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Machine Learning for Marketers Made Simple with MonkeyLearn
Blog Feature

By: Paul Roetzer

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June 8th, 2017

Machine Learning for Marketers Made Simple with MonkeyLearn

What if every marketing and sales email you received was automatically prioritized by importance the moment it hit your inbox? Or your next survey or study on customer behavior came pre-loaded with the most valuable insights already pulled out, so you didn’t have to read through every response or data point?

That’s where MonkeyLearn (@monkeylearn), an artificial intelligence platform, comes in. MonkeyLearn automates marketing and sales workflows and analyzes data using the power ofScreen Shot 2017-06-02 at 9.28.24 AM.pngAI technologies that include machine learning, deep learning and natural language processing (NLP).

The result is an AI-powered marketing and sales workflow that saves times and money, freeing up professionals to perform higher-value tasks. We spoke with MonkeyLearn CEO Raul Garreta (@raulgarreta) to learn more. 

In a single sentence or statement, describe MonkeyLearn?

We help companies to automatically process text data to automate business workflows, get business insights or implement natural language processing (NLP) features into their products.  

How does MonkeyLearn use artificial intelligence (i.e. machine learning, natural language generation, natural language processing, deep learning, etc.)?

MonkeyLearn uses machine learning to allow users to build customized text-analysis models. We abstract the complexity of machine learning and NLP with a beautiful graphical user interface, API and third-party platform integrations. This way, we allow developers without background in machine learning and even non-technical users (e.g. marketers and sales teams) to create effective text-analysis solutions in a very short time. 

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What do you see as the limitations of artificial intelligence as it exists today?

I think AI is at an inflection point due to the new advances in deep learning that have achieved in areas like computer vision and language processing. But still there are some kind of AI problems where we need more research in order to solve them. Chatbots are one example. I think there's a lot of hype around this area and significant research is needed in order to solve it effectively. We don't believe that the approach now should be to completely automate human interactions with machines, but instead empower or augment human capabilities with machines. 

What do you see as the future potential of artificial intelligence in marketing and sales?

Initially we started MonkeyLearn with the vision to empower developers. But we have seen also great interest from marketing and sales teams that want to use NLP technologies to automate and improve their workflows. We believe that by making NLP accessible for non-technical users we can make huge impact in these areas. When technologies are democratized and made massively accessible, that's when you see exponential growth and improvements. We see lots of potential in automating manual, non-scalable and time-consuming processes in marketing and sales workflows.

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What makes MonkeyLearn different than competing or traditional solutions?

MonkeyLearn is different from the other providers as it allows users to build customized text-analysis models by leveraging machine learning technologies. The user can for example customize the categories of a text classifier or use their own data to train a machine learning model in a couple of minutes.

At the end of the day this means much more accurate results than the traditional solutions with minimum effort. We are also making a further step forward democratization by creating integrations with marketing, sales, customer service and data analysis tools in order to allow non-technical users to easily incorporate NLP and machine learning technologies into their workflows without the need of coding. 

Who are your prototype customers in terms of company size and industries?

We initially focus on English-speaking markets and companies with at least $1 million in revenue. The reason is that usually these are the markets where you find early adopters with the desire and resources needed to invest in new technologies. But MonkeyLearn is designed as a global tool and provides support for other languages like Spanish, French, Portuguese and more. In terms of industries, our top customers come from the software services industry, business intelligence and social media. 

What are the primary use cases of MonkeyLearn for marketers and sales professionals?

The main goal is to automate manual workflows, and empower marketers and sales professionals to do more with less. There are many steps in marketing and sales processes that could be automated with machines, in particular using NLP technologies.

The two main use cases can be grouped into two groups: workflow automation and data analysis. Let me give you an example for each one. 

In the case of workflow automation, we have customers that are automatically analyzing email responses from outbound sales campaigns. Usually processing responses means a lot of manual work for the sales representatives to read every single email response, which takes a lot of time, effort and energies that could be invested in other strategical tasks.

MonkeyLearn helps by doing that process automatically. Every response is first analyzed by MonkeyLearn and classified with categories like "Interested", "Not interested", "Not the right person", "Contact is on vacations", "Already using the product" or any particular category. That means saving hours of time for sales representatives reading and processing responses. MonkeyLearn solves that in seconds. Then sales representatives can immediately know how to prioritize, which responses should be answered, which ones should be diverted to another team member or even which ones could be automatically answered with a canned response.

In the case of data analysis, we have customers that have to process huge amounts of text data in order to get insights for their businesses. For example, marketing teams performing surveys may receive open-ended feedback. In order to get insight from that data, you have to manually read every single response, but the problem doesn't scale when you continuously receive thousands of responses. In those cases, MonkeyLearn can help by processing responses in real time to get structured insights on sentiment and aspects that are expressed in the responses. For example: you could see if people are complaining about a particular feature of your product or service. Those are just two examples, but there are many others as most of the business data is in text format.

Any other thoughts on AI in marketing, or advice for marketers who are just starting to explore the possibilities of AI?

We definitely believe that there are huge opportunities to apply AI in marketing and sales. The key for us is to make the technology easily accessible by:

  • Providing an interface easy to use for marketers and sales professionals without technical expertise.
  • Integrating with the tools that marketers and sales teams use everyday, like CRMs and marketing automation tools.

 

Imagine if every new growth hacking idea that marketers and sales team have, which involves NLP or machine learning, could be tried out in a couple of minutes. That's when you can have a massive impact. With tools like MonkeyLearn we want to empower sales and marketing teams to build NLP and machine learning powered growth hacks in a matter of minutes. Both by allowing them to pick or create custom text analysis with a simple UI and integrating with the tools that they already use.

Get free access to the Ultimate Beginner's Guide to AI in Marketing: https://www.marketingaiinstitute.com/beginners-guide-access

About Paul Roetzer

Paul Roetzer (@paulroetzer) is founder and CEO of PR 20/20, author of The Marketing Performance Blueprint and The Marketing Agency Blueprint, and creator of The Marketing Artificial Intelligence Institute and Marketing Score. Full bio.

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