WEBVTT

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Southern Philippines. The country's military said
he believed the attack was retaliation after a

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military operation against pro Islamic state groups. A pair of once unbeaten teams are

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in and an undefeated team is controversially
held out of the four team New Year's

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Day college football playoff field. It
was announced today Number one in unbeaten Michigan

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will face number four Alabama in the
Rose Ball in one semifinal, while unbeaten

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second ranked Washington takes on third ranked
Texas in the Sugar Bowl in the other

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semifinal. The winners will play for
the national championship. The Florida State Seminoles

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at thirteen to zero, were left
out, becoming the first unbeaten Power Five

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Conference winner to ever miss out on
the college football playoff. I'm Chris Karragio,

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NBC News Radio or on board kcaa's
Inland Extress KCAA Comlinda ten fifty Am,

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the station that leaves notice year behind. The information economy has a ride.

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The world is team with innovation as
new business models reinvent every industry industry.

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Inside Analysis is your source of information
and insight about how to make the

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most of this exciting new era.
Learn more at Inside Analysis dot Inside Analysis

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dot com. And now here's your
host, Eric Kavanaugh. L all right,

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ladies and Doleman, welcome to the
future. Indeed, your host Eric

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Kavanaugh here for another episode of Inside
Analysis, the only Coast to coast radio

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show, all about the information economy
and what's the big deal of the information

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economy these days. The experience.
It's all about the experience, folks,

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the customer experience, of course,
the partner experience, the user experience.

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That experience had better be good if
you're going to keep your customers, if

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you're gonna keep them happy. There
are lots of options these days. It's

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very easy to leave a certain service
provider. You can just get tired and

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go to a new service provider.
So we're going to talk about customer experience

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and how that gets done, all
the interesting stuff that happens behind the scenes

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and under the covers in order to
get you that high quality customer experience.

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Now, I can tell you I
remember a number of years ago, one

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of the first companies to figure out
that they could grab my record from my

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phone number. I was like,
hooray, they figured it out. They

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know this is the number associated with
my account, they could bring up my

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call record when I call it to
the call center and do a good job

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helping me. What a fantastic innovation
that was. Well, the gentleman on

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a call today know all about that. We're going to be hearing from Dan

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Bodner. He's the CEO and co
founder of a company called a Varrant that

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does all kinds of good stuff and
customer experience. And Andy Roberts, who

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is with the Sabio Group out of
UK. They do similar things. And

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we're going to talk about call center
as a service. So what does that

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mean? So call centers a service
is a new kind of technology and basically,

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as the name implies, it's a
service provided to companies that want call

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centers. What do you have to
do? You have to line up all

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these different systems, get the data
there, make sure you have good people

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of course on the phone. All
that stuff comes in handy. But the

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experts are going to tell us what's
going on. So first of all,

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Dan Bodner, welcome to Inside Analysis. Tell us a bit about yourself and

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your amazing journey at Varian since what
nineteen hundred and ninety four, that's like

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the last millennium or something right,
tell us what's going on? Yes,

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Hi Eric, and hello everyone.
So it's all about the ex customer experience,

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and today it's all about the ex
automation. With AI coming to play.

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Finally we can use bots to help
the agents to improve customer experience.

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So happy to talk about that.
But a little bit about variant. Yes,

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we started in nineteen ninety four.
At that time it was all about

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taking unstructured data, mostly voice and
speech data, and analyze it to find

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insights in the data. Ten years
later, in the two thousand and six

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timeframe, we created workforce optimization.
This was about giving the workforce tools so

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they can perform their their work better
because you know, conduct center, as

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you mentioned, are very labor intense, intense, there's a lot of people

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working. So we kept that journey
with marrying data mostly unstructured data, initially

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speech but also text and video and
giving workforce engagement tools, you know,

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more capabilities. But really what's exciting
is a few years ago we started to

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put together very da VINCAI at the
core of the platform. So the workforce

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is starting to use AI and of
course today with Jenyi, the workforce is

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no longer just people, it's really
people on bots working together, and that

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increase the capacity of the workforce tremendously
and allow the workforce really to get the

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more enjoyable and elevated customer experience for
the consumers, which is really what the

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industry has been trying to do for
many, many years. But because it's

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very expensive to hire people, it's
also very hard to find them and train

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them, and then they don't have
time really to delight the customers. So

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now is the help of bots working
side by side with people. I think

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we're getting to now to a point
that we can actually achieve more with the

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same resources, same budgets, so
our customers can delight their consumers with better

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customer experience through the consumption and boughts. Yeah, I think you hit the

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nail on the head there with the
same budgets line, that's the kind of

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thing that makes the coos at your
clients very happy in the CFOs because you

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typically hear about new, fun,
interesting things that cost more money. So

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if you can deliver a better experience, a more streamlined experience, for the

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same budget, that makes people really
happy. So the vendor experience is pretty

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too, right, Absolutely, our
customers are happy because they can contain the

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budget, but also they want to
do better job. They know that they

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can differentiate from their competitors by giving
better customer experience. So customer experience is

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not lost on brands. It's just
that it's expensive and with new technology and

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people on bot's working together, they
can now afford it and do more with

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the same budgets and at the same
time delight to their consumers so they can

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get more leyalthy, they can get
you know, better revenue generator from a

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generation from their customers. So it's
a win win win. The consumer obviously

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wins, and of course we all
consumers ourselves, so we love to be

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on the call where everything goes very
smoothly and we get a contextual and quick

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responses. So obviously it's a win
for the consumers, and it's a win

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for our customers because they can do
more with the same budget. And it's

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a win for Variant because we get
to sell new technology. Yeah. Well,

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and just real quick, before I
bring Andy into comment on this,

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I'd like to share with their audience
there are so many ways in which machine

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learning and AI can help and the
automation component in particular. For example,

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skill path routing is something I learned
about some twenty odd years ago. Over

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a course of time, if you
have machine learning in place, you can

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track which call reps do better in
certain scenarios. It could even be with

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certain demographics, if someone's calling in
from Georgia or New York or New Jersey

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or wherever. It could be the
location, it could be the age group,

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it could be the topic at hand
that someone's calling about. And to

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be able to ascertain that kind of
thing while the call is coming in and

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then route that path to the appropriate
person. That's just one of many ways

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that you can really optimize the experience. Right, Absolutely, you hit the

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nail on it. It's all about
data, right. If you have the

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right data, then your AI can
do machine learning on the data. And

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data power is really the very open
platform. So basically, this data that

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we have in our platform because we
have been recording agents for many, many

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years, and that agent behavior is
really the source of the data that empower

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the bots to do machine learning and
emulate agents and try to do the job

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that and take some of the functions
that humans are doing replace it with automation.

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So yes, it's data and there's
lots of use cases for the data.

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You mentioned one, and I'm sure
we're going to cover a few more

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on the show here, because the
data does power the AI, and the

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AI powers automation. Yeah, it's
really wonderful stuff because what you're doing is

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you're leveraging, first of all,
the organic energy and success of your team

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members. So everyone knows when you
dial in, this call may be recorded

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for quality monitoring and assurance. Right, everyone understands that. But these days

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you can automate that process. Now. Twenty years ago, even ten years

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ago, most of the time there
was just some manager who was listening and

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it would take manual notes. But
now with the power of AI, there

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are all sorts of technologies. Gone
comes to mind. There's some others out

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there that are very interesting, and
I know that you guys have a whole

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bunch of good stuff too, where
you can do sentiment analysis even in real

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time listening to someone to understand,
Oh, this person's getting upsetting. Hit

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a red flag, bring in the
higher level service. Oh it's a high

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value customer too. Uh, let's
go all out make sure this person is

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taken care of. You can trigger
all those actions because you have this foundation

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of data, a real world data
of what people said, how they said

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it, how timely the conversation was. All these little tidbits fold into a

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recommendation engine that then gets the job
done for you, right real quick,

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Dan, Yes, absolutely, So
the data is the foundation. We call

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that the gym. That the bots
need to really go to the gym every

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day twenty four to seven, twenty
four x seven and they train on that

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data so they become better and better
and more accurate to augment the workforce.

