Megatrends shaping customer service in 2020

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Good morning, evening and afternoon

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everyone. My name is Josh

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Reed and I’m from the

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digital events team here at

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Genesys, and I’ll be the

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moderator for today’s webcast. And

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let me be the first

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to welcome you and say

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thank you for joining to

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this webcast Mega Trends Shaping

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Customer Service in 2020. Before

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we get started, as usual,

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we have a few housekeeping

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items to go through before

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we get started. First off,

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if you experience any issues

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is designed to be an

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interactive experience between you and

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our two presenters today. So

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at any time during the

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webcast, feel free to throw

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as many as we can

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with the time that we

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have at the end of

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the presentation. However, don’t fret

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if we do run out

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of time and we don’t

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answer your question aloud, we

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will follow up with you

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few business days. And to

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miss something, don’t worry, you

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within the next few business.

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And also at any time

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during the webcast, feel free

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to check out the resources

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below the Q& A window. Clicking

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don’t worry about that. It’ll

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open up in a new

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tab in your browser and

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that’ll help expand on today’s

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topic of mega trends. See,

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told you short and sweet.

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So today we have two

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excellent presenters, excited to give you a

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glimpse of the future and

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discuss mega trends that are

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redefining contact center protocols in

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2020. So with that being

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said, I’m going to hand

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things off to one of

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our speakers of the hour,

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Joe Ciuffo product marketing director

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here at Genesys. Joe, the

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floor is yours. Thanks Josh.

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And hi everyone. Thank you

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so much for joining us

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today. As mentioned, and for

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the first time my name

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is spelled right. But I’m

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Joe Ciuffo and I’m a product marketing director

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here at Genesys. So my

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current position is I focus

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on artificial intelligence, which right

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now surfaces up through chat

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bots, using bots on a

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voice channel and using predictive

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technologies to identify when to

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engage with customers and really

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find the right time. But

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I always like to point

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out that I started here as a support

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engineer. So near and dear

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to my heart is knowing how to

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help people and knowing how

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to use these technologies to

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actually help the agents that

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are using it day in

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and day out. So with

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that, I’ll stop blabbing for

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a moment. And Kate, would

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you mind introducing yourself to

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everyone as well? No, no

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problem. Hi there. I’m Kate

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Leggett, I’m a VP and

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principal analyst here at Forrester Research

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and I focus on all

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things customer service and customer

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engagement. And thank you for

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taking an hour out of your

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busy days to listen to Joe

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and I talk about contact

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center trends. So Joe, back

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to you. Awesome. Well I

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think we’re in a good

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place here. Why don’t we

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go ahead and get started.

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So I guess I got

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to move the slide right?

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There you go. So my

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first prediction is that agents

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aren’t essential to scale anymore.

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And let me tell you

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what I mean by that and

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what I want to do

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is just take a step

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backwards and think about customer

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engagement and actually think about

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all the wonderful experiences that

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surround us in our daily

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lives. I mean, I think

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about ride sharing apps like

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Lyft and Uber that take all

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anxiety out of me getting

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to my destination because I

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have a full disclosure of

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information that I need about

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my ride to make me

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feel comfortable in that experience.

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I think about services like

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Amazon, like Netflix. I always

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joke that those two services

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know more about me than

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my husband does because if

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they’re able to recommend products,

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movies, content based on my

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particular history, what I’ve done,

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that intimate knowledge of where

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I’ve been, what I’ve done,

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how I’ve rated products or

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content. And so what we say

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today is that we’re surrounded

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by these differentiated experiences and

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these experiences have done a

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good shop at up leveling

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our expectations for engagement with

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any company that we do business

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with, both in our lives

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as consumers or in our

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business lives. And at Forrester

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we say we’re in the

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age of the customer where

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you, the consumer, the B2B

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customer, you control the conversation

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with any company that you

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do business with. And your

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expectations are heightened because of

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all the wonderful consumer experiences

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that surround us. And it’s

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like when I see on

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the screen here, you expect

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that any information that you

