a change via the WYSIWYG interface allows us to see multiple changes without developer intervention.

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• The service is not cheap, so don't commit unless you're going to use it.
• Really improved our tech support process by allowing us to identify problems much sooner.
• Helped identify potential frustrations, which could be eliminated to reduce friction at checkout.

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