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So, yeah, the data is
key, and real time obviously is very

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important. How do we help the
agent in real time, whether it's suggestion

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knowledge or as you said, pointing
out the sentiment that needs to be addressed

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because the customer is frustrated they don't
really hear what you say, right,

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so slow down, take to time, show some empathy. These things can

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be done in real time because now
all this technology is running in the cloud

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and you have elastic process in power, so you can actually apply posting power

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in the cloud to create real time
AI outcomes business outcomes for the agents.

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So the technology of cloud technology,
AI technology coming together empowered by data is

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really the Sickeret sauce for increasing c
exformation. Yeah, that really is.

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It's the confluence of all these factors. As you suggest, cloud computing AI,

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which is really mature now. I
mean AI has been around arguably for

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forty five fifty years. We went
through a couple AI winters when the hype

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got too high and there just wasn't
the compute power that we have today.

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But now it's everywhere. We have
many different cloud providers. You could do

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some of this stuff on prem you
don't have to go to the cloud.

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But it really does help to have
this marshaling area because guess what, those

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folks are busy making sure those servers
hum as best they possibly can all day

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long. That's their job. So
your job as the company using that service

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is to optimize what you're doing.
And as you suggest, having that wealth

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of information, of rich unstructured data
of audio files, text files, process

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models, all that fun stuff,
it all comes together to create this springboard

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basically, which is now in action, right Dan, Now, right now,

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that kind of technology is happening when
people call the call setters today,

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right Dan. Absolutely, we have
hundreds of boughts deployments across many the largest

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brands in the world are now adapting
AI powered bots, so it's no longer

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just a high when you bring the
bots into the platform and you embed it

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into existing workflows, so it doesn't
disrupt the workforce, right, it's part

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of the natural flow of what the
agent does. Because look, contact center

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agents don't have time to search with
AI tools, right, They're not going

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to get on the internet and search
because that means they have to put you

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on hold, and obviously that will
take a longer call and a very poor

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customer experience. So the goal of
today's platform is to bring that AI to

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the fingertips of the workforce, so
it's embedded in their workflows and it can

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really power them to do to be
more productive. And the way we think

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about bots here at Variant is we
created many, many bots. We have

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thirty five bots right now in our
platform. Each one is doing only one

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thing, but they do it very
well. So again I give you one

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example. One of the things agent
needs to do at the end of the

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call when the customers sign off is
they have to summarize the call. Now,

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they don't like to do the summaries
because it takes them time. They're

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not really good in typing and they
always been criticized. Hey, the summary

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is really terrible English. So guess
what you know? They call the rapprobot

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and instantly they get the summary prepared
and it's better summary that they can do

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themselves. So the agents are happy. You can cut sixty or ninety seconds

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of the call, which is a
huge amount of savings, and obviously it's

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a win win win for everyone.
So this type of Jenny I is not

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just technology. It's really technology that
is the finger tips of the agent when

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they most need it. And this
way you can really start to create business

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outcomes. Yeah, that's wonderful stuff. And just to help our audience understand

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actions like putting someone on hold,
I promise you no one likes to be

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put on hold. Okay, everyone
to deal with it. They were like,

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okay, I'll get put on a
lot. It's fine. You go

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and go back to your computer,
start typing or doing something else as you're

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weighting. But no one wants to
be put on hold. So if you

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can lower that number in particular,
that's going to greatly improve customer experience across

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the board. And I see Andy
nodding his head in the background. There,

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let's bring in Andy Roberts from Sabo
Group has been working with the folks

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at Variant for like decades, for
a long long time. And Andy,

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I saw you smiling there. So
you know that this whole concept of open

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call center as a service, we'll
talk about that today. It really reflects

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an inflection point I think in the
industry right because and you see this across

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the board in terms of it,
you see all sorts of things as a

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service, managed service providers. That
just makes life easier for the customers who

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are just trying to get their business
done. But tell us about yourself,

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Andy, and call center as a
service. Up, you're on mute.

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I think you're on mute. No, I can't hear you. It doesn't

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say your arm on mute, but
I can't hear you. Let's go back

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to Let's go back to Dan while
he's figuring that out. Something must have

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happened with your microphone. We'll get
you back on in just a second here,

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But Dan, I'll throw it back
up to you. Call center as

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a service in particular, open call
center as a service. Talk about what

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that means. I mean, you
talked about this open varied platform. What

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do you mean by open call center
as a service. Yes, So you

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know the technology now is moving so
quickly that customers are really not want to

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be disrupted by a transformational change,
right, so you can just throw away

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everything you have in the contact center
and start with the next technology and guess

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what, a year from now,
it will be even better technology. So

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he can't really restart every year.
So you need an open platform, one

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that integrates into the ecosystem that you
have today and that you can plug and

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play with pieces of mute technology that
you can add to your legacy solution and

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evolve over time. And especially when
it comes to open data, because we

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agreed right, data is the second
sauce here, so you really need to

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bring all the relevant data into an
open platform so that all the applications running

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in the platform can benefit from this
data. So open data being the ability

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to collect silo data and behavioral data. You know, like we discuss agent

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how agents behave is really locked up
in silos across the enterprise. So bringing

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all that together to an open data
hub using open da Vinci, which is

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our AI engines, so you can
leverage commercial models, not just propriety models.

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Because there's so much innovation industry around
AI engines. So the ability to

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bring in the latest AI engines into
the platform, quickly train it on the

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data in the gym, and then
embedded into workflows. That's creating an open

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environment where customers can evolve, and
you know, we have customers that really

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move quick, but other customers need
to evolve at their own pace because you

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know, running a connecenter with five
thousand agents, it is not a small

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headache for the COO and you can't
just disrupt yourself. So we designed the

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platform to be open with that in
mind, and also hopefully Andy can comment

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when when you get his mic back. But hoping is great for partners because

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partners can really develop all kinds of
value edited services around the platform to help

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customers to consume AI more effectively.
And there's so much they can do,

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whether it's data practices and getting insights
from data so they can improve you know,

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the to bestic class operation, or
or taking bots and integrating these bots

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with the customer environment so they can
create more connectivity. Because eventually the bots,

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you know, even if they understand
the human being, their ability to

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respond is really based on how well
they integrated into the customer ecosystem. So

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a partners can can leverage an open
platform in order to really differentiate themselves at

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services and delight our joint customers.
And and uh, you know, uh,

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we've seen a lot of activity for
partners taken advantage of being open.

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Yeah, and that's that's a really
key point about open standards and open systems,

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open platforms, because we are dealing
with an ecosystem, and when you

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try to do everything yourself, you're
never going to do the best job.

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If you can enable your partners to
work within your ecosystem and leverage their expertise,

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that's when you really get the power
of a whole environment, of a

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whole ecosystem. Well, folks,
don't touch that. That will be right

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back talking to a couple of experts
in the field of customer experience, Dadminitor

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and Andie Roberts. Don't touch that, doll. You're listening to Inside Analysis,

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Expected Recording and Progress. Welcome back
to Inside and Analysis. Here's your

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host, Eric Tavanaugh and take this
to all right, folks, back here

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on Inside Analysis talking to a couple
veterans in customer experience. These folks really

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know what they're talking about. We're
talking to Dan Badner's CEO and co founder

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of Variant, founded in nineteen ninety
four. I could do some quick math.