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need is available on any

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device at a person’s moment

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of need. And so what

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has happened is again, our

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expectations are heightened because of

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these wonderful consumer experiences. And

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then the way that we

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interact with companies has also

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changed. So let’s look at

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some data here. This is data

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from last year. It’s from

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probably the best trove of

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contact center data. It comes

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from dimension data’s a benchmarking

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report where they go out

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and they survey thousands and

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thousands of contact center decision-

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makers around the world. And

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what they are telling us,

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and it’s very close to

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one Forrester sees as well,

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is that customers, again, either

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consumers or B2B customers want

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very little friction when they

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interact with companies, they tend

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to choose self- service as

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a first point of contact

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as they interact with brands.

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And if they’re not able

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to find what they’re looking

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for via self service, they’re moving

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to digital engagement modalities, chat

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messaging for example, because it

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values their time. A data point

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from Forrester says that 73%

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of customers say that valuing

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their time is the most

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important thing that companies can

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do to provide great customer

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service. So again, just looking

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at the data here, you’d

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look at that top bar

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and it says 88% of

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contact center decision makers project

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that their self service volumes

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will increase. They’re calling it

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robotic automation, but these are

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self- service volumes are increasing

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this year. 77% say that

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digital agent assistant service volumes

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will increase. And the third

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bar says, and my old

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eyes can’t see it, I

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think it says 66% of

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contact center decision makers say

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that their overall interaction volumes

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will increase. This is because

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you make it easier to

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engage with the companies. And so your

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customers will engage more with

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companies. And so what this

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means is that companies are

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being flooded by this mass

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of digital engagement from their

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customers. The better, the easier

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they make it for customers

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to engage, again, more customers

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will engage with you. And

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you want this because better

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engagement strengthens customer relations. But

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what happens is you can’t

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keep up with these engagement

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volumes without turning to AI

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and automation. And that’s where

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the prediction of agents are

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no longer essential to scale

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comes from because companies are

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infusing AI and automation everywhere

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in their operations to keep

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up with these ballooning engagement

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falling from their customers and

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to deliver the quality of

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service that customers expect. So what

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you’re looking at on the

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screen here is what we think

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of as being value chain

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for AI for customer service,

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where AI and automation, again,

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it encompasses a wealth of

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different technologies that basically add

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intelligence to your operations and

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offload agents comes from doing

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rope repetitive work. So at

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the low end of this

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value chain, you’re looking at

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AI and automation being able

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to increase efficiency on technology

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like RPA or automatic case

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classification or automatic routing to

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be able to offload all

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the reproducible or lower value

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tasks from agents. Moving up

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00:10:06,900 –> 00:10:08,680
the value curve, you’ve got

281
00:10:08,900 –> 00:10:10,550
AI and automation that can

282
00:10:10,550 –> 00:10:13,360
help reduce friction. For example,

283
00:10:13,360 –> 00:10:15,330
monitoring the sentiment of an

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00:10:15,330 –> 00:10:19,650
interaction and escalating automatically if

285
00:10:19,650 –> 00:10:22,320
a customer seems to distressed.

286
00:10:22,770 –> 00:10:24,220
Moving up the value chain

287
00:10:24,220 –> 00:10:27,440
you’ve got enhanced customer empowerment.

