VoiceOps is on Mission to Make Sales Calls Smarter through AI

04/12/2017 02:45 pm ET Updated Apr 12, 2017

VoiceOps is at the forefront of voice data analytics. Designed for enterprise sales teams and leveraging a form of AI (natural language processing), they've built a product that does in an hour what currently takes sales managers days - understanding what their team is actually saying on the phone. It breaks down core skills and highlights behavioral differences between the best and worst performers, and then scales the winning behavior to the rest of the team. In short, they help sales teams close more deals.

With customers like Weebly, Advent, Livestream, and Intermedia and having recently raised venture capital from well-known funds including Accel Capital, Founders Fund, Lowercase Capital, and Y-Combinator, they're off to a strong start in a space that a lot people are eagerly paying attention to. They also launched on Product Hunt today.

Engineers and data scientists from Harvard and Yale, company founders Ethan Barhydt, Nate Becker, and Daria Evdokimova have cumulatively spent roughly a decade building sales analytics and customer support tools for some low profile companies you probably wouldn’t have heard of, like Google, LinkedIn, Coinbase, Gusto, and General Assembly.

I had a chance to connect with the co-founder and CEO, Daria Evdokimova for a Q&A about their current velocity and where this exciting company is headed.

Sriram: What's your life story?

Daria: Where should I begin? I’ll start the story in Moscow, where I was born…

No, I’ll spare you the details. I went to school at Harvard, studied CS for a while, didn’t quite graduate before I joined Google to work with voice data. From there I joined Gusto, building tools for their support team, and then spent time at Coinbase as a data engineer.

Sriram: No seriously, tell us about Moscow...

Sriram: Tell me about the problem you’re trying to solve.

Daria: I’ll start here: 70% of sales conversations happen over the phone. Compare that to the 10-15% that happens over email.

Today, there’s a multibillion dollar market made up of tools designed to optimize email – email deliverability, open rates, accessibility, the list goes on. But when it comes to call analytics and optimization tools, there aren’t many options. Companies have very little insight into what their reps are saying, why one rep might outperform another by 600%, or how to provide effective feedback and improve performance.

The current process requires a sales manager or team supervisor listen to hours of calls per rep, then use a checkbox system to try and guess at how to coach them. It’s one of their most time consuming tasks, and it’s a process that’s not currently centered around data.

Sriram: Fairly esoteric...how did you recognize that as an opportunity?

Daria: Ethan, Nate, and I have cumulatively spent roughly a decade building sales analytics and customer support tools for some of the most data-driven companies in the world, including LinkedIn, Coinbase, Gusto, and General Assembly.

Even at some of these companies, there’s a gap in software designed to help teams better understand how to optimize their calls for what they might call a “successful outcome”.

Our collective thought was, if we can build a tool that removes the manual process of listening to call recordings and saves managers 15+ hours a week, we’ve built something of some value. But if that data can be used to provide coachable insights that improves successful outcomes, i.e. close rates, and the ROI can be seen not just in time saved but in revenue lift, we built something of immense value.

Sriram: I would agree. Tell me how you’re actually delivering on that.

Daria: That’s proprietary. I can’t tell you.

Sriram: Hmmph

Daria: Actually, from a high level it’s not all that complicated. VoiceOps transcribes calls, our AI engine parses those transcripts at better-than-human quality, then we deliver insights on an individual’s core skills through week-over-week trends.

Was that a lot of buzzwords? It felt like a lot of buzzwords.

What that actually looks like is a dashboard that tells you if your sales rep David is highlighting benefits or just feature dropping on his calls, if he’s making upsell attempts, or asking for the close at the appropriate moment. We then compare individual performances to team averages, and track progress longitudinally. That output makes it really easy for us to also be prescriptive in saying - here are the three things David can focus on to improve.

And while that’s a good thing, it’s only as good as it is effective. Fortunately, we’ve seen some promising numbers so far - one client is seeing a 5% lift in successful close attempts, which at a large organization like theirs equates to millions in revenue.

Weebly is one of our favorite customers. They have seen an 85% reduction in time spent preparing for coaching sessions and a 40% increase in close attempts - results so successful they’ve completely eliminated manual QA from their sales process.

Sriram: Okay, interesting. So is this an ML company or a call analytics company?

Daria: We leverage machine learning to analyze call data, but we’re certainly more of the latter.

AI/ML is going to commoditize in the near future, if we’re not already there. It’s tough to see companies providing AI as a service being viable long-term, and I think the novelty of self-identifying as an AI company will be short-lived. Our perspective is, if you have a strong foundation and your product works, there’s no sustainable competitive advantage in being an AI company.

The competitive advantage is in having a proprietary dataset. That’s where the moat is. By having a huge dataset of call recordings we’ll be able to build best-in-class models that let us perform analysis at a tiny fraction of what it would cost now, making it virtually impossible for a new entrant to get to that level of accuracy at those costs.

Tl;dr - For us, it’s all about traction.

Sriram: Tell me about that - who are your customers?

Daria: We have a pretty wide range, both SMBs and publicly traded companies, but if I were to describe what they all have in common, it would be that our customers are companies that have growing sales teams. They’re onboarding more reps and any tool that measurably helps make QA and training more efficient is extremely useful. The other trait is that they have a fairly transactional sales process, meaning they don’t require 6 months to close a sale.

The kind of actionable call data we deliver is designed to help reps course-correct near-term, so it’s less useful for sales that are based on relationship-building and dinners out, and far more valuable for companies that sell within a single touch point.

Weebly is a great customer of ours - their Customer Success team continues to grow and they put a high value on continually improving and understanding how to optimize for successful customer interactions, both over chat and phone.

Sriram: How do you see the product evolving over the next 5 years?

Daria: It might sound lofty, but I don’t think it’s an overstatement to say there’s an opportunity for a new entrant to be the hub of all enterprise voice data. We think VoiceOps will become that hub.

In a more tactical sense, right now we’re focused on sales, but any industry with an internal org that is reliant on calls and call data for customer success is a potential client of ours – customer support, compliance, the list goes on.

Step one is build the best tool we can, optimizing for an output that is both actionable and valuable for sales teams. If we can prove that value through an impactful ROI, it sells itself.

Step two is take over the world.

Sriram: The world?!

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