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That's thirty three years ago. That's
pretty impressive. You've been working with

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lots of different customers and you got
thirty three bots. I think you said

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thirty three or thirty five each,
thirty five each two different things, and

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a lot of people are now figuring
out what this jenai stuff is all about.

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And it's not just being able to
spin up fun, little creative articles.

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There are lots of other things it
does. Summarizing is one of the

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amazing things that it does. You
can take a big, long paper,

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feed it into one of these engines
and say give me a two page summary,

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and bam, it does. I
actually did a fairly complex ETL job

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on Friday, scraping a bunch of
content from the web and then using chat

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GPT, actually using bard by Google
to create an ordered list, and it

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was like magic. It would have
taken a couple hours for a good ETL

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person took me five minutes with these
new tools. So there are very cool

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tools fueled by AI that are changing
the game. And I'm excited to bring

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Andy Roberts now in from Sabio Group. Andy, you had some technical difficulties,

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but we've got that solved. So
I always blame the Russians for hacking.

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By the way, I'm pretty sure
it's their fault. But Andy,

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tell us about your perspective on contact
center or call center as a service and

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how it changes the game. Okay, good Athian everybody. I'm calling in

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from London. It's actually not raining. It's beautiful out there at the moment.

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I can see some pools over my
shoulder. So yeah, I'm at

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Saber twenty five years, so not
quite as much as Dan, but we've

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been working with Variants since two thousands
and been on the journey with them.

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Sabby are a specialist services, expert
services company that really absolutely understand how to

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be able to deploy the technology and
to be able to deliver return on investment

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and excellent customer experience. Everyone here
gets up every morning trying to make the

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customer experience brilliant. As far as
the journey is concerned around contact centers call

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centers, the journey is quite an
evolving one and it's going at pace.

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The relationship that we have with Variants
going back twenty years was very much around

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on premise contact centers with all of
the workforce engagement management technology that was on

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top of it. What we saw
probably about seven or eight years ago was

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a move to cloud, and certainly
the move to public cloud has really accelerated,

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and that's where the SEACASS development and
be able to move to the public

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cloud has really gained pace. And
what we've seen is that organizations of delivering

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and moving to the cloud at different
speeds. Digital native organizations are moving very

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quickly and larger scale enterprises are moving
a little bit slower. As far as

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open seacats is concerned. What we're
able to see is we're able to see

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the ability to be able to not
just move to the cloud, but it's

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actually to be able to augment,
to be able to bring other parts of

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the customer experience to life. And
that is the whole of the variant portfolio

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quality monitoring, speed, channeltics,
workforce management, but also all of the

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AI components as well. And Tabby
have had a relationship with automation as long

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as we've been in existence. It
started off with IVRs and it moved to

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natural language speech recognition, and more
recently it's been around conversational AI. So

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when we actually start talking about the
ability to be able to make a difference

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for the end customer, there's one
key thing that I think is important,

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and that's about the appropriate service at
the right time. So we've all experienced

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issues with automation when the experience has
been poor and I think you're smiling there,

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Eric and you're thinking, oh,
I can remember that time. So

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actually looking at how you want,
how organizations want to ensure that they provide

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the experience that's really the right time
for the right type of transaction is the

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heart of what we're looking to try
and do, and the whole of the

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variant portfolio allows us to be able
to do that. But it's about thinking

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about the right transactions to be able
to automate, and those where you need

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a live agent, being able to
have the right context, the right information

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to be able to delight the end
customer. Yeah, that's all really important.

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And you hit one nail squarely on
the head there, which is knowing

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what to automate, how to automate, and when to automate. And of

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course you always want the manual override
correct, like when things are going bad,

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you want to be able to pull
the handle and slow it down and

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bring in the agents, and that
all really goes back to the data and

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the architecture too. Do you want
to talk a bit about the architecture of

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these solutions, because as you said, you started off on prem. Now

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I'm sure heavily in the cloud,
but I think on prems legacy will live

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on for a long time. As
they say, the rumors of on Prems

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demise have been somewhat exaggerated. But
tell us how the architecture is changing from

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your perspective. I'll give you an
example of a very large mobile provider in

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Europe and the architecture that they have. They've got an on prem architecture and

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they've but as far as all of
their AI and automation, that's done in

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the cloud and that is utilizing well. So this specific provider is he's been

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able to automate fifty nine percent of
all the traffic. Now that is a

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total of thirty six million calls a
year are being automated. But the key

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thing about that is being able to
drive the net promoter score up. So

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over the same period they've increased their
net promoter score by twenty seven points.

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So being able to look at the
two things in conjunction, I think is

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really important. The other thing you
mentioned, which is around being able to

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migrate to the cloud at the right
time, I think is really important.

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What we've seen is people moving to
the public cloud, but they're replacing a

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lot of the technology that was on
premise, and they're replacing it on the

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cloud without getting any of the real
benefits. And I really think that the

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types of solutions that Varrant and Sabio
are looking to try and put together is

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being able to actually extract the value
for the enterprise customers that we work with.

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But we've got to think about two
major components and two sets of populations

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as we do. So. The
first one is the end customer. So

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it's about having user centered design engineers
sitting on the customer side of the fence

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to actually sit in your shoes when
you're actually engaging with an insurance company,

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an airline, or a utility.
And then secondly, it's about having speech

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scientists about being able to tune the
box and being able to tune the AI

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engines to allow us to be able
to maximize the operational run for customers.

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And what we're seeing is that that's
where the next phase of automation and workforce

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engagement management is sitting in that space. Yeah, we're really going to see

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a lot of change I think over
the next probably two to five years in

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terms of who's doing what in organizations. And I think the real success factor

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is going to be getting team members
who are willing to use the new technologies,

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who are willing to embrace new ways
of doing things as opposed to fearing

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the use of these technologies and trying
to work around them. What do you

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think? Yeah? Absolutely, And
we were talking a little bit at one

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of the management meetings I was in
this morning. Dan spoke about summarization and

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being a really really good benefit from
a generative AI perspective, and I agree

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totally. But there's also auto translation, which is incredibly useful, especially in

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Europe. But also there was a
really good use case, which is when

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somebody sends her an email. Typically
the SLA on an email is one day,

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two days. So if a customer
ends up sending through an email and

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wants to cancel their hotel booking for
two days time and they send it by

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email, that might not get looked
at. However, we've designed a use

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case specifically for this for one of
our travel customers, and specifically, what

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I was looking to do is to
be able to understand the email that comes

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through that if the data cancelation was
the twenty ninth of November. It actually

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uses the knowledge that's in the email
to be able to root it directly answer

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the question, wow, present an
email and go back to the customer.

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But we could also have human intervention
into that where you could actually root that

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to a live agent. Now I
believe that'd be amazing customer experience. And

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that's the type of way in which
we can use I believe generative AI to

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really really move the dial. Yeah, that's amazing. I mean really you

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think about it. The power of
AI, the power of analytics, the

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power of automation, these things come
together with appropriate data. Of course,

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a baseline of data is requisite.
I mean, you can use adversarial models.

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I don't know if we'll have time
to get into all that, but

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to be able to capture to receive
the email, understand the syntax, the

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key messages inside there, and then
take action to respond to that. Wow,

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that's what we've been hoping for,
like the last twenty thirty years,

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right, absolutely, And if you
think about that recording starting from progress.

