I'm Luciano, co-founder and CTO of ScopeAI (<a href="https://www.getscopeai.com/" rel="nofollow">https://www.getscopeai.com/</a>). We’ve built a product that automates the process of extracting and communicating user insights to product and operation teams. To extract product insights, we integrate with support channels such as Zendesk, Intercom and desk.com and use NLP to automatically tag, categorize and cluster support tickets.<p>Customer support teams currently spend hours manually tagging customer support tickets to track trends in user feedback. The process is inefficient, lacks consistency and is reported retrospectively. This process typically fails to capture the granular insights requested by product and operation teams.<p>Natalie, our CEO is a former UX researcher. In that world, the process for extracting trends from user interviews was completely manual. It involved codifying the conversations and counting how frequently certain feedback was mentioned. It was definitely difficult to scale. We recognized that there needed to be a better way of extracting trends from unstructured data and started working on ScopeAI!<p>Some things we’ve learned/be happy to discuss further:<p>-Our process for extracting key phrases from tickets - currently done through a custom pipeline built using spaCy<p>-How we connect similar phrases - currently using a word2vec model trained on both GloVe vectors and text from tickets in our system<p>-How we assign broader categories and sentiment analysis using Tensor Flow<p>Here's an example of an insight we'd extract:<p>There were 67 requests for subscription cancellations for company x during the month of July:<p>• 24 requests “slow service”<p>• 19 requests “I have another account with y company”<p>• 8 requests “login issues”<p>Knowing this is really valuable for this company because they can make better decisions - in this case, making the software faster became a much higher priority.<p>Happy to answer questions and looking forward to hearing any feedback!
Yelp, Google and other review aggregators do an interesting technique where they isolate significant phrases and intents from reviews and collect them into one report, and present it to the end user to help them make an educated decision. How does your technique compare? Are there significant differences in the objectives when catering to an internal audience (ticket management) instead of an external audience (public reviews)?
As an operator of a subscription service, this sounds valuable. I would be interested in an app where I could connect my Drift account and get some data on this.<p>I would also be interested in a SaaS that simply managed cancellations and collecting feedback there.<p>Also, interested in a SaaS that does the same except for tracking true chain of referral (tracks down the customer and makes them answer 'Where did you initially find us', 'What ultimately triggered the purchase'
Sorry for the harsh words, but this looks like a solution looking for a problem. Any CEO of a company that's lucky enough to have customers contacting them would spend a not trivial amount of their time reading and replying to the contacts. For example, Jan Koum, Whatsapp founder said that they only started hiring dedicated customer service staff when they reach 150M users [0].<p>[0] <a href="https://youtu.be/8-pJa11YvCs?t=849" rel="nofollow">https://youtu.be/8-pJa11YvCs?t=849</a>
This is an interesting topic, at my first job I've developed some tool to help our customer support, it's a topic which is quite interesting for me.<p>On what data do you train your models? Do you train them individually for each customer with their own data, or do you take the data of all your customers and train "universal" model?<p>Another question about the broader categories, are they defined by you? I guess you're doing some supervised learning. Is it possible for the customer to add own categories?
Awesome. Have recently started work on a well established product with a ton of data but it's painful to go through all these channels manually and tag it all. Ideally we'll get better at that over time but I know that we'll miss out on some of those new tags, or adding verbatims to tags easily.<p>Requested access but 404 on your Calendly link to book a call...
Is there any integration for review channels on iOS/Android app stores? Google play console currently has some tools around review analysis but I don't believe the Apple app store does. This could be really helpful.
Congrats on the launch Luciano and Natalie! The product sounds incredibly helpful and I'm eager to try it out.<p>I was wondering- if customer support tickets are coming into an inbox (like in gmail), can this also be integrated?<p>Thanks!