Chattermill vs. IBM Watson Studio on Cloud Pak for Data

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

Incentivized

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

Incentivized

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

Incentivized

• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

Incentivized

• More report types

• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

Incentivized

because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

Incentivized

Super easy to use and very intuitive

Incentivized

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

Incentivized

From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

Incentivized

Never had slow response even on our very busy network

Incentivized

I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

Incentivized

The trainers on the job are very smart with solutions and very able in teaching

Incentivized

The Platform is very handy and suggests further steps according my previous interests

Incentivized

It surprised us with unpredictable case of use and brand new points of view

Incentivized

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

Incentivized

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

Incentivized

It helped us in getting from 0 to DSX without getting lost

Incentivized

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

Incentivized

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

Incentivized