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Sorry I was trying to hit mute
there hit the wrong button. Go ahead.

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So if you think that from an
agent perspective, if you're actually having

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the message being typed for you and
suggesting you're just checking, you know,

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I think that reduces repetitive work and
it allows you to really go and help

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the customer, which is what we're
all here to do. Yeah, I

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mean you think about the different component
parts coming together and being able to serve

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the purpose of helping out the client
and also have a record such that when

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the person who works at the company
comes and run and checks in on that,

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they can see what happened, they
can score. And that's really the

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curation side of this whole storyline,
right, is being able to monitor.

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I've seen some of these really good
tools lately, monitor what the GENAI is

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coming up with and being able to
curate that and say yes, no,

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or maybe tweak this a little bit
until you get it right, and then

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you let the automation do its thing. Right. Absolutely, and I think

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that you know, the individual that
was demonstrating this was from Glasgow, all

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right, and there's he actually was
able to get personality. So we saw

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the first version, which was a
very polite response, but when he actually

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changed it and said could you have
it in glass region all right so you

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can pick it literally, you had
a completely different email that was being written,

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which took which put real personality into
the response, which I think for

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various different for certain companies and certain
populations. I think it's a superb way

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of being able to give the box
some real identity. Yeah, that's that's

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also amazing that they can pick up
on dialects and different ways of phrasing things

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and then sort of reflect back to
the user what they wanted to hear in

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their own language. That makes people
feel comfortable. I mean, we heard

401
00:31:00.319 --> 00:31:03.519
Dan talking about it earlier. This
term delight It is delightful if you feel

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00:31:03.839 --> 00:31:08.160
you've been heard, if you feel
that your situation has been handled properly,

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that's when you're a happy customer.
That's when you come back. And that's

404
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the whole objective of customer experience,
right Andy, Absolutely, absolutely, you

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00:31:18.440 --> 00:31:22.440
want to ensure that when an organized
when somebody is interacting with a company,

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it's an appropriate service that dealt with
quickly, swiftly. Twenty four to seven

407
00:31:27.759 --> 00:31:33.599
through automation where it's simple and it
needs to be relatively easy to use.

408
00:31:33.839 --> 00:31:37.920
But when it is a nine to
nine to nine call, your car's broken

409
00:31:37.960 --> 00:31:41.720
down, you need road side assistance, you're in distress. Sentiment analysis was

410
00:31:41.720 --> 00:31:45.200
mentioned by Dan earlier on all these
things. You want to if somebody is

411
00:31:45.200 --> 00:31:49.240
getting angry, getting frustrated, you
want to try and diffuse the situation,

412
00:31:49.640 --> 00:31:52.400
and you want to be able to
use the appropriate service, which quite often

413
00:31:52.480 --> 00:31:56.759
is a live service. Yeah,
and we should point out too, it's

414
00:31:56.799 --> 00:32:02.119
not just humans or bots, it's
really humans with bought assistance or augmentation,

415
00:32:02.359 --> 00:32:06.240
right, Because you're going to have
a human on the call with someone,

416
00:32:06.599 --> 00:32:09.720
but the AI engine is in the
background listening to things and can figure something

417
00:32:09.720 --> 00:32:13.960
out and then boom, make a
suggestion or you know, come out and

418
00:32:14.000 --> 00:32:16.359
say, hey, offer this offer. That the idea is that you get

419
00:32:16.400 --> 00:32:22.039
this team basically working with you as
a call center operative talking to the customer,

420
00:32:22.119 --> 00:32:25.759
and behind the scenes, all these
little bots are doing their thing to

421
00:32:25.799 --> 00:32:30.440
give you advice and to help you
along the way. Right. Yeah,

422
00:32:30.480 --> 00:32:34.160
But being able to provide assisted service
and being able to take out any after

423
00:32:34.240 --> 00:32:37.559
cool work, being able to ensure
that the repetitive task for the agents as

424
00:32:37.559 --> 00:32:43.799
well is being reduced. All these
things will make people's life much better.

425
00:32:44.240 --> 00:32:46.720
Yeah, I love it. Maybe
we'll bring Dan back in here to comment

426
00:32:46.799 --> 00:32:50.160
on some of this stuff too.
Dan, you know, when I think

427
00:32:50.160 --> 00:32:54.799
about the coalescing of these different technologies
in different workflows, once again, if

428
00:32:54.799 --> 00:33:00.200
you have the data underneath to map
it, we're dealing with a completely different

429
00:33:00.960 --> 00:33:04.599
environment than mean were just two or
three years ago. What do you think,

430
00:33:04.680 --> 00:33:08.519
Dan, Yeah, you know,
we focused on the bots that are

431
00:33:08.519 --> 00:33:13.839
helping the agent in real time,
but we have boughts that are helping other

432
00:33:13.920 --> 00:33:17.519
personas in the connectenter for example,
the compliance team, right, the people

433
00:33:17.559 --> 00:33:22.039
that their job is to make sure
that all the interactions are in compliance,

434
00:33:22.799 --> 00:33:27.960
especially in healthcare and financial services.
And these people what they need to do

435
00:33:28.119 --> 00:33:31.960
is actually they need to sample a
small number of calls and randomly listen to

436
00:33:31.960 --> 00:33:37.519
this calls to make sure that all
the mandatory statements have been made and the

437
00:33:37.519 --> 00:33:43.119
call is basically compliant. And you
know there's sometimes you know, there's industries

438
00:33:43.160 --> 00:33:45.799
where you have to make the mandatory
statement within the first thirty thirty seconds of

439
00:33:45.839 --> 00:33:51.559
the call, otherwise you ought of
compliance. So our compliance bots will actually

440
00:33:51.599 --> 00:33:55.000
listen not just to a random sample, but compliance what we'll we'll listen to

441
00:33:55.079 --> 00:34:00.880
one hundred percent of the call automatically
fine noncomp line's issues. So it really

442
00:34:00.920 --> 00:34:06.119
is another bought that helps the compliance
t The bots are helping the supervisors,

443
00:34:06.160 --> 00:34:10.239
the bots that helping managers to make
better decisions. And so when you think

444
00:34:10.280 --> 00:34:15.079
about all these bots helping different roles
around the conduct center, now you can

445
00:34:15.119 --> 00:34:20.199
get really the whole machine to do
what end said right, which is to

446
00:34:20.280 --> 00:34:25.079
focus on how do we actually do
the better job for our end customers.

447
00:34:25.079 --> 00:34:29.880
And it's always in the same budget
and resources. Yeah, that's a really

448
00:34:29.920 --> 00:34:32.719
good point too. And as I've
learned over the years, these bots,

449
00:34:32.719 --> 00:34:36.480
they do have to have very specific
tasks. I mean, I'm sure we're

450
00:34:36.480 --> 00:34:39.440
going to be getting better with these
things to where they can be more versatile

451
00:34:39.480 --> 00:34:43.800
and do different things, but at
the point in time we are right now,

452
00:34:44.119 --> 00:34:45.760
you want them to do very specific
things and to do them well and

453
00:34:45.840 --> 00:34:49.480
to measure all of that, and
folks, don't touch that down. Will

454
00:34:49.480 --> 00:34:59.519
be right back. You are listening
to Inside Analysis. Welcome back to Inside

455
00:34:59.559 --> 00:35:07.119
Analysis. Here's your host, Eric
Kavanaugh. All right, folks, back

456
00:35:07.119 --> 00:35:12.760
here on Inside analysis talking to a
couple experts in customer experience. These are

457
00:35:12.760 --> 00:35:15.920
the folks behind the scenes who make
the experience good. And I promise see

458
00:35:15.920 --> 00:35:19.960
there are many many other folks who
are working on this stuff. They're writing

459
00:35:20.000 --> 00:35:24.079
code, they're building systems, they're
checking things, validating, they're curating.

