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…
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.
What Tweepi is not is a posting or content management tool. It does have some ability and makes recommendations for a bit of engagement but that is it. If you are looking for a posting tool this is not it. However, if you are looking to manage the followers of a Twitter account then this is the tool to use. A great example is a campaign that we are running right now for a news organization. Content can not be pre-planned as it comes in real-time so having a posting tool is not needed here. This client first came to us in Nov., their site is a UX/UI nightmare, they had very little traffic, there was little that we could do with the existing site other than some minor design changes. We wanted to give the client a quick win even if it was a small one. The only social media account that this company had was Twitter. So we began a campaign using Tweepi to grow their followers and help the SEO with a bit of social engagement as well as try and drive a bit of traffic to their website.
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.
Filtering is without a doubt the greatest strength of Tweepi. In just a few seconds, I can filter through thousand of users I am following and who are following me to find and identify those I should follow or unfollow based on many different and sometimes multiple criteria.
Speed - it is really fast to sort and filter through so many users connected to me through Twitter.
Find and filter other users friends and followers, and apply some or all of the aforementioned criteria.
The number of criteria. I can search followers more or less than X number of followers, find those inactive X number of days, those who haven't completed the profile or uploaded an avatar, and combine all this data filter thousands down to just one.
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 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
I am not sure if I would consider this a "Con" but it is a limitation. When you are on the Platinum Level Tweepi has recommended follows each day when you log in. However, that number is under the Twitter daily follow limit. Tweepi will give you 250 when the limit for an unverified account is 400 and a verified account 1000. We are sure this limit is set to help protect twitter accounts.
Other than the limitation mentioned above we frankly can not seem to find anything else. There is just nothing bad to say.
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
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
It's very quick, easy to learn and intuitive, and it doesn't require much of a learning curve. I think anyone who has sorted data in a spreadsheet will be very comfortable managing filtering and optimising their Twitter account with Tweepi.
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.
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
Tweepi's support is quite good. They are fast to answer any questions that you have. I have never had a problem with Tweepi but there have been a few billing questions that were taken care of just fine. The only other times I have contacted support have been more in the area of the tools best practices and I have always been happy with the response.
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'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
I've not used similar products to any great extent. All I can say is that Tweepi does an exceptional job of the very specific task of filtering friends and followers of a Twitter account, and optimising that Twitter account to get the best value for the user.
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 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
[Excellent] results can be had with just this simple tool.
The cost of Tweepi is very reasonable and along with the time savings it is a good return on the investment.
A positive that I think should be mentioned is that we have fewer issues when using Tweepi than we do following within Twitter manually. The Tweepi limits do provide a safety net for the account.
We have not seen any negative impact with the use of Tweepi.
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