288
00:10:27,490 –> 00:10:29,850
This is all about chat

289
00:10:29,850 –> 00:10:31,580
bots and self- service and

290
00:10:31,980 –> 00:10:34,560
self- service processes. Again, we

291
00:10:34,560 –> 00:10:35,610
know that customers want to

292
00:10:35,650 –> 00:10:36,990
self serve as first point

293
00:10:36,990 –> 00:10:40,830
of contact and these technologies

294
00:10:41,090 –> 00:10:44,240
empower great self service. And

295
00:10:44,240 –> 00:10:46,740
then four and five on

296
00:10:46,740 –> 00:10:48,450
this value chain are about

297
00:10:48,760 –> 00:10:52,150
proactive and even preemptive service

298
00:10:52,380 –> 00:10:53,760
where for example, you’re looking

299
00:10:53,760 –> 00:10:56,740
at the customer’s journey on

300
00:10:56,820 –> 00:10:58,960
a web property independent on

301
00:10:58,960 –> 00:11:01,730
the customer’s behavior, you’re proactively

302
00:11:01,730 –> 00:11:03,360
engaging with the customer to

303
00:11:03,360 –> 00:11:04,640
be able to start a

304
00:11:04,640 –> 00:11:08,860
conversation or offer content or

305
00:11:08,860 –> 00:11:10,570
give them an offer or

306
00:11:10,570 –> 00:11:13,460
preemptive services, again, all about

307
00:11:13,520 –> 00:11:16,360
connected devices and being able

308
00:11:16,360 –> 00:11:21,210
to to preemptively intervene upon

309
00:11:21,210 –> 00:11:22,900
signs of distress. So what

310
00:11:22,940 –> 00:11:25,280
it means is that companies

311
00:11:25,280 –> 00:11:29,020
are infusing AI and automation

312
00:11:29,020 –> 00:11:30,610
just about everywhere in the

313
00:11:30,610 –> 00:11:32,840
customer service operations and what

314
00:11:32,840 –> 00:11:35,740
it does is it allows

315
00:11:36,110 –> 00:11:38,380
content centers to scale without

316
00:11:38,380 –> 00:11:42,270
necessarily having add agent headcount.

317
00:11:43,540 –> 00:11:44,790
So that was a lot

318
00:11:45,450 –> 00:11:50,370
shareable but. I love that.

319
00:11:50,370 –> 00:11:52,130
Actually a few points that

320
00:11:52,130 –> 00:11:53,160
I had written down about

321
00:11:53,270 –> 00:11:55,090
you said where really that

322
00:11:55,090 –> 00:11:56,820
idea, that ease of access

323
00:11:56,820 –> 00:11:58,440
to the information is sometimes

324
00:11:58,500 –> 00:11:59,830
just as important as the

325
00:11:59,830 –> 00:12:01,900
information you’re getting itself. And

326
00:12:01,900 –> 00:12:02,550
I love that you brought

327
00:12:02,550 –> 00:12:04,990
up transportation because some side

328
00:12:04,990 –> 00:12:05,670
notes here, I live in

329
00:12:05,670 –> 00:12:07,390
San Francisco. When I think

330
00:12:07,390 –> 00:12:09,170
about this ease of access

331
00:12:09,170 –> 00:12:11,100
to information and Uber, right?

332
00:12:11,430 –> 00:12:13,440
Transportation in San Francisco, I

333
00:12:13,440 –> 00:12:15,150
can take the subway or

334
00:12:15,150 –> 00:12:15,930
I can take an Uber

335
00:12:15,930 –> 00:12:16,840
and the subway might be

336
00:12:16,840 –> 00:12:18,650
going the exact same direction,

337
00:12:19,120 –> 00:12:20,260
but man, for that last

338
00:12:20,260 –> 00:12:22,010
mile, it’s definitely a different

339
00:12:22,010 –> 00:12:23,800
experience. So I am more

340
00:12:23,800 –> 00:12:24,500
likely to pay a little

341
00:12:24,620 –> 00:12:25,840
bit more for the experience

342
00:12:25,840 –> 00:12:26,830
that gets me where I

343
00:12:26,830 –> 00:12:27,810
need to go in a

344
00:12:27,810 –> 00:12:29,970
quicker way. And we’re seeing

345
00:12:29,970 –> 00:12:31,590
this with customers as well.