460
00:35:24.119 --> 00:35:28.800
That's we talked about a moment ago. That's one of the big functions in

461
00:35:28.840 --> 00:35:32.000
the modern world. And you know, really, this Jenai stuff is amazing.

462
00:35:32.000 --> 00:35:36.440
So I'll ask each of you to
kind of comment on how Jenai plays

463
00:35:36.440 --> 00:35:40.320
a role in customer or call center
as a service, as a technology space.

464
00:35:40.960 --> 00:35:45.000
I've been playing around a lot with
these Genai tools, and I had

465
00:35:45.000 --> 00:35:47.400
the CTO of Boomy on the show
a couple of weeks ago, and he

466
00:35:47.480 --> 00:35:51.360
made a really good point, asked, and what's the most important aspect do

467
00:35:51.400 --> 00:35:54.119
you think about this nu machine learning
technology, this AI? And he said

468
00:35:54.559 --> 00:35:59.400
learning and he meant human learning.
And so Dan, I'll throw it over

469
00:35:59.440 --> 00:36:02.079
to you first. You know,
we as humans, we need to understand

470
00:36:02.159 --> 00:36:07.239
this is like a new kind of
surfboard or roller blade. It's a new

471
00:36:07.280 --> 00:36:10.440
way of doing things that has not
really been around it to any great extent

472
00:36:10.719 --> 00:36:14.440
in the past. So it takes
a lot of practice just to play around

473
00:36:14.480 --> 00:36:15.559
with it and see how it operates. What do you think, Dan,

474
00:36:17.400 --> 00:36:22.039
You know, we we spoke before
about some bots of the system agents in

475
00:36:22.079 --> 00:36:27.920
real time, and you know,
one of the important things in real time

476
00:36:27.960 --> 00:36:31.239
assists is that the boss will be
really, really accurate, because agents don't

477
00:36:31.280 --> 00:36:37.239
want to be disrupted by bots of
giving them some stupid advice. So and

478
00:36:37.280 --> 00:36:42.159
I remember no longer, you know, three years ago, we've sold some

479
00:36:42.199 --> 00:36:46.239
systems with AI that certain agents just
turn them turned them off because they said,

480
00:36:46.239 --> 00:36:49.480
no, we don't need that.
You know, we know better than

481
00:36:49.480 --> 00:36:52.519
the bot, right, and that
disrupting me with some coaching that actually is

482
00:36:52.519 --> 00:36:59.719
not helpful. So key to to
UH agent assist is is that the boss

483
00:36:59.760 --> 00:37:01.599
will be really accurate. And the
only way they can become accurate is if

484
00:37:01.599 --> 00:37:07.840
they train on the right data.
So when you develop an AI model,

485
00:37:07.119 --> 00:37:12.840
obviously you need data that's the initial
training, and that initial data can be

486
00:37:13.199 --> 00:37:16.000
captured and it's a limited amount of
data and you can create an AI model

487
00:37:16.079 --> 00:37:21.840
that works, But in order to
be really accurate, you really need the

488
00:37:21.880 --> 00:37:24.480
boss to continue to train on fresh
data. And that's twenty four to seven

489
00:37:24.559 --> 00:37:30.800
training in the gym on fresh data. Because the data changes, the discussions

490
00:37:30.800 --> 00:37:36.239
between the customers and the consumers and
the agents are actually changing because you know,

491
00:37:36.280 --> 00:37:38.480
if you are from a circle company
and you announce a new job,

492
00:37:38.639 --> 00:37:44.480
obviously the conversation about the side effects
will be different than new So it's not

493
00:37:44.559 --> 00:37:49.960
just static data. It has to
be fresh data. And that's why this

494
00:37:50.079 --> 00:37:57.159
boss need to live in a platform
where the data is being constantly collected into

495
00:37:57.199 --> 00:38:00.119
a single hub, where it's all
unified and available for the bots to get

496
00:38:00.119 --> 00:38:04.480
trained and get better and better all
the time until the point that they really

497
00:38:04.519 --> 00:38:07.199
are doing what they're supposed to do
very well. And you know, Eric,

498
00:38:07.280 --> 00:38:12.159
you mentioned before the break that maybe
over time bots will be able to

499
00:38:12.159 --> 00:38:15.199
do more and more and more.
But you know, if bots can really

500
00:38:15.239 --> 00:38:19.679
do one thing and do them well, that's okay because then you can create

501
00:38:19.679 --> 00:38:24.480
another bot to do something different and
again make sure that that new bot is

502
00:38:24.599 --> 00:38:29.760
very good at what they do.
So the approach we take it variant is

503
00:38:29.800 --> 00:38:32.280
we have today thirty five bots,
and you know, we said we're going

504
00:38:32.320 --> 00:38:36.599
to have another fifteen bots in the
next couple of months, so we're creating

505
00:38:36.599 --> 00:38:39.360
a lot of bots quickly. Because
the data is only did the platform,

506
00:38:39.480 --> 00:38:44.519
and we bring new AI morals,
not just at variant development. We bring

507
00:38:44.559 --> 00:38:47.239
it from any company in the world
that creates new including a lot of open

508
00:38:47.239 --> 00:38:52.760
source. We bring that new AI
model and we train them on our unique

509
00:38:52.840 --> 00:38:57.599
data, this behavioral data, and
then very quickly we turn them into something

510
00:38:57.639 --> 00:39:02.639
that is really helpful to automate the
single test and our approaches want tusk of

511
00:39:02.719 --> 00:39:07.920
the time, you can make the
whole conneccenter be really really better. Yeah,

512
00:39:07.079 --> 00:39:10.519
division of labor, right, so
you have different bots to do different

513
00:39:10.559 --> 00:39:14.639
things. And to your point,
and maybe I'll throw this one over to

514
00:39:14.719 --> 00:39:19.840
Andy, situations do change. I
know one of the things that we learned

515
00:39:19.960 --> 00:39:23.400
during the COVID period of time is
that many of the models that were doing

516
00:39:23.519 --> 00:39:29.679
very well before COVID did not do
well after COVID because behavior changed, because

517
00:39:29.679 --> 00:39:32.280
people stayed home a lot more,
they were buying stuff online. The whole

518
00:39:32.679 --> 00:39:37.920
humanity out there changed behavior significantly,
and that required a lot of effort from

519
00:39:38.000 --> 00:39:44.960
developers and from companies like yourselves to
be able to re understand and reconfigure and

520
00:39:45.000 --> 00:39:47.719
sort of realign what the algorithms we're
doing. What do you think about that?

521
00:39:47.800 --> 00:39:55.760
Andy. So as far as the
contact center and customer experience, all

522
00:39:55.800 --> 00:40:00.559
the data that it's been talked about
by Dan say that the contact center becomes

523
00:40:00.559 --> 00:40:05.679
the eyes and ears of the organization. If you think about the amount of

524
00:40:05.800 --> 00:40:10.039
data that comes in through all the
relevant channels, and you think about the

525
00:40:10.079 --> 00:40:15.920
sentiment analysis, and you think about
the areas where customers are really excited or

526
00:40:15.000 --> 00:40:20.159
really frustrated, what you have is
and this is through the contact center,

527
00:40:20.239 --> 00:40:24.519
it's through all the workforce engagement platforms
CRM and everything that's captured by AI.

