HubSpot Marketing Hub vs. TensorFlow

As I said previously, I was not a fan at the beginning because, like everyone, I don't take change so well. Once I started following leads through HubSpot Marketing Hub, I integrated it with my email and created templates for what I needed, I started to see the positives immediately. My time working was better spent as I eliminated time put in routine tasks. I was able to track all communication in HubSpot Marketing Hub, we created systems in the office assigning tasks to one another and, through HubSpot Marketing Hub, our leads were able to book meetings in my calendar automatically. To be honest, if you are in sales and marketing, I cannot think of scenarios where HubSpot Marketing Hub cannot help. All I would like to say is, when using templates, pay attention to where you send them - you may need some personalization. I would encourage the management of HubSpot Marketing Hub to create short tutorials for beginners like me who need to configure and start using various features: setting up deals, templates, and dashboards.

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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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• All-in-one software - the main thing HubSpot does well is the breadth of functionality.
• Easy of use - personally, I find it more intuitive than Marketo, which is the other MAP that I know well.
• Ads - I like that we can drill down on ads by channel, ad group, campaign, and even create and see performance data at each level.

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• 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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• While Hubspot has added some collaborative tools, I think this could improve. I'd like to see more options to comment on emails and ways of being able to share out changes as well as approval channels.
• The ability to update ads via the calendar is pretty cumbersome. There's no good way to bulk edit or update. You have go into each one individually.
• Real time edits would be nice. If you have multiple people working on the same thing, you'll get kicked out and your changes might not be saved.

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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.

Our business relies on the HubSpot platform to manage our marketing, sales and CRM processes. HubSpot marketing automation helps us define our activities and streamlines them in a cohesive and efficient way. Without HubSpot, we will have to revert to the 'old way' of doing everything with a variety of disparate systems.

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While there are some frustrating things that pop up unexpectedly ("wait... I can't do X?"), I have found HubSpot to be easy to use and extremely helpful to my daily work. The documentation is really good, and when it's not helpful, the support staff have been amazing.

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Support of multiple components and ease of development.

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They have had issues with system availability over the course of days. Sometimes the system is unusable, other times updates simply take a long time to show up. It's better now but, from a reliaibility standpoint, HubSpot is not Salesforce.com yet. Still great software though.

With all the new features in HubSpot, the system can get a tad slow sometimes... That said, most of the time it is lightning fast and I have no problems. Because most of the integrations are API, they silently work in the background. I have not had trouble with lag due to HubSpot integration

During the few times I've needed it, HS support has been accessible, helpful and efficient. Often rolling up their sleeves to make changes for you as opposed to leaving you with a list of instructions to decipher on your own.

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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I did the 2-day classroom at HubSpot's corporate office in Cambridge. First off, it was amazing to see their corporate office in general. They have such a cool office environment. But it was also great to have the ability to learn in a workshop format with other HubSpot users and meet my Account Manager/ Inbound Marketing Consultant in person.

I went through Inbound Marketing University in 2006. Great training and helped my transition from traditional (outbound) marketing to inbound marketing that I've been able to apply to a number of businesses from wastewater and water reuse, to professional services and SaaS. Share information of value to build awareness and trust. Answer customers' questions in a transparent way to generate more qualified leads. Understand the difference between a marketing qualified lead and a sales qualified lead and put together a lead nurturing program. Your sales and marketing efforts will see significant ROI.

My biggest piece of advice for those who are implementing Hubspot is that you need to devote the time up front and learn how to use the product. Once you learn how to use Hubspot, it will be much more effective as well as much easier to use in the long run

Use of cloud for better execution power is recommended.

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Other competing software such as Zoho and Boomtown may have more bells and whistles, but it is too cumbersome and has many parts that only advanced users can operate. With HubSpot, each function is within reach of the average agent. It doesn't overpromise and then makes you feel incompetent when you can't use it all...

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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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HubSpot is addressing this more and more. Currently you can assign tasks to designated sales teams, which grow as you grow. They've added free baseline products for those just getting started. These and more contribute to the scalability of HubSpot - so I gave it an 8 and am hoping for more in the future!

• HubSpot has helped us increased our lead generation.
• HubSpot has allowed us to continue to grow our customer base without having to spend a ton of marketing dollars on advertising.
• HubSpot has allowed our company to standardize our process and bring the company onto one platform for sales and marketing.

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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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