Kissmetrics vs. TensorFlow

[Kissmetrics is well suited for the] abandon cart scenario to re-engage users on the purchase journey. Engaging users to personalized content using the visit metrics derived from the data captured at each digital touch points. [Implementing] website campaign and journey orchestration is easy. You get visitor profile to segment upon using different visit metrics and action.

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TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).

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• The more events you track and properties you send along, the more you can see how specific users use your product/service. The user based timeline gives you a perfect start point to get in touch with users, because you can see where they get stuck.
• Tracking your traffic sources and how they influence conversions is awesome. You can get a perfect view of how much a traffic source contributes to revenue.
• Funnel reports give you more insights into micro and macro conversion steps and give you actionable data to work with.
• Customer support!

• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

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• Installing.... yes this is also a negative. While you can install and have the program running in minutes, if you use Unbounce, the form tracking process is quite complicated!
• Updates... I feel like the product updates have slowed a lot lately. Thankfully, the product functionality is so amazing that it hasn't impeded the use of it. However, it is still disappointing to see less frequent software updates.
• Occasionally clunky UI... there are a few reports that are really easy to mess up and leave you scratching your head on why it isn't showing you any data.

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• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.

Price sensitivity and the different choices that now exist in the Analytics industry. I think it makes sense for us sometime this year to rethink our analytics strategy to see how we may leverage the best of GA (which has included lots of new features and updates the past years_, Kiss and other tools as need be

Right after login, you'll get to a dashboard which shows you a quick view on how your business is doing across the events you are tracking. There is no need to dig deeper than that unless you want to, in which case it's very easy to do so just by clicking on the metric on which you want more information. The interface is very intuitive.

Support of multiple components and ease of development.

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The application was very rarely down; during the period we used the application, I can think of only two or three occasions in which the site was down. Notably, at no time was the the performance of our own site compromised as a result.

Speed improved dramatically as the service matured. Early iterations of the publicly-released application would occasionally provide slow processing of results, but those delays became much rarer occurrences during the last year that we used KISSmetrics. One of the more impressive views (which started out feeling more like a toy) is the live view of visits. Knowing that you could see, in real time, what events a user triggered, was gratifying and instructive.

Our front-line product support person (Mika) is great. She is responsive and great to work with.

However, the data accuracy issue described earlier is the reason for the low score here. This issue was escalated from front-line to support to level 2 technical support and then it disappeared into a black hole. Escalations, in general, do not go well. We get no response for days, or I have to chase things down. This is not acceptable. Marketing metrics are critically important to me and I need answers quickly. I cannot afford to wait around for days / weeks for a response.

Just to be clear, these comments only apply to escalated support issues.

Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.

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Again, we were fortunate to work with KISSmetrics as they built their application, but Hiten, their CEO and founder, was incredibly helpful to me personally, and to our metrics-driven business as a whole, as we adopted their tool.

I loved this aspect of the product. It wasn't just that the documentation and online tutorials are great - which they are - the on-boarding process though was really stellar. Once you have set everything up, you get a welcome message followed by a step-by-step guide to get you started that is built right into the product interface. For example, the UI asks you to first do X, and then copy this code snippet and send it to your developer who will know what to do with it. When you come back after the first interaction with the product, it continues the process by explaining right in the UI how to track events etc. This kind of step-by-step approach is incredibly efficient. Although there are various forms of supporting documentation (PDFs videos etc) to support every step, you don't really need them. This approach means that you are up and running very quickly with virtually no training time or documentation consultation. Highly efficient process.

In order to build trackability down to revenue, there was quite a lot of work to integrate Kissmetrics with our software and internal process. We had to build the hooks so that Kissmetrics could call back into our software and billing system, etc.. However, we didn't need additional expertise to do this. Once you understand the API, and you own systems, making it work is not too difficult. We did not require an outside consultant or anything like that

Use of cloud for better execution power is recommended.

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Kissmetrics is a next-level step up for people who are used to getting their tracking and reporting from Google Analytics or Shopify's CMS. While HubSpot arguably has a better user interface, Kissmetrics certainly has the power and usability necessary to track important conversion data and help you make better marketing decisions.

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Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice

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• Unfortunately for this client (small business) the cost of Kissmetrics was just too prohibitive. But it's obvious that for a larger company that can afford it, the data would be invaluable to gain more insight in how to gain more active users and orders for a funnel.
• The data provided really increases an understanding of how best to provide the right experience for the users...happy users equals increase in ROI.

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• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

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