528
00:40:24.679 --> 00:40:30.199
There's an enormous amount of data.
So when you start talking about how to

529
00:40:30.280 --> 00:40:36.679
be able to look at generative AI
data is the new goal, all right,

530
00:40:37.239 --> 00:40:40.360
So being able to ensure that you
can access that data and mine it

531
00:40:40.440 --> 00:40:45.480
for the information that you're talking about
about when things happen with COVID and when

532
00:40:45.559 --> 00:40:50.159
people's working patterns change, you can
see it happening through the data that's collected

533
00:40:50.199 --> 00:40:52.840
and in real time. Yeah,
and you make a really good point about

534
00:40:52.840 --> 00:40:58.320
the quality of data and the context, the appropriate context of data. So

535
00:40:58.800 --> 00:41:00.800
one thing that I think a lot
of people learning is that these large language

536
00:41:00.800 --> 00:41:06.519
models like CHADGBT and BARD, they
were trained on the corpus of data that

537
00:41:06.639 --> 00:41:10.440
was vast and really whatever was available
on the web. Twitter. Elon Musk

538
00:41:10.559 --> 00:41:15.960
was talking about how he did rate
limiting because they realized that people were scraping

539
00:41:15.159 --> 00:41:21.000
massive amounts of data from Twitter in
order to ascertain trends and then come up

540
00:41:21.039 --> 00:41:25.639
with models to train them around particular
topics, etc. The more focused that

541
00:41:25.760 --> 00:41:30.360
data is, the better a chance
you're going to get of this spot doing

542
00:41:30.400 --> 00:41:32.800
the right thing. If you have
very noisy data, if you have data

543
00:41:32.840 --> 00:41:36.239
of all sorts of different things,
it's going to be a lot harder.

544
00:41:36.280 --> 00:41:39.840
It's kind of like a It really
is kind of like raising a child over

545
00:41:39.960 --> 00:41:44.519
time. Like if you have a
good structured environment and you're good to your

546
00:41:44.599 --> 00:41:47.400
child and you're caring, you're going
to have good results. But if the

547
00:41:47.719 --> 00:41:52.079
child lives in a chaotic environment,
there's all kind of weird bad things happening,

548
00:41:52.119 --> 00:41:54.400
all that is going to reflect through
the child's eyes at some point in

549
00:41:54.480 --> 00:41:58.199
time. The same is true for
these bots, So you want to be

550
00:41:58.280 --> 00:42:01.800
very careful about how you train them, which data you give it access to,

551
00:42:02.199 --> 00:42:06.199
and that's really important stuff. I'll
throw that over to to day and

552
00:42:06.239 --> 00:42:09.599
to comment on what do you think
there, Yeah, you need to unified

553
00:42:09.679 --> 00:42:14.960
data hub. You need to bring
all these data, whether you physically bring

554
00:42:15.000 --> 00:42:17.400
it or you just link to the
data. But all that data that is

555
00:42:17.440 --> 00:42:24.800
behavioral data that is being captured by
recording if you will, the human agents

556
00:42:24.920 --> 00:42:30.360
talking to real customers, that all
that data they could be talking, could

557
00:42:30.360 --> 00:42:32.280
be chatting, could be social media, right, it could be surveys.

558
00:42:34.079 --> 00:42:37.559
All that data, all the interaction
data, is the behavioral data that you

559
00:42:37.679 --> 00:42:44.559
need access for the bots. And
and and if if you only have partial

560
00:42:44.639 --> 00:42:47.960
data or you have noising data,
yes you're going to get partial results or

561
00:42:49.079 --> 00:42:52.639
just bad results. Right. You
don't want bad results. You don't want

562
00:42:52.679 --> 00:42:55.360
your bots annoying people. That's pretty
much the last thing you want these things

563
00:42:55.440 --> 00:43:00.079
to do. You want them to
be understanding a situation and being able to

564
00:43:00.239 --> 00:43:04.159
act in a particular environment. And
that takes time. You curate it over

565
00:43:04.280 --> 00:43:07.360
time. I would tend to think, right, you deploy a bot for

566
00:43:07.400 --> 00:43:09.880
a particular customer, and you want
to be able to have this unified data

567
00:43:09.920 --> 00:43:14.000
hub so you can train that butt
in their environment so you can learn.

568
00:43:14.519 --> 00:43:16.400
And we get back and real quick, I throw this over to Andy.

569
00:43:16.679 --> 00:43:22.360
We get back to training it on
data relevant for the company and for the

570
00:43:22.559 --> 00:43:25.199
use case, right, and using
your data, your historical data, and

571
00:43:25.239 --> 00:43:29.599
then figuring out who are the good
call reps, who are the people who

572
00:43:29.599 --> 00:43:32.760
have good reports and good results.
Let's train the data, train the bots

573
00:43:32.800 --> 00:43:37.199
on the data from those calls,
not the data from the bad calls.

574
00:43:37.280 --> 00:43:40.440
Right, Andy, Yeah, I
think that two points. The first one

575
00:43:40.480 --> 00:43:45.360
is good prompt engineering, I think
is really really important. Being able to

576
00:43:45.480 --> 00:43:52.760
accurately present the right prompt to get
the right outcome is absolutely essential. I

577
00:43:52.800 --> 00:43:58.559
also think that as far as being
able to provide the best customer experience,

578
00:43:59.400 --> 00:44:04.440
the way to be able to get
the box that Dan's talking about is spend

579
00:44:04.519 --> 00:44:08.119
time with customers, understand their problems
and to be able to understand what the

580
00:44:08.199 --> 00:44:13.480
good agents do, and then trying
to replicate that with being able to use

581
00:44:13.519 --> 00:44:17.800
the data that's been collaptured from all
the various different sources to allow us to

582
00:44:17.840 --> 00:44:24.920
be able to ensure that we're delivering
the best possible outcomes to automating utilizing the

583
00:44:24.960 --> 00:44:30.280
best behaviors that have come from from
those agents. So you go ahead,

584
00:44:31.840 --> 00:44:36.440
we talk about best practices, right, We've talked about best practices for like

585
00:44:36.519 --> 00:44:38.599
forty fifty years, probably longer than
that. But what you want now is

586
00:44:38.639 --> 00:44:43.719
for those best practices to be codified. And what's a beautiful thing is you

587
00:44:43.760 --> 00:44:46.199
can score all these behaviors. You
can score them, you can understand.

588
00:44:46.199 --> 00:44:50.519
We have net promoter score that we
understand. There are metrics that you use

589
00:44:50.599 --> 00:44:52.639
to be able to gauge where you're
getting. This happens all the time in

590
00:44:52.719 --> 00:44:57.360
call centers. We want to reduce
customer return by five percent, we want

591
00:44:57.400 --> 00:45:02.760
to increase engagement by five percent at
targeted, metric driven goals, and then

592
00:45:02.800 --> 00:45:07.480
you work toward those goals and that
works very very well. I can tell

593
00:45:07.480 --> 00:45:10.639
you that pretty much anyone in the
working world, if you have a job

594
00:45:10.719 --> 00:45:14.800
somewhere, if you know what to
do and how to do it, that's

595
00:45:14.800 --> 00:45:16.920
the first key to success. And
then if you're measured on what you do,

596
00:45:17.000 --> 00:45:22.679
and you're measured honestly and transparently,
that's wonderful stuff. I mean,

597
00:45:22.760 --> 00:45:24.840
sporting folks can do that all the
time. They know when they get a

598
00:45:24.840 --> 00:45:28.199
first down, they know when they
get a touchdown. It's pretty obvious,

599
00:45:28.480 --> 00:45:30.559
and in the business world it's not
quite as obvious, but it's still pretty

600
00:45:30.559 --> 00:45:35.079
clear. When you get a good
customer engagement. You feel good about that.

