Chattermill vs. IBM Watson Studio on Cloud Pak for Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Chattermill
Score 9.0 out of 10
N/A
The Chattermill Unified Customer Intelligence Platform helps businesses understand their customer reality. Using Chattermill, companies can unify their customer feedback data across reviews, support tickets, conversations, and social media to uncover what customers want, need, and expect from their products and services. Chattermill unifies customer feedback, customer support, and product feedback, and uses deep learning artificial intelligence (AI) to analyse customer data at…N/A
IBM Watson Studio
Score 10.0 out of 10
N/A
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.N/A
Pricing
ChattermillIBM Watson Studio on Cloud Pak for Data
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
ChattermillIBM Watson Studio
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
ChattermillIBM Watson Studio on Cloud Pak for Data
Considered Both Products
Chattermill
Chose Chattermill
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. …
Chose Chattermill
way more user friendly than others, which is one of the main reasons we went with Chattermill. We needed a tool that would take less time than pulling and analysing data, and chattermill was the top dog by far
IBM Watson Studio
Chose IBM Watson Studio
Google Cloud may be a good place but it is not as easy to understand as IBM Watson is. Google Cloud has a lot of things and it is terrifying for a beginner. You need hours of specialization for that. On other hand, anyone can start using IBM Waston just by the following …
Chose IBM Watson Studio
  • Data ingestion
  • Batch data processing
  • Built-in connectors to Python
Chose IBM Watson Studio
Anaconda and Jupyter Notebook
Chose IBM Watson Studio
AWS Sagemaker is a well-established product that supports on-demand notebooks, data pipelines, and so on, however, it also comes with the learning overhead of the whole AWS stack. It does allow per-defined models, but the benefit of using IBM Watson Studio is that users are …
Chose IBM Watson Studio
Organization of data, use of data, manage the data, visualize the data is easy. Use of the environment for any project. We can use python or R or Scala in the notebook.
Chose IBM Watson Studio
It provides better user experience. All your data on cloud and does not take up space locally.
Chose IBM Watson Studio
I think they are very similar but IBM Watson is not good enough yet to pay for the services that I can already get from Jupyter Notebook.
Chose IBM Watson Studio
Easy to use, but still requires a lot of coding to use. There is no ranking of models used and models are not persistent, which means you have to keep running the models again every time you leave the session. The filesystem is clunky and need to keep authorizing Google Drive …
Chose IBM Watson Studio
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 …
Chose IBM Watson Studio
IBM offers a deep neural network training workflow, with a flow editor interface similar to the one used in Azure ML Studio. However, the custom build modeling in IBM has notebooks such as Jupiter to program models manually using popular frameworks like TensorFlow, …
Chose IBM Watson Studio
As it offers more features and can be used for several applications like AI,ML,DS etc.,
Chose IBM Watson Studio
With my experience on Jupyter Notebook I think both are good and currently more comfortable with Watson Studio product. With Jupyter it's open source (free) is always good. "Lots of languages (50), data visualization with Seaborn, work with the building blocks in a flexible and …
Chose IBM Watson Studio
We didn’t evaluate other products but we liked what we saw in Watson Studio.
Chose IBM Watson Studio
As an IBM Business Partner, we are financially incentivized to recommend and deploy IBM solutions where it makes sense to do so for the customer. Against other solutions, few have the governance and security that IBM offers, which is essential for any kind of work in highly …
Chose IBM Watson Studio
They are close, but I feel Alteryx is more of an enhanced Jupyter capability, whereas WS is more of an enterprise solution for multiple teams
Chose IBM Watson Studio
I am excited with the roadmap of Watson Studio incorporating SPSS Modeler in the offerings.
Chose IBM Watson Studio
Watson Studio was our choice in data management because its "all-in-one" packaging. Watson studio also stood out to us because it was more affordable and free for our organization to try out. We also greatly value the open source ecosystem Watson Studio has fostered.
Chose IBM Watson Studio
AWS Sagemaker is new, and I personally think it's better than sliced bread. There's very little set up to do. Watson Studio needs to up its game against Sagemaker.
Chose IBM Watson Studio
AWS stacks up very favourably against Watson Studio, and in fact this is what the customer ultimately chose over Watson Studio after an evaluation period due to the sophistication, maturity, security, and capabilities of the AWS components. The downsides of AWS are having to …
Chose IBM Watson Studio
The learning curve for DSX is smaller compared to other tools. The data science user base often has preferred tools that they have used previously which are often not DSX which makes adoption of DSX by trained data scientists harder than new users.
Chose IBM Watson Studio
IBM DSx is more comprehensive and easy to use, IBM Data science experience has many connectors to the data source and guarantees the portability with your old projects.