346
00:12:31,930 –> 00:12:33,360
In fact, even though I

347
00:12:33,360 –> 00:12:34,780
hate this term millennial because

348
00:12:34,780 –> 00:12:35,520
it puts me in a

349
00:12:35,520 –> 00:12:37,660
bucket, we are seeing research

350
00:12:37,660 –> 00:12:39,950
that shows that millennial users

351
00:12:39,950 –> 00:12:41,420
of banking apps are much

352
00:12:41,420 –> 00:12:43,320
more likely to switch when

353
00:12:43,320 –> 00:12:44,460
they look at better mobile

354
00:12:44,510 –> 00:12:47,030
or digital capabilities. So you’re

355
00:12:47,030 –> 00:12:48,360
probably wondering why I’m talking

356
00:12:48,360 –> 00:12:50,770
about digital capabilities and my

357
00:12:50,770 –> 00:12:52,450
first prediction is actually about

358
00:12:52,450 –> 00:12:54,450
voice. I want to talk about our

359
00:12:54,450 –> 00:12:56,030
first prediction here and that voice isn’t

360
00:12:56,030 –> 00:12:58,080
dead, but it’s absolutely different.

361
00:12:58,660 –> 00:12:59,730
What we’ve seen as we

362
00:12:59,730 –> 00:13:01,310
talk with customers and we

363
00:13:01,310 –> 00:13:03,200
look at the research, is

364
00:13:03,200 –> 00:13:05,790
that consumers rate immediate responses

365
00:13:06,070 –> 00:13:07,550
as super important. In fact,

366
00:13:07,640 –> 00:13:09,940
HubSpot report noted that 90% of

367
00:13:09,940 –> 00:13:11,980
consumers put it in important

368
00:13:11,980 –> 00:13:13,580
or very important when it

369
00:13:13,580 –> 00:13:14,940
comes to the immediacy of

370
00:13:14,940 –> 00:13:16,820
the response they get. And

371
00:13:16,820 –> 00:13:17,780
we’re also looking at other

372
00:13:17,780 –> 00:13:19,680
research that’s showing that these

373
00:13:19,680 –> 00:13:22,350
self service requests or initiations

374
00:13:22,350 –> 00:13:23,820
are coming over a voice

375
00:13:24,060 –> 00:13:26,190
channel. So when you think of voice, you think

376
00:13:26,190 –> 00:13:27,430
of a phone, but that’s

377
00:13:27,430 –> 00:13:28,400
not the first place where

378
00:13:28,400 –> 00:13:29,270
it starts or even where

379
00:13:29,270 –> 00:13:30,810
it ends. I always like

380
00:13:30,810 –> 00:13:31,610
to tell the story. When

381
00:13:31,610 –> 00:13:32,250
my wife and I went

382
00:13:32,250 –> 00:13:33,410
to Ireland last year, we

383
00:13:33,410 –> 00:13:34,900
were really excited and she

384
00:13:34,900 –> 00:13:35,870
gave me two things to

385
00:13:35,870 –> 00:13:36,930
make sure that I did. It

386
00:13:36,930 –> 00:13:38,490
was make sure our data

387
00:13:38,490 –> 00:13:39,130
would work when we were

388
00:13:39,130 –> 00:13:40,130
in Ireland because I have

389
00:13:40,130 –> 00:13:41,150
no idea how to use

390
00:13:41,150 –> 00:13:42,990
a map apparently. And the

391
00:13:42,990 –> 00:13:44,430
second one was to make

392
00:13:44,430 –> 00:13:45,520
sure I set a travel alert

393
00:13:45,560 –> 00:13:46,760
on her credit card so

394
00:13:46,760 –> 00:13:47,720
we didn’t have any issues.

395
00:13:48,340 –> 00:13:49,360
I remembered to set the

396
00:13:49,360 –> 00:13:50,420
travel alert when I was

397
00:13:50,420 –> 00:13:51,720
in Dublin and my card

398
00:13:51,720 –> 00:13:52,680
got declined at the first

399
00:13:52,680 –> 00:13:54,370
restaurant. And I bring that

400
00:13:54,370 –> 00:13:55,950
up because it was something

401
00:13:55,950 –> 00:13:57,040
we fixed, we phoned in

402
00:13:57,040 –> 00:13:58,140
and fixed it, but it

403
00:13:58,140 –> 00:13:59,100
could have been very different.