601
00:45:35.079 --> 00:45:37.840
That's what people want, That's what
everyone wants. That's the key to

602
00:45:37.960 --> 00:45:42.199
exceptional customer experience. Folks who've been
talking to Andy Roberts and Dad Bonder.

603
00:45:42.239 --> 00:45:45.679
What a couple of great experts you've
been listening to Inside a oaus. All

604
00:45:45.719 --> 00:45:49.039
right, folks, time for the
podcast bonus segment here on Inside Analysis.

605
00:45:49.039 --> 00:45:52.480
What a fun show talking to Andy
Roberts of Savio Group and Dan Bodner of

606
00:45:52.760 --> 00:45:55.760
Variant, the CEO and co founder. And Andy, you've got a fun

607
00:45:55.760 --> 00:46:00.159
story about a very large insurance company
and the CECAST in particular, which is

608
00:46:00.480 --> 00:46:05.679
call center as a service. Go
ahead and tell that story. It's a

609
00:46:05.719 --> 00:46:08.639
little bit unusual. So it's a
customer that we've actually been working with for

610
00:46:08.679 --> 00:46:15.679
a long period of time, specifically
around automation. Anyway, they had a

611
00:46:15.760 --> 00:46:20.480
five day outage. It's a global
insurer, a five day outage, which

612
00:46:20.519 --> 00:46:25.360
was pretty traumatic for them. We
were able to spin up a seacast platform

613
00:46:25.440 --> 00:46:32.199
from scratch in twenty four hours,
all right, and it literally they had

614
00:46:32.239 --> 00:46:37.960
a huge issue. It was in
the US and in the EU, and

615
00:46:38.239 --> 00:46:44.159
we were able to take four and
a half thousand calls in the first day,

616
00:46:44.639 --> 00:46:49.039
six hundred agents. When people talk
about the ability to be able to

617
00:46:49.119 --> 00:46:53.239
look at seacasts as an environment,
now this was a very basic, you

618
00:46:53.280 --> 00:46:59.000
know, dr as a service solution
that we put in, but we were

619
00:46:59.039 --> 00:47:01.360
looking on the customer side of things
and being able to say, right,

620
00:47:01.400 --> 00:47:06.199
you need you need telephony, and
you need it really really quickly. The

621
00:47:06.239 --> 00:47:10.679
guys basically took the solution overnight and
we're able to replicate it back and give

622
00:47:10.719 --> 00:47:16.920
it to the client. Now that
is how quickly a seacast solution can be

623
00:47:17.000 --> 00:47:22.119
deployed. However, what you then
need to be able to overlay is all

624
00:47:22.159 --> 00:47:30.400
the really really interesting technology which allows
agents to be effective. It allows for

625
00:47:31.599 --> 00:47:37.199
the customers to be able to maximize
customer experience. They need to look all

626
00:47:37.239 --> 00:47:40.800
the automation engines at the top as
a site. But as far as being

627
00:47:42.000 --> 00:47:45.920
the speed of which things can be
deployed in the SEACAS public cloud environment,

628
00:47:45.480 --> 00:47:50.760
that that's an example of how things
have changed enormously. Now, is it

629
00:47:50.800 --> 00:47:53.119
going to be able to offer a
world class service. No, but what

630
00:47:53.199 --> 00:47:58.920
it can do is it can change
things rapidly to be able to improve customer

631
00:47:59.000 --> 00:48:05.000
experience. Yeah, that's a fantastic
story that just amazing stuff. Herculean effort

632
00:48:05.079 --> 00:48:07.599
is I guess what I would say, and you know, Dan, I'll

633
00:48:07.639 --> 00:48:12.119
bring you in for some final comments
here. You know what really excites me

634
00:48:12.199 --> 00:48:19.400
about all this stuff is the ability
for these engines to identify success and failure

635
00:48:19.440 --> 00:48:22.119
and even just interesting stuff. I
mean, you work with a Jenai tool,

636
00:48:22.159 --> 00:48:25.440
you can say, give me the
most interesting three parts about this twenty

637
00:48:25.480 --> 00:48:30.039
page paper. It will go find
interesting stuff. And I've been surprised at

638
00:48:30.039 --> 00:48:32.599
what a good job it does.
So my point is that you know,

639
00:48:32.679 --> 00:48:38.480
for so many years now in business
management, in sales and marketing, we've

640
00:48:38.559 --> 00:48:42.559
kind of been going by gut instinct. I mean, we'll use data from

641
00:48:42.639 --> 00:48:45.559
dashboards and different things like that.
But I think what we're seeing right now

642
00:48:45.719 --> 00:48:50.159
is a see change, is an
inflection point, It's a j curve,

643
00:48:50.239 --> 00:48:52.719
whatever you want to call it,
where all of a sudden we can leverage

644
00:48:52.760 --> 00:48:59.360
the power of real world data at
scale to understand what are people really buying,

645
00:48:59.360 --> 00:49:02.199
what are people we're really excited about. What does make for a successful

646
00:49:02.199 --> 00:49:06.599
phone call? We had to guess
for many years we had a pretty good

647
00:49:06.639 --> 00:49:09.239
idea, but not like we can
do today. When you can when you

648
00:49:09.239 --> 00:49:15.840
can capture, analyze, measure,
report, and then optimize that whole cycle

649
00:49:15.360 --> 00:49:19.960
is really powerful these days. And
if you have this unified data hub,

650
00:49:20.000 --> 00:49:22.079
as you've suggested, you're kind of
off to the races. What do you

651
00:49:22.079 --> 00:49:25.280
think about all that, Dana?
So, yeah, that the data is

652
00:49:25.320 --> 00:49:30.599
the key, and the bots that
are working and using this data to deliver

653
00:49:30.679 --> 00:49:36.599
business housecomes. I think that's what's
really excite our customers. So you know,

654
00:49:37.480 --> 00:49:39.400
bots are not just helping people,
and we're now at the point already

655
00:49:39.400 --> 00:49:46.320
where bots are helping bots and and
I'll give you an example. So when

656
00:49:46.400 --> 00:49:49.960
you call, let's say you want
to change the quality on your order,

657
00:49:50.039 --> 00:49:53.920
and the containment bots will be able
to say, yes, mister Cavanaught took

658
00:49:53.960 --> 00:49:57.159
care of it, so you're done. But then if you want to change

659
00:49:57.159 --> 00:50:01.639
your payment schedule, and the containment
ball will actually, I'll have to transfer

660
00:50:01.679 --> 00:50:07.360
you to the proper agent. At
that point, the transfer bot is coming

661
00:50:07.400 --> 00:50:10.760
to help the containment bot and then
transfer the call with all the right context

662
00:50:12.000 --> 00:50:15.280
so the agent does not have to
authenticate you again, does not have to

663
00:50:15.320 --> 00:50:17.320
say how can I help you?
Right, you can go back to the

664
00:50:17.360 --> 00:50:22.199
point and say, oh, so
that you're trying to shag your payment schedule.

