IBM Watson Studio on Cloud Pak for Data vs. Mixpanel

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.

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As a worker in the sales area, I see closely how complex it can be to evaluate the commercial funnel and Mixpanel has been an indispensable guide to prioritize above all what customers expect to receive from our company, and thus be able to determine the main service we offer. Without a doubt, Mixpanel has special functions to be the one that guides the route and marks the objectives much more clearly.

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• 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.

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• Mixpanel is a daily use application for everyone in my organization; it helps us have a better flow of information and interaction between work teams.
• The user interface of this platform is simple and has a wide variety of functions and resources to help us work in the most organized way, have better team coordination, and keep efficiency high.
• I love that it is so easy to program our calendar to our liking, so we can prioritize our activities and know what is pending, and the best thing is that I can update the calendar if necessary.
• The chat function is great to improve the interaction between colleagues and share work schedules and any information with third parties.

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• 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

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• Mixpanel requires an explicit setting of events from your app. This means you need to be very thoughtful in the design of your events because missing one means you aren't collecting any data from it. Inserting it into the process later on then brings challenges in tracking when certain events came online.
• A tool like Mixpanel comes packed with features that sometimes are harder to discover. It's very easy to get sucked into one part of its toolset and not be aware of other tools which may be very useful.

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

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It's not an all encompassing solution like Google Analytics tries to be, but MixPanel offers much easier to use and understandable data insights. That's valuable when juggling many responsibilities as startup life demands, so a renewal would be easily justified.

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

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Relativity easy to use. Once you get the hang of it, very easy to create dashboards for different use cases. I split my dashboards between customers or use cases

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

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Never had slow response even on our very busy network

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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

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We have only ever had to use their support once, when we were setting up the account, but their responses were prompt and the solutions were well documented. The people who solved our issues were helpful, even to non-tech people.

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

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

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Mixpanel has a great resource about their product, with videos on how to use it and real world examples from other companies on how they integrate Mixpanel into their business processes.

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

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Again, somewhat annoying to be charged based on data points when many other analytics providers have one flat fee. Implementation was good, but I might have tracked a few more detailed points if I had the option.

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.

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• There was not really any other competitors for this specific use case that had a chance
• We used Google Analytics for some things but obviously, GA is anonymous data so not useful for product teams with users
• Other solutions out there seemed to be too expensive and this was the most recommended
• Heap seemed to be the best OTHER option but didn't take the time to evaluate

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

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• 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

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• We've been able to increase the funnel conversions of one of our new product funnels from a 1% conversion rate to a 5% conversion rate.
• We've been able to increase the CTR on another of our main product pages from ~3% to ~10% (so far)
• We've been able to segment out how users from different traffic sources behave, allowing us to eliminate thousands of dollars of wasteful spending on advertising campaigns that weren't working.

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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