
Launch HN: ScopeAI (YC W17) – Extract insights from customer conversations - iloveluce
I&#x27;m Luciano, co-founder and CTO of ScopeAI (<a href="https:&#x2F;&#x2F;www.getscopeai.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.getscopeai.com&#x2F;</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&#x2F;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&#x27;s an example of an insight we&#x27;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!
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Zee2
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)?

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iloveluce
Great question! It’s similar in that we extract significant phrases and
measure their frequency. Our differences:

• We combine similar significant phrases together. I.e. cancel subscription
and end services would be clustered together.

• We track and display their distribution over time not just their total
frequency. i.e. is it trending? Is the current downward trend significant?

• We ‘learn’ from each company the topics that interest them by both manually
allowing them to subscribe to topics or mark them irrelevant as well as by
automatically tracking click through rates and time spent.

As for the internal audience vs. external audience, we focus on the questions
that are important to a product or operations team: how to reduce churn,
feature requests, sentiment (what improves customer satisfaction) which is
different from information valuable for consumers.

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gargarplex
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.

I would also be interested in a SaaS that simply managed cancellations and
collecting feedback there.

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'

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boto3
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].

[0] [https://youtu.be/8-pJa11YvCs?t=849](https://youtu.be/8-pJa11YvCs?t=849)

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tomblomfield
At Monzo (a bank), we have approximately 10,000 customer contacts a week.

This would be valuable to us.

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lifeisstillgood
You also have a significant advantage over other banks in that your customers
can easily and securely communicate with you over text (I once tried to send a
secure message to my "other bank" and gave up after ten minutes on their
site.)

This is great for me as a customer, but I suspect it creates for you a greater
per customer text volume to comb through than most other banks.

Anyway, looks like this might become a sale - hope it goes well for both
sides.

(A very satisfied Monzo customer)

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arrmn
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.

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?

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?

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nedwin
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.

Requested access but 404 on your Calendly link to book a call...

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iloveluce
Thanks so much for catching that. It should be good now, here's the link for
reference
[https://www.getscopeai.com/schedule_call](https://www.getscopeai.com/schedule_call)

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whospablo
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.

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iloveluce
We currently don’t have iOS/Android app store integrations but they’re
definitely on our roadmap! Many of our current customers already have their
customer support channels all integrated, for example they integrate Zendesk
with their Google app reviews so when we integrate with their main support
channel i.e. Zendesk, we end up analyzing all of their customer conversations!

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sebastianw
Congrats on the launch Luciano and Natalie! The product sounds incredibly
helpful and I'm eager to try it out.

I was wondering- if customer support tickets are coming into an inbox (like in
gmail), can this also be integrated?

Thanks!

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smith-kyle
We were drowning in feedback at my old job, this would've been super useful.
Can I customize the tags at all?

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iloveluce
Yes we can automatically apply established tags to incoming support tickets
and there is the option to delete and merge similar tags that ScopeAI
generates and assigns to tickets as well!

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drc18
Would this work with tickets where customers mix a bit of local language
(spanish in our case) with English?

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iloveluce
Generally homogeneity in language works better for the analysis (accuracy is
higher) but we do capture tickets with some mixed languages. In this case, we
mark the non-english word as an irrelevant entity instead of translating.
Spanish support however is next on our roadmap of languages to support!

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kuntajts
Whats the ideal number tickets coming through to get valuable feedback?

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iloveluce
The higher the volume of tickets the more precise our insights can be but as a
general rule 100 tickets are required to gauge trends in feedback. For some
companies this is an hour of customer requests and for other companies this
could take a week to accumulate.

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saycheese
What is your revenue model? Are there costs to use the platform? Etc.

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iloveluce
We have a monthly subscription revenue model based on volume of tickets
analyzed (different tiers for different ranges of ticket volume).

