Chattermill vs. H2O.ai

Think about large scale ecommerce store (with thousands customers, and hundreds of orders every week). If you want to manage a large scale customer's feedback - this tool is for you. It is too complex for small projects, but for large one it is perfect. Has some minor issues, but in general it will help you to draw a Customer Journey Map, find "hot spots" and implement solutions. Great visualization (in reports) is a huge benefit of Chattermill (and sentiment analysis of course). Thanks to connection between customer support system (like ticketing system) it will allow you to manage support/returns/warranties feedback also. I dont know how it works for other businesses but for large scaled ecommerce it is perfect

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Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.

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• collect customer feedback
• automate feedback reports
• pulling insights on customer data

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• Excellent analytical and prediction tool
• In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
• Readily available algorithms, easy to use in your analytical projects
• Faster than Python scikit learn (in machine learning supervised learning area)
• It can be accessed (run) from Python, not only JAVA etc.
• Well documented and suitable for fast training or self studying
• In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
• It can be used as open source

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• More report types

• Better documentation
• Improve the Visual presentations including charting etc

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Super easy to use and very intuitive

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The overall experience I have with H2O is really awesome, even with its cost effectiveness.

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Chattermill is more complex, easier to integrate between systems that we used (ticketing, livechat, reporting, CRM). Very informative reports (that will reveal what your customers really think). Custom reports creation is a benefit too - it is easier than in other systems. Automatic categorization of feedback is a helpful tool that do big part of job for you

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Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.

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• Helping reduce churn
• easily 10x return on investment in happy customers
• product ideas and updates that otherwise would not have been surfaced

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• Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
• Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
• Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product

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Independently Invited by TrustRadius

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