Features
ChattermillIBM Watson Studio on Cloud Pak for Data
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Chattermill
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.1
Ratings
3% below category average
Connect to Multiple Data Sources00 Ratings8.00 Ratings
Extend Existing Data Sources00 Ratings8.00 Ratings
Automatic Data Format Detection00 Ratings10.00 Ratings
MDM Integration00 Ratings6.40 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Chattermill
-
Ratings
IBM Watson Studio on Cloud Pak for Data
10.0
Ratings
18% above category average
Visualization00 Ratings10.00 Ratings
Interactive Data Analysis00 Ratings10.00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Chattermill
-
Ratings
IBM Watson Studio on Cloud Pak for Data
9.5
Ratings
15% above category average
Interactive Data Cleaning and Enrichment00 Ratings10.00 Ratings
Data Transformations00 Ratings10.00 Ratings
Data Encryption00 Ratings8.00 Ratings
Built-in Processors00 Ratings10.00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Chattermill
-
Ratings
IBM Watson Studio on Cloud Pak for Data
9.5
Ratings
13% above category average
Multiple Model Development Languages and Tools00 Ratings10.00 Ratings
Automated Machine Learning00 Ratings10.00 Ratings
Single platform for multiple model development00 Ratings10.00 Ratings
Self-Service Model Delivery00 Ratings8.00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Chattermill
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.0
Ratings
6% below category average
Flexible Model Publishing Options00 Ratings9.00 Ratings
Security, Governance, and Cost Controls00 Ratings7.00 Ratings
Best Alternatives
ChattermillIBM Watson Studio on Cloud Pak for Data
Small Businesses
IBM Watson Studio
IBM Watson Studio
Score 10.0 out of 10
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10
Medium-sized Companies
PG Forsta HX Platform
PG Forsta HX Platform
Score 8.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
PG Forsta HX Platform
PG Forsta HX Platform
Score 8.0 out of 10
Posit
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
ChattermillIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
8.8
(0 ratings)
8.0
(0 ratings)
Likelihood to Renew
-
(0 ratings)
8.2
(0 ratings)
Usability
9.1
(0 ratings)
9.6
(0 ratings)
Availability
-
(0 ratings)
8.2
(0 ratings)
Performance
-
(0 ratings)
8.2
(0 ratings)
Support Rating
-
(0 ratings)
8.2
(0 ratings)
In-Person Training
-
(0 ratings)
8.2
(0 ratings)
Online Training
-
(0 ratings)
8.2
(0 ratings)
Implementation Rating
-
(0 ratings)
7.3
(0 ratings)
Product Scalability
-
(0 ratings)
8.2
(0 ratings)
Vendor post-sale
-
(0 ratings)
7.3
(0 ratings)
Vendor pre-sale
-
(0 ratings)
8.2
(0 ratings)
User Testimonials
ChattermillIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
Understanding the nuances of sentiment completely - not just in a black and white positive/negative way. Chattermill helps us find out insight that would only be possible through manual tagging, pulling data and hours of analysis. It's cut my data time in half while also helping our team show the work we've done with automated reports very easily
Read full review
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.
Read full review
Pros
  • feedback management
  • integration (Slack, Zendesk)
  • analysis (NLP and sentiment analysis is my favourite)
  • great support
Read full review
  • 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.
Read full review
Cons
  • Reports do not automatically save when navigating to different pages
Read full review
  • 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
Read full review
Likelihood to Renew
No answers on this topic
because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
Read full review
Usability
Super easy to use and very intuitive
Read full review
The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
Read full review
Reliability and Availability
No answers on this topic
From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
Read full review
Performance
No answers on this topic
Never had slow response even on our very busy network
Read full review
Support Rating
No answers on this topic
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
Read full review
In-Person Training
No answers on this topic
The trainers on the job are very smart with solutions and very able in teaching
Read full review
Online Training
No answers on this topic
The Platform is very handy and suggests further steps according my previous interests
Read full review
Implementation Rating
No answers on this topic
It surprised us with unpredictable case of use and brand new points of view
Read full review
Alternatives Considered
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
Read full review
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.
Read full review
Scalability
No answers on this topic
It helped us in getting from 0 to DSX without getting lost
Read full review
Return on Investment
  • Chattermill has transformed the way we look at customer satisfaction
  • Chattermill has helped us reduce churn
Read full review
  • 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
Read full review
ScreenShots

Chattermill Screenshots

Screenshot of Zendesk support tickets, customer surveys, and Google Play reviews all contain valuable feedback, product improvement ideas, and customer requests. They can be consolidated in one place using Chattermill.Screenshot of A dashboard can be created in Chattermill that includes important metrics, charts, and tables for different areas of your business – CX, support, product, retail stores, – providing a shared workspace to fuel collaboration.Screenshot of Zendesk, App Store, or Typeform can be connected to Chattermill, so CX, support, and product teams can have a unified view of the voice of the customer. The platform integrates with over 50+ different sources of customer feedback – think online reviews, support tickets, customer surveys, and chat messages.Screenshot of It takes seconds to create a team dashboard that includes important metrics, charts, and tables. These can be added to a custom dashboards with a few clicks.Screenshot of Customer insights with dedicated CX metrics and various segmentation options help understand the hidden patterns and get to the root of problems and opportunities.