Home > Columns > Executive Interviews

CallMiner Executive Interview

As one of the founders of CallMiner, please tell us a little about your background and why you started the company.   

I began building the foundation for CallMiner 20 years ago. I got my start working at a hardware startup company creating high-density media servers to bring voice applications into telephone networks. Back then, everyone was trying to figure out how to leverage voiceover IP, and, during a sales pitch, I thought of voice mining. This particular business I was pitching was tracking over 15,000 companies for things like stock, analyst meetings, etc. but they were just human transcribing the data they gathered and putting it into the archives. There was no actual analysis. I said, “What if 5 minutes after all calls, the data was turned into a transcript and then all those transcripts were analyzed and mined for insights to learn what was said and what wasn’t?” They loved the idea, so I conducted research and tried to find a speech recognizer that had the tech I needed, but no one had the software readily available. I started talking with a friend about the idea of turning call center data into transcripts for trend analysis and discovery, and he thought it was a great idea, too. So, we wrote a business plan and the rest is history.

Over the past few years we have been hearing about how the customer’s experience can make or break a product.  With this in mind, why is it critical for organizations to have the capability to transform the voice of their customers and agents into operational intelligence?

Brands are finally starting to realize that all interactions matter – not just the big ones. We listen to 100% of interactions, including call, email, chat, etc., so we can give brands significantly more insight into customer feedback while also capturing how the exchange was handled by the brand’s agent. This data not only tells you how well you listened to that customer, but also how well you prepared that agent for success. When armed with both, you can evaluate the entire interaction and learn how you can improve.

CallMiner Eureka was named a Wave Leader in the Forrester New Wave AI Fueled Speech Analytics report.  What does your solution do that others in this space do not?

CallMiner Eureka is the only true platform – others are silos that are part of a larger suite. We have the full AI ecosystem needed to deliver key business intelligence in an elevated way. For example, many companies will say they can identify silence, we take it a step further. We evaluate the why behind the silence so you can discover what the problem is and how to fix it. So instead of saying, “There’s 40% of silence on calls into your contact center,” we say, “There’s 40% of silence on calls into your contact center and that silence is tied to a validation process that is unnecessary because the customer called in from a phone number attached to their profile.” We see silence as an insight that can be fixed rather than just a metric to be serviced. The speed to insight and velocity of results are also big differentiators.

The platform’s ability to provide AI and machine learning informed next-best-action guidance to agents while they are still on the call – based on the outcomes of thousands of previously mined calls – is also quite unique, as is the flexibility of the platform to allow each customer of ours to define their own call categories for analysis, based on their own business and the needs of their customers.

And like any true platform, the ability of Eureka to be connected to any other application in our customers’ ecosystems using our API enables our customer to leverage the combined data and combined intelligence of ALL their tools to make better decisions. And we love being part of any solution that does that.

Can you outline the process the Eureka platform uses to leverage AI and machine learning to analyze all customer interactions across every channel?

We don’t view AI and ML as nouns, they are a means to an end. They have boosted the natural language processing (NLP) aspects of Eureka, allowing us to derive intelligence from all interactions including call, text and chat. With AI and ML, we are able to uncover emotion recognition and sentiment analysis to improve customer experience and drive better business solutions. AI and ML quickly, easily and accurately surface the insights needed to do things like call dispositioning so we can evaluate more data and categories than before and understand what’s happening at a deeper level.  

Where do you see speech analytics technology going over the next 3-5 years?

Speech analytics will go away as a standalone category and will evolve into a broader sector that is focused on combining interactions analytics and journey analytics. We will move away from “tools” and more toward business intelligence. Our Coach product is a great example of this – the automated scoring and training is fundamentally changing how agents interact by grading them, identifying the best employees and training others based on that intel. It’s an output that goes beyond just data to drive toward a solution.

Speech analytics will continue to become more about service as a solution and, in turn, will guide entire organizations from the call center to marketing to sales to operations. Executives will leverage the voice of their customers – uncovered from all customer interactions – to fundamentally shift and transform how they operate and make decisions.