Jupyter Notebook vs. Mixpanel

I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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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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• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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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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• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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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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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.

Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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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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I haven't had a need to contact support. However, all required help is out there in public forums.

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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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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.

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.

With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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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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• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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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