CallMiner Executive Interview
Jeff Gallino, CTO and Founder, CallMiner
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
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
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
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.