665
00:50:22.320 --> 00:50:25.639
Let me help you death at that
point. So all these bots now

666
00:50:25.719 --> 00:50:30.800
working on the data, as you
said, and fitting from the same data,

667
00:50:30.880 --> 00:50:35.480
and understanding the context and the flow
of your call, of your prior

668
00:50:35.559 --> 00:50:40.360
calls, of your history, all
that coming together now in a platform that

669
00:50:40.480 --> 00:50:45.760
can really eventually increase the extoltomation,
which the industry has been looking to do

670
00:50:45.840 --> 00:50:50.480
for many, many years. Yeah. Well, there are so many tedious

671
00:50:50.559 --> 00:50:53.679
tasks involved in any job. And
if you can get these bots to hammer

672
00:50:53.719 --> 00:50:59.519
away at the tedious things you achieve, and I'll make this the final point

673
00:50:59.519 --> 00:51:02.119
I get each of your comment on
it, you will improve morale in your

674
00:51:02.239 --> 00:51:07.000
organization. I believed for a long
time now that morale is the single most

675
00:51:07.000 --> 00:51:10.079
important characteristic of any organization, because
when morale is high, good things happen.

676
00:51:10.320 --> 00:51:13.760
When morale is low, you can
have all the money, tools,

677
00:51:13.800 --> 00:51:16.159
the best people, and good things
are not going to happen because morale is

678
00:51:16.320 --> 00:51:22.400
down. And these bots, when
orchestrated effectively and efficiently, First to Andy

679
00:51:22.440 --> 00:51:24.440
and then Dan, just comment on
this, that's going to do wonders for

680
00:51:24.559 --> 00:51:29.360
morale, That does wonders for customer
service. What do you think Dan or

681
00:51:29.400 --> 00:51:34.400
first, Andy, go ahead.
I totally agree. If you're sitting in

682
00:51:34.400 --> 00:51:39.079
a contact center environment, you want
to have a really really strong agents are

683
00:51:39.119 --> 00:51:45.320
motivated. If you look at any
reports that comes out around the biggest issues

684
00:51:45.360 --> 00:51:51.119
that customers experience for the contact center
world, it will be being able to

685
00:51:51.159 --> 00:51:54.519
retain quality agents. Yea, the
hardest thing to do, all right.

686
00:51:54.719 --> 00:51:59.679
You'll see reports, you know,
eighteen ninety percent CEOs will say that the

687
00:51:59.679 --> 00:52:05.280
big issue they have is with training, with attracting and retaining quality people.

688
00:52:05.440 --> 00:52:10.719
If you're able to remove the boring, repetitive taskt then you're on the right

689
00:52:10.760 --> 00:52:16.000
path to be able to move forward
and give them more interesting work. They

690
00:52:16.039 --> 00:52:19.880
want to get out of bed every
morning, and to be able to excite

691
00:52:19.920 --> 00:52:23.400
customers and have happy customers at the
end rather than grumpy ones that complain.

692
00:52:23.880 --> 00:52:28.079
That's right. Good help is hard
to find. To be keep them around,

693
00:52:28.079 --> 00:52:30.280
you're going to be in really good
shape. Final comments from Dan Bartner

694
00:52:30.320 --> 00:52:37.000
a variant. So yeah, so
ex employee experience is the driver for customer

695
00:52:37.000 --> 00:52:40.480
experience. And since today I'm talking
about bots, i'll give you the latest

696
00:52:40.519 --> 00:52:45.119
part that we're just announced, and
that's a time flex spot. So if

697
00:52:45.119 --> 00:52:50.440
you're an agent and your child is
sick and you need to get them to

698
00:52:50.480 --> 00:52:52.639
the doctor. You call your supervisor
and you say I need to get out

699
00:52:52.679 --> 00:52:59.199
of the shift, and the supervisor
will say, nope, cannot do it.

700
00:52:59.280 --> 00:53:04.320
We need you. So flexibility for
the workforce is a key UH,

701
00:53:04.639 --> 00:53:07.119
a key aspect of morale. They
really want to have more flexibility in their

702
00:53:07.119 --> 00:53:13.480
lives and they need flexibility in terms
of changing the schedule. So the time

703
00:53:13.519 --> 00:53:17.920
flex bot that we created is using
something like the Uber system, so you

704
00:53:19.000 --> 00:53:23.599
can trade points or coins. UH. Basically you can opt in into a

705
00:53:23.679 --> 00:53:29.719
shift where there's really shortage of people, and when you opt in, you

706
00:53:29.880 --> 00:53:32.039
gain coins. Now you need to
take your key to the doctor, you

707
00:53:32.159 --> 00:53:36.079
pay with points, and you get
out of that shift, and then somebody

708
00:53:36.079 --> 00:53:39.559
else will take your points, and
the bot is actually recalculating the schedule so

709
00:53:39.599 --> 00:53:45.440
that overall the customer queue is going
to be at the right level. This

710
00:53:45.559 --> 00:53:49.079
is something that the industry has been
trying to do with people for years.

711
00:53:49.159 --> 00:53:53.039
But you have to hire enormous amount
of supervisors to change schedules all the time

712
00:53:53.039 --> 00:53:57.000
and find an optimal schedule in real
time, and that's just not possible.

713
00:53:57.800 --> 00:54:00.360
Using that Uber system and you know, and you get points and payments points

714
00:54:00.400 --> 00:54:06.000
based on whether it's a desirable shift
or not. So if you opt out

715
00:54:06.039 --> 00:54:07.440
to the midnight shift, you can
gain a lot of points, and then

716
00:54:07.480 --> 00:54:12.280
when you have an emergency, use
the points to opt out of that shift

717
00:54:12.320 --> 00:54:15.559
that you need. You need flexibility, so you can see the pots.

718
00:54:15.639 --> 00:54:19.320
Bots are there to help people in
every aspect because at the end of the

719
00:54:19.400 --> 00:54:23.440
day, people are making customer experience. I love it the bots helping bots,

720
00:54:23.480 --> 00:54:28.960
bots helping people. We've been talking
to Dan Bodner of Variant and Andy

721
00:54:29.079 --> 00:54:31.679
Roberts of Sabio Group today. Look
these folks up online. What a great

722
00:54:31.719 --> 00:54:35.639
show customer experience. That's what you
want. We'll talk to you next time.

723
00:54:35.679 --> 00:54:43.440
You've been listening to Inside Analysis.
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What are you Nurse? Pile?
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fifty eight. The fastest moving chart
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eight is the chip Monk Song,
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million. That was very good.
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you were a little slat, watch
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from yesterday dot com. It's that
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right here on KCIA My show is sponsored

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locally by Teas nineteen thirty two,
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at one O six point five FMK
two ninety three, CF Brino Valley,

779
00:59:25.239 --> 00:59:30.679
NBC News Radio. I'm Chris Garagio. Israel says its defense forces are now

780
00:59:30.719 --> 00:59:35.400
operating in the Southern Gaza Strip and
intense fighting was reported there today. The

781
00:59:35.519 --> 00:59:39.000
chief of the IDF General Staff said
that the focus is on targeting Hamas commanders

782
00:59:39.199 --> 00:59:44.079
in a very strong way. Witnesses
told Reuters that hospitals were struggling to keep

783
00:59:44.119 --> 00:59:47.239
up with the number of wounded coming
in. The Israeli military ordered Palestinians to

784
00:59:47.280 --> 00:59:52.639
immediately evacuate half a dozen areas in
South Gaza. National Security Council spokesman John

785
00:59:52.719 --> 00:59:58.000
Kirby says it's unclear when talks aimed
at resuming a truce between Israel and Amas

786
00:59:58.039 --> 01:00:00.840
will restart. We would like that
to happ in today, but honestly,

787
01:00:01.039 --> 01:00:05.119
I just don't know. Appearing on
NBC's Meet the Press, Kirby said the

788
01:00:05.199 --> 01:00:07.159
US is working really hard to try
to get both sides back to the table.

789
01:00:07.320 --> 01:00:08.559
French authorities say one person