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Raúl Garreta

MonkeyLearn 3.0 - Get actionable data from text with machine learning

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Turn emails, support tickets, chats, social media, surveys and documents into actionable data. Make your teams more efficient by automating business processes, getting insights and saving hours of manual text data processing.

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Raúl Garreta
Maker
Hi everybody, we're excited to introduce MonkeyLearn 3.0! You may know of us as a service that makes it easy to classify texts (by topic, sentiment or intent) or to extract specific data (such as keywords, names, companies and addresses). Over the last couple years we have been working with clients ranging from well known SaaS companies, to oil and gas businesses. They have all had a common need to automate manual processes around text analysis: whether it be to process support tickets, analyze feedback or reviews, or extract information from contracts and documents. We are now launching a new MonkeyLearn version allowing more people to build text analysis models powered by machine learning. Here is what we are excited to share: - New redesigned GUI and API: a cleaner and simpler to use both for technical and non-technical users. - Custom Extractors: this is a new major feature, you can train a ML model to extract custom data within texts. - Custom Classifiers: you can train a ML model to classify texts into tags as with our previous version, but much easier :) - Active Learning: the tagging process is quicker and tags are suggested as you train the model. Looking forward to hearing your feedback!
Shreyaa Ratra
@rgarreta Can't wait to test it out for my own SAAS. Couple of thoughts if you do B2B : A) Target SAAS companies who have recently raised funding. Their prime focus will be to scale. Scale >> More users >> More customer queries >> MonkeyLearn B) Target companies who are hiring for customer support/customer feedback because these are the companies who are hiring for more customer service employees and will be needing a solution to handle/process their tickets.
Santiago Alonso
@rgarreta congrats! 💪🇺🇾
Federico Pascual
Maker
@rgarreta @shreyaa_ratra let me know how it goes, I'm happy to help! Thanks for the ideas :)
Federico Pascual
Maker
@rgarreta @madebysan thank you mate 😊
Raúl Garreta
Maker
@shreyaa_ratra Answering your comments: A) That's right, believe that repetitive tasks like tagging support tickets and customer feedback should be done automatically by machines. That means more efficient support/product teams: scalability and more consistent criteria. In the case of customer support: shorter response times >> happier customers. In the case of customer feedback: more powerful insights from qualitative data >> better products >> happier customers. B) Agree, this is a rising area, product companies must compete on having meaningful interactions with their customers. Our mission is to empower (not substitute) teams such as customer service to achieve those goals. Humans should be focused on strategical tasks, machines on the repetitive ones.
Felipe Coirolo

I love this product

Pros:

Powerful and easy to use

Cons:

None

Raúl Garreta
Maker
Thank you Felipe!
guillaume cabane
Been using MonkeyLearn for 2 years now - and it's been an awesome experience. Excited to see all the improvements brought with 3.0, especially custom extractors. Here's what I've used MonkeyLearn for: - Categorize NPS comments to inform my product team - Sentiment analysis on outbound email campaign responses (as a leading indicator to campaign/list quality) - Many other unpublished growth hacks :)
Raúl Garreta
Maker
@guillaumecabane Thank you G! We've learned a lot from your use cases ;)
Bruno Stecanella

more!

Pros:

Custom extractors! A new API that doesn't suck!

Cons:

A worrying lack of monkeys :(

Nico Bistolfi

Pros:

Events classifiers work like a charm

Cons:

Can't classify what my dog is trying to say... yet

Raúl Garreta
Maker
Will try to add support for dog language :)
Sebastián Álvarez
Amazing! Congrats guys
Raúl Garreta
Maker
@sap_uy thank you!
Cody Fitzpatrick
Product and website look great. Congrats @rgarreta and @federicopascual on reaching V3! Your efforts on this will surely pay off if they haven't already. Looking forward to trying it!
Raúl Garreta
Maker
@federicopascual @codyfitzpatrick Thank you Cody! Definitely all the team has been working very hard :D Would love to hear your comments after you try it out!
Federico Pascual
Maker
@rgarreta @codyfitzpatrick thanks Cody for the kind words! Looking forward for your feedback :)
Clarisa Lucciarini

It's great that you can use what you need, without expensive setups.

Pros:

Make data actionable for you business

Cons:

No complaints!

Chris Buttenham
Excited for these updates. Congrats Raúl and team!
Raúl Garreta
Maker
@chrisbuttenham Thank you Chris! The team worked hard to ship these new features :)
Niraj Swami

Great work from a solid team!!

Pros:

Great redesign and features -- text-centric ML now has an amazing UI to design and API to consume. Also, rock solid out-of-the-box models!

Cons:

Want more customization powers on those models! :)

Tibor Fejos
I want this!
Federico Pascual
Maker
@fejostibor sweet! would me amazing if you can try it out!
Martin Manzo
Looks awesome!
Federico Pascual
Maker
@tincho89 thanks Martín :)
Elijah Elkins
This looks GREAT! Very impressive! Not sure how you’re training your ML. CloudFactory could probably help if you need a scalable data workforce. https://www.cloudfactory.com/
Elijah Elkins
This looks GREAT! Very impressive! Not sure how you’re training your ML. CloudFactory could probably help if you need a scalable data workforce. https://www.cloudfactory.com/
Elijah Elkins
This looks GREAT! Very impressive! Not sure how you’re training your ML. CloudFactory could probably help if you need a scalable data workforce. https://www.cloudfactory.com/
Mayank Chhabra
Wow, this looks incredibly helpful! Looking forward to give it a shot. On a side note, could you please let me know where did you get your explainer video done from? It is fantastic!
Raahul Seshadri
Congrats on the new version. Sounds exciting. Has there been successful deployments of MonkeyLearn other than in the customer service industry? Any case-studies that I can refer to? Thanks!