A Solution for Shortening Feedback Loops: Announcing Viable, the Startup Providing Instant Qualitative Customer Insights

A-Solution-for-Shortening-Feedback-Loops-Announcing-Viable_-the-Startup-Providing-Instant-Qualitative-Customer-Insights

Early-stage company raises $3.9 million to automate qualitative analysis powered by the GPT-3 advanced natural language system

Viable, an analytics startup using artificial intelligence to automate text analysis of customer feedback, announced it has raised $3.9 million in pre-seed and seed funding to bring to market its next generation qualitative feedback analysis AI tool. Craft Ventures and Javelin Venture Partners led the funding rounds respectively.

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“Computers have been great at crunching numbers for decades, but we’ve only recently unlocked this ability for text. Viable’s state-of-the-art text analysis technology enables the simplest interaction to discover insights in your feedback data: simply asking a question”

Specifically, Viable is the first analytics startup to use the GPT-3 language model, from AI research and deployment company OpenAI, to extract immediate insights buried in customer surveys, helpdesk tickets, and app store reviews at enterprise scale.

“Computers have been great at crunching numbers for decades, but we’ve only recently unlocked this ability for text. Viable’s state-of-the-art text analysis technology enables the simplest interaction to discover insights in your feedback data: simply asking a question,” said Viable CEO and co-founder, Daniel Erickson. “Viable is like an AI UX research assistant. It frees up time so you can get to the insights you need to inform product roadmaps and customer initiatives faster.”

Alex Gurevich, managing director at Javelin Venture Partners, added, “Natural language AI is evolving by leaps and bounds; Viable is putting this cutting-edge technology to work on a compelling use case. As a former product manager who has spent countless hours sifting through, and making sense of, qualitative research, I immediately understood the value of getting instant customer insights from qualitative data.”

Automation that eliminates manual sorting and tagging of free-form text

Customer service and product teams struggle to get useful insights from their customer feedback. It can take them hours to manually sort and tag text for analysis.

Viable solves this by using a combination of language models to tag user surveys, support tickets, and app reviews from tools like Zendesk, Intercom, Delighted, Front, and many others via Zapier. In minutes, Viable structures and analyzes thousands of text entries.

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Useful responses in human language

Once data is ingested in Viable, users can ask any question like “how can we improve our product?” or “what features should we build next?” Viable then generates answers in seconds, written in human language thanks to the natural language processing capabilities of GPT-3.

“Automated qualitative analysis from Viable is like having a friendly, knowledgeable colleague summarize key takeaways from thousands of survey responses,” said Jeffrey Erickson, Viable’s chief product officer and co-founder.

Designed to be simple and accessible for all

Viable provides a simple yet powerful user experience:

  • Easy, 2-click direct integration with feedback tools.
  • Trends are automatically graphed for reporting needs.
  • Unlimited questions—no data formatting required.

As businesses look to use voice of the customer insights in their product cycles, they’re opting for faster insights and less time spent on non-value add tasks.

“Once you’ve found product-market fit, it is vital to constantly ask customers questions in order to sustain and accelerate growth. Viable has made it possible for anyone to do this easily and in real time,” said Rahul Vohra, founder of Superhuman and Viable investor.

A 30 day free trial is available. The startup tier is $250 for up to 1,000 data points per month while the growth tier is $1,000 for up to 5,000 data points per month. An enterprise tier is available for larger monthly data volumes.

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