Exclusive Interview with Daniel Erickson, CEO, and co-founder, Viable

Designing a good User Experience is all about catching the pulse of the users. Given the diversity of mindsets digital users come with, it is near impossible to engage every visitor or user. It has long been a challenge for UX designers to gather user data, particularly when it is qualitative and draw useful insights. Viable, a major player in providing AI-enabled analytics says its platform talks to its clients in their language. Analytics Insight has engaged in an exclusive interview with Daniel Erickson, CEO, and co-founder, of Viable.

1. Kindly brief us about the company, its specialization, and the services that your company offers.

Viable is a technology company that helps businesses access and act on valuable insights in customer feedback. It sounds simple, but the reality is that businesses collect data from a host of sources – help desk tickets, surveys, product reviews, internal customer notes, call transcripts, and so on – and it all sits in different databases and different formats. The result is that much of it goes unused. Our artificial intelligence-based technology changes that.

With Viable, businesses can automatically aggregate, structure, and analyze all of their data – we're talking qualitative data and not just quantitative. Now, don't get me wrong, quantitative data can be immensely useful, but it can't relay what your customers are telling you in their own words across all of your channels. That is where we come in.

Our focus on qualitative analysis is what makes us different from many analytics platforms, but even more so, we stand alone with our question/answer format, which is enabled by GPT-3. There are no SQL queries or pivot tables, it's just asking questions in plain language and getting plain language, yet data-backed insights in return.

This approach vastly extends the usability of the platform while removing bottlenecks and empowering customer support, product management, and all the other departments that need to act on insights. They would no longer need to go to the research team and then wait weeks for research to get back to them. The tech itself becomes the analyst. It's fast, it's scalable and it never needs to go on vacation.

2. In short, tell us about your journey since the inception of the company? What is your mission?

Put simply, Viable's mission is to help businesses of any size quickly understand what their customers are telling them so they can better address the issues that matter most.

When the company was launched, things were a bit different. Our target customer was early-stage companies and the mission was to help them find product-market fit. We created a survey and collected responses, then using the Superhuman product/market fit engine, we could return a product roadmap.

Many of the elements we use today such as natural language processing (NLP) and some of our AI algorithms were already in place, and our front end through which the data could be analyzed was great. What wasn't, was the focus on startups. It was simply too small of an addressable market.

We were fortunate that our next move came to us, as larger companies started approaching us to help them understand their data. It's a bit like we moved into customer feedback more broadly by popular demand.

As we moved in that direction, we knew we still needed a killer differentiator, and this turned out to be GPT-3, Open AI's language model that leverages deep learning to generate human-like text. I was invited to experiment with it early on, and I spent a month working with the technology and our data. The "Eureka" moment was realizing a question-and-answer system — now the main pillar of our product — as possible.

3. Tell us about some of your notable awards and achievements that you feel have been valuable for the company.

We launched in 2020, so the fact we've made it this far is already a pretty big achievement. We were able to bring in some funding from an incredible group of investors, which we see as the greatest testament to the value of our solution.

Our first raise of $4M was led by Craft Ventures, whose notable exits include Tesla, Bird, and Airbnb, along with Javelin Venture Partners, whose portfolio includes Mythical Games and Smart Asset, which recently joined the unicorn club. Our most recent raise of $5M was led by Streamlined Ventures, previous backers of DoorDash, AppLovin, and iRobot. 

4. Tell us about your contributions to the company and the industry.

My story is a bit different from the average founder, and I believe this unique background is the source of much of my contributions.

I spent the majority of my childhood as a competitive gymnast, and it's here that I learned one of the most important life lessons: how to fall gracefully and get back up every time. Now, as an entrepreneur, I'm not afraid to try new things and fail, because, with every failure, there's an opportunity to learn something hugely valuable.

I'm also a self-taught coder who skipped college. This has helped shape a lot of my opinions and approaches, and frankly, I believe it's made me a thoughtful and understanding CEO.

For the developer community, I'm probably best known as an early engineer at Yammer and someone who helped get Node.js, an open-source, cross-platform, back-end JavaScript runtime environment, off the ground in its earliest phase.

Now that I'm in management, I'm consciously working to help eliminate some of the biases in the system, from tech and an organizational standpoint. While I was with Getable back in 2013, I was able to build a gender-balanced engineering team. I continue to work to encourage better diversity, equity, and inclusiveness at the company level.

At Viable, I'm also pushing for DEI on the technological level working to mitigate the human biases that we inadvertently inject into technology. For example, our proprietary technology allows us to mitigate the harm of bias within our qualitative feedback reports, providing our customers with a sense of comfort that the feedback they are receiving is not influenced by the potential bias presented in manual analysis or more generic use cases of large language models.

5. Kindly mention some of the major challenges the company has faced.

The biggest challenge for Viable is, perhaps ironically, finding product-market fit. We originally raised a seed round for our first product, but after realizing that there might be a better, more appealing market alternative, we went back to the drawing board and used our investments for a different purpose than what we originally sought out. In addition to the actual transition over to our new product, understanding and accepting that we needed to restrategize our product was a huge challenge.

Another challenge has been cost optimization. We didn't want to repeat the mistake of many startups by providing a service at an unsustainable cost. Now, we've built enough understanding to know when to build versus when to buy. For example, in addition to GPT-3, which can easily get costly, we have over a dozen other proprietary and internal models in use.

6. How do you see the company and the industry in the future ahead?

One of our focal points for future product development is addressing an issue we didn't see coming, and that customers don't know what they don't know. Or in other words, though they have a tool to ask questions, they don't necessarily know what questions to ask.

What we realize is that pulling insights – asking and getting an answer – is only half the equation. We also need to push insights to them. This means we have to better understand what it is that they are looking for.

We're also planning to incorporate quantitative data so that, in addition to questions about how the people who use features feel about it; our customers can find out how many users of feature X exist.

We're also exploring how needs are changing with the normalization of remote and hybrid work environments. We're sure that there will be new gaps for which we will need tech to fill. We just haven't experienced them yet. So, we're looking ahead and exploring opportunities for things like sound and image AI that can position us to address new use cases as they emerge.