Founded in Switzerland in 1997, Magnolia is a CMS used to build composable digital experiences. Magnolia helps create fully integrated customer experiences and speeds up digital delivery of content. Magnolia boasts 480 enterprise customers, thousands of Community Edition deployments, and more than 200 certified Magnolia Partners around the world. They further state that their enterprise customers include Sanofi, Generali, the Atlassian, The New York Times, Harley Davidson, and Union…
$3,500
per month
Optimizely Web Experimentation
Score8.7 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
Magnolia
Optimizely Web Experimentation
Editions & Modules
DX Core
$3500
per month
DX Cloud
$6000
per month
No answers on this topic
Offerings
Pricing Offerings
Magnolia
Optimizely Web Experimentation
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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More Pricing Information
Community Pulse
Magnolia
Optimizely Web Experimentation
Considered Both Products
Magnolia
Verified User
Chose Magnolia
Magnolia is in a league of it's own vs the other platforms I have previously used. Rather than being a turnkey solution Magnolia puts the power into the hands of your company and developers allowing you to build anything you can imagine. Being a DXP rather than a CMS Magnolia …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Magnolia is a very capable DXP, that provides client with lots of flexibility in composing its own stack. While the core of the platform is a content management system, the open architecture of Magnolia DXP allows it to connect to any platform, allowing client to extend the capabilities. One scenario would be a centralized content hub - where through a single platform, content authors can choose which channel to distribute what content. For example, long form content for consumers viewing on a laptop, short form content for those using a mobile browser. This allow the client to personalized the experience based on channels. Another scenarios would be leveraging on GenAI - using Magnolia's built-in connector to ChatGPT. If that is not the service that one desire, you can always connect to another AI service such as Google Gemini. With GenAI, connected, content author can use AI as co-pilot to help them scale up their content production.
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Speed of development - time to delivery from zero to MVP was excellent
Ease of use - the authoring experience is very easy to build and train
PAAS/SAAS - the managed service platform removed the traditional overhead of running in-house technologies, meaning we could focus on value add, with less time spent keeping the lights on.
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
The documentation provides samples that are often out of context, and difficult to know where the provided example code should be implemented. More tutorials providing the full project or step-by-step instructions on how to implement subject material would help greatly. Baeldung is a resource I would consider the gold standard in how this is done in other spaces.
The use of JCR and Nodes makes object serialization/deserialization painful. Jackson compatibility or similar would be a welcome enhancement to the developer experience. Maybe leveraging code-gen from light modules to build model classes when possible could help accomplish this.
Modifying the home layout from light modules is frustrating. It seems that any configuration overrides made merge with the default rather than overwriting, which makes for a difficult combination of guess-and-check while referencing the documentation to see what should be in each row/column when making changes.
Including "mark all as read" or "delete all" in the notifications app would be a great quality of life improvement. It seems that by default, users have to individually select messages and operate them.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
We've shown it to a number of users both clients and our own team and despite initial apprehensions, they "get it" very quickly. It's intuitive and friendly and quick to perform daily tasks. We once had a client tell us "Using Magnolia makes me smile" which says it all for us.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
I gave [it] 7/10 only because of the loading time of pages. Otherwise, I think it deserves an 8. Normally this is not an issue per [se] but considering the rating matrix and as I have been asked to honestly write about it. Yes, the page loading times could be improved.
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
You always get an answer based on your SLA. But you always get a solution. That's the successfactor in this case. To often i was frustrated about people in a company without even a clue what there product is about or how to solve a problem. Magnolia's Support Team does a very good job and try to help you in most of the cases
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.
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).
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.
I've used several CMSs like AEM and EpiServer, and comparatively, they all excel at different things. Magnolia is the best to develop for/against. Episerver has the best/most fluid UI in terms of content editing, and the overall admin experience AEM is just all around sucks.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
Magnolia has brought about positive impacts. For instance, we need not outsource web design and marketing services because thanks to this software, we can handle most work inhouse
The software is affordable with no compromises on capabilities and therefore it is gives us value for money.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info