Launch HN: ScopeAI (YC W17) – Extract insights from customer conversations
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.
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!
Some things we’ve learned/be happy to discuss further:
-Our process for extracting key phrases from tickets - currently done through a custom pipeline built using spaCy
-How we connect similar phrases - currently using a word2vec model trained on both GloVe vectors and text from tickets in our system
-How we assign broader categories and sentiment analysis using Tensor Flow
Here's an example of an insight we'd extract:
There were 67 requests for subscription cancellations for company x during the month of July:
• 24 requests “slow service”
• 19 requests “I have another account with y company”
• 8 requests “login issues”
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.
Happy to answer questions and looking forward to hearing any feedback!
24 comments
[ 3.0 ms ] story [ 61.3 ms ] thread• 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.
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'
Requested access but 404 on your Calendly link to book a call...
[0] https://youtu.be/8-pJa11YvCs?t=849
We completely agree that anyone making product decisions should spend a considerable amount of time listening and seeking customer feedback but we see ScopeAI as a tool that can make that process more efficient and accurate. Using algorithms for mining trends can prevent product decisions from being made with biases like Recency Effect[0] for example.
We also think ScopeAI can customer feedback more transparent and accessible to people across the company - this becomes more and more of an issue as companies scale and ticket volumes becomes unmanageable for people to manually sift through. For example it’s a lot harder for a CEO to read all customer service tickets if they receive 100 an hour!
[0] https://en.wikipedia.org/wiki/Serial_position_effect
Most other businesses need more touch with the customer.
If you are at the "hacker and/or hustler" phase, efficiently using the time of the principals matters a lot; a founder could probably train a system like that in a week or so and use that to reply to tickets with less time spent and then hand it off to other hires like a "playbook".
This would be valuable to us.
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)
I was wondering- if customer support tickets are coming into an inbox (like in gmail), can this also be integrated?
Thanks!
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?