Authenticx’s VP of Product Innovation Michael Armstrong and CEO Amy Brown recently sat down to discuss the challenges they faced in corporate America that can now be resolved with Authenticx.
Could you each share your perspective on how Authenticx is solving problems for corporate America today?
Amy: First off, I have to give all the credit to Michael for helping us solve these problems. I describe the problem and what I’d like it to look like at the end and he figures out how to make that happen.
So one of the ways that we solve for this is we are able to curate, using technology, the most valuable insights that contain the answers to the burning questions that are our clients have. In other words, we help them find needles in haystacks. We help them curate the most valuable insights that can help them know things like, “What is causing the greatest amount of customer pain? What is causing our company to be at risk of losing customers? What are the biggest pain points of our customers?”
Conversely, we’re also able to surface, “What are the things that our customers value that we don’t even know about? What are the things they’re saying about our people, our product, our competition?” And so using, not only the Speech Analyticx aspect of our platform, but the guided insights analysis part of our platform, that gives analysts the ability to consume the data, to immerse themselves in the customer’s journey. We’re able to make the absolute best value of very expensive time (our client’s time) by putting in front of them highly accessible, highly consumable customer interactions that are going to make the difference.
“We are able to curate, using technology, the most valuable insights that contain the answers to the burning questions that are our clients have. In other words, we help them find needles in haystacks.”– Amy Brown
Michael: I think my answer is very complementary to what you just said, Amy. I think this is again shaped by previous experience throughout my career. I have been involved in data analytics for a long time and there are a couple of consistent themes.
One is that there are statisticians, data analysts, and data scientists with a lot of powerful tools now that didn’t exist 20 years ago. But they all generally have the same problem and that is, “I can build a model that predicts that or I can build a model that would tell us the answer if I just had training data. If I just had data that was already labeled for me and it was high quality and I could trust it.” And I experienced that over and over again.
And then the second thing I saw was people thinking of AI and machine learning as a silver bullet. That you could just go in and apply a magical neural network and somehow it’s just going to give you the answer, whatever that might be. And quite frankly, if it did give you the answer it wouldn’t really be useful.
That’s not what we’re trying to get to. We’re not trying to get to the answer, we’re trying to get to the insights. We’re trying to get to the actionable output, the nuances of an interaction or interactions overall. We’re trying to find those trends that our customers can act upon. And so to that end, the way I think about what we’re doing is, we have tools that allow you to engage analysts straight out of the gate. We’re doing some sampling and we’re completing evaluations of an interaction, which is done by analysts, and its manual. But that’s okay because one month in, you have insights. And they’re really good, high-quality insights.
But the other thing you have is the beginning of a really high-quality training data set. And that whole idea of machine learning and AI is built into our product and built into our process, and built into the way we go to market. We’re not trying to say it’s magical or a silver bullet. We’re trying to say we have a process, we have toolsets that will allow us to get better every single month, every quarter. You’re going to get insights you would never even imagine or dream you would get out of this data set.
But also, one month in you’re going to have insights. There are so many other companies and they approach it from the standpoint of “we have to gather data for six months” or “we have to gather data for months at a time and we have a huge implementation and sort of ramp-up” and that just doesn’t fly with us. That just doesn’t work. I can tell you working with Amy now for quite some time, the answer is generally, “We measure things in weeks, not in months.” So we have to get to insights in a hurry.
You can listen to Amy and Michael’s entire conversation here.