404
00:13:59,270 –> 00:14:00,370
What if while I was

405
00:14:00,370 –> 00:14:02,370
packing, I just asked my

406
00:14:02,370 –> 00:14:04,130
Amazon Alexa or my Google

407
00:14:04,130 –> 00:14:05,050
home or some of those

408
00:14:05,050 –> 00:14:06,890
devices to do this for

409
00:14:06,890 –> 00:14:08,440
me. And you start to

410
00:14:08,440 –> 00:14:10,210
think about voice being less

411
00:14:10,210 –> 00:14:11,410
of a channel and more

412
00:14:11,410 –> 00:14:12,920
of an interface. When we

413
00:14:12,920 –> 00:14:14,470
look at these home devices,

414
00:14:14,790 –> 00:14:16,140
they are becoming the towns

415
00:14:16,140 –> 00:14:18,260
square where it may not

416
00:14:18,260 –> 00:14:19,550
be controlled by a business

417
00:14:19,550 –> 00:14:21,280
or even something that you

418
00:14:21,280 –> 00:14:23,830
have direct ownership of but

419
00:14:23,830 –> 00:14:25,560
it’s absolutely where communication is

420
00:14:25,560 –> 00:14:27,200
happening and it’s absolutely where

421
00:14:27,200 –> 00:14:29,460
that real conversation with customers

422
00:14:29,460 –> 00:14:31,270
are occurring. So it’s the

423
00:14:31,270 –> 00:14:33,640
notion of letting someone set

424
00:14:33,640 –> 00:14:34,930
the music they’d like to

425
00:14:34,930 –> 00:14:36,560
listen to and also navigating

426
00:14:36,560 –> 00:14:38,220
through your interface all from

427
00:14:38,220 –> 00:14:39,350
the same place in the

428
00:14:39,350 –> 00:14:41,420
moment of need and alleviating

429
00:14:41,750 –> 00:14:44,310
those user experience issues. Just

430
00:14:44,310 –> 00:14:45,200
to note on that, I

431
00:14:45,200 –> 00:14:46,030
still to this day have

432
00:14:46,030 –> 00:14:47,510
no idea where sending the

433
00:14:47,510 –> 00:14:48,610
travel alert would occur in

434
00:14:48,610 –> 00:14:50,280
my banking app. It’s something

435
00:14:50,280 –> 00:14:51,020
I would call in for

436
00:14:51,020 –> 00:14:51,900
or have to dig around

437
00:14:51,900 –> 00:14:53,200
for, but not something that

438
00:14:53,200 –> 00:14:55,980
I would proactively research. So

439
00:14:55,980 –> 00:14:57,700
to tie that in, it

440
00:14:57,700 –> 00:14:58,830
comes down to why should

441
00:14:58,830 –> 00:15:01,170
we care? Well, this is the first time,

442
00:15:01,170 –> 00:15:02,360
especially looking in this next

443
00:15:02,360 –> 00:15:04,090
year, that that technology is

444
00:15:04,090 –> 00:15:05,820
good enough to start leveraging

445
00:15:05,820 –> 00:15:07,740
on the voice channel. As

446
00:15:07,740 –> 00:15:08,980
you think about calling in

447
00:15:08,980 –> 00:15:10,230
on a number now, we

448
00:15:10,230 –> 00:15:11,660
can extend this voice bot

449
00:15:11,660 –> 00:15:13,820
technology. It has the ability

450
00:15:13,820 –> 00:15:15,180
to pick up on nuances

451
00:15:15,180 –> 00:15:17,150
like alphanumeric where if you’re

452
00:15:17,150 –> 00:15:18,210
an airline and you’re giving

453
00:15:18,210 –> 00:15:19,630
your confirmation code, which is

454
00:15:19,630 –> 00:15:22,760
numbers, digits and letters, this

455
00:15:22,760 –> 00:15:23,610
used to be hard to

456
00:15:23,610 –> 00:15:25,060
understand from a natural language

457
00:15:25,060 –> 00:15:26,790
processing. We don’t have these

458
00:15:26,790 –> 00:15:28,450
problems anymore so that we can

459
00:15:28,450 –> 00:15:30,100
change the experience to be

460
00:15:30,100 –> 00:15:31,920
more about the conversation and

461
00:15:31,920 –> 00:15:32,650
where we need to take

462
00:15:32,650 –> 00:15:34,140
you next because we understand

463
00:15:34,140 –> 00:15:35,740
that instead of a maze

464
00:15:35,740 –> 00:15:36,770
of menus that you have

465
00:15:36,770 –> 00:15:38,640
to listen to and hopefully

466
00:15:38,640 –> 00:15:40,890
select the right one. Now

467
00:15:40,960 –> 00:15:41,790
I did cheat a little

468
00:15:41,790 –> 00:15:42,500
bit here. I have a

469
00:15:42,500 –> 00:15:43,820
1A as well on our

470
00:15:43,820 –> 00:15:45,570
predictions and it’s because I

471
00:15:45,570 –> 00:15:46,320
don’t want you to think that

472
00:15:46,550 –> 00:15:47,470
we’re just keying in on

473
00:15:47,470 –> 00:15:50,140
voice. Commerce is messaging. We’re

474
00:15:50,140 –> 00:15:51,450
seeing a lot of research

475
00:15:51,540 –> 00:15:52,610
hint at that as well

476
00:15:52,610 –> 00:15:54,460
from our end and for

477
00:15:54,460 –> 00:15:55,330
you out there in the

478
00:15:55,340 –> 00:15:57,220
field and in the audience,

479
00:15:57,460 –> 00:15:58,780
have you ever kept multiple

480
00:15:58,780 –> 00:16:00,420
tabs open on your browser

481
00:16:00,830 –> 00:16:02,180
because you wanted to purchase something,

482
00:16:02,180 –> 00:16:03,450
but you just had another

483
00:16:03,450 –> 00:16:04,790
question around it, right? Think

484
00:16:05,020 –> 00:16:06,530
about shoes that you wanted to buy.

485
00:16:06,880 –> 00:16:08,010
Do those shoes run small,

486
00:16:08,010 –> 00:16:09,060
do they run large? You’ve

487
00:16:09,060 –> 00:16:09,900
got to look that up.

488
00:16:10,410 –> 00:16:11,420
Think about a flight you

489
00:16:11,420 –> 00:16:12,870
might have for work, is

490
00:16:12,880 –> 00:16:13,810
there a chance if it’s

491
00:16:13,810 –> 00:16:14,600
a long flight that you

492
00:16:14,600 –> 00:16:15,620
can get the upgrade even

493
00:16:15,620 –> 00:16:16,840
though that’s on the wishlist,

494
00:16:16,840 –> 00:16:18,390
not always happening. It’s worth

495
00:16:18,390 –> 00:16:19,600
checking and it stops you

496
00:16:19,600 –> 00:16:21,400
from purchasing it. And what

497
00:16:21,400 –> 00:16:23,300
about something else, maybe even

498
00:16:23,300 –> 00:16:24,580
more in the weeds. You’re

499
00:16:24,580 –> 00:16:25,290
trying to get a car

500
00:16:25,290 –> 00:16:26,540
insurance policy. You’ve just moved

501
00:16:26,540 –> 00:16:27,840
to a new state, you’ve

502
00:16:27,840 –> 00:16:28,930
already got home insurance with

503
00:16:28,930 –> 00:16:30,340
that company, do you get

504
00:16:30,340 –> 00:16:31,590
a discount? Would it stop

505
00:16:31,590 –> 00:16:32,880
you from just self- serving

506
00:16:32,880 –> 00:16:33,800
and filling that form out

507
00:16:33,800 –> 00:16:36,300
on your own? Messaging is

508
00:16:36,300 –> 00:16:37,970
asynchronous by nature and Kate

509
00:16:37,970 –> 00:16:39,650
brought this up earlier. I

510
00:16:39,650 –> 00:16:41,330
could start chatting with my

511
00:16:41,330 –> 00:16:42,720
company on the train on

512
00:16:42,720 –> 00:16:43,870
the way to work asking

513
00:16:43,870 –> 00:16:45,570
them about this insurance policy.

514
00:16:46,150 –> 00:16:46,730
But once I’m in the

515
00:16:46,730 –> 00:16:48,240
office, I might switch to

516
00:16:48,240 –> 00:16:49,580
my laptop and switching to

517
00:16:49,580 –> 00:16:50,910
my laptop means I’m picking

518
00:16:50,910 –> 00:16:52,520
up on that same conversation

519
00:16:52,540 –> 00:16:54,400
across a messaging channel but

520
00:16:54,400 –> 00:16:55,330
I’m doing it in a different

521
00:16:55,330 –> 00:16:56,650
way and I’m doing it

522
00:16:56,650 –> 00:16:58,830
at a different time. Great

523
00:16:58,830 –> 00:17:00,290
thing about messaging is we

524
00:17:00,290 –> 00:17:02,430
see this burst capability, so

525
00:17:02,430 –> 00:17:03,800
asynchronous, meaning we pick up

526
00:17:03,800 –> 00:17:05,210
when we’re ready and then

527
00:17:05,210 –> 00:17:06,340
we can really jump in

528
00:17:06,340 –> 00:17:07,670
and start that conversation when we

529
00:17:07,670 –> 00:17:09,470
have the free time. And

530
00:17:09,470 –> 00:17:10,180
this is what it comes

531
00:17:10,180 –> 00:17:11,840
down to, is giving someone

532
00:17:11,840 –> 00:17:13,170
a tangible reference of when

533
00:17:13,170 –> 00:17:14,410
things are working, when they’re

534
00:17:14,410 –> 00:17:15,680
going in the right direction,

535
00:17:16,120 –> 00:17:17,110
and then having a place

536
00:17:17,110 –> 00:17:17,960
that they can come back

537
00:17:18,190 –> 00:17:19,160
and pick up. And that’s

538
00:17:19,160 –> 00:17:21,220
what messaging is about. So

539
00:17:21,590 –> 00:17:22,770
I think it all ends

540
00:17:22,770 –> 00:17:24,390
with the fact that messaging

541
00:17:24,390 –> 00:17:26,140
apps tie into payment applications.

542
00:17:26,320 –> 00:17:27,210
This is the first time

543
00:17:27,210 –> 00:17:28,310
we have these channels that

544
00:17:28,310 –> 00:17:30,010
bring everything together and don’t

545
00:17:30,010 –> 00:17:31,500
call it a disjointed experience.

546
00:17:31,950 –> 00:17:32,960
Kate I know I have babbled

547
00:17:32,960 –> 00:17:34,060
for a while over back

548
00:17:34,060 –> 00:17:34,280
to you. What do you think about that?

549
00:17:38,380 –> 00:17:40,610
agents are no longer essential to

550
00:17:40,610 –> 00:17:42,620
scale. I talked about digital

551
00:17:42,620 –> 00:17:43,870
engagement in the house self-service

552
00:17:44,390 –> 00:17:46,510
and digital engagement is rising.

553
00:17:46,690 –> 00:17:48,700
You talked about voice self

554
00:17:48,840 –> 00:17:51,380
service, you talked about conversational

555
00:17:51,380 –> 00:17:54,440
commerce, which is powered in

556
00:17:54,500 –> 00:17:58,150
part by bots, by automation.

557
00:17:58,530 –> 00:18:00,910
It all makes sense. This

558
00:18:00,910 –> 00:18:02,210
is the way the world

559
00:18:02,210 –> 00:18:04,250
is going. So what happens

560
00:18:05,240 –> 00:18:09,710
to your agents. Where do

561
00:18:09,710 –> 00:18:13,320
they fall in the spectrum

562
00:18:13,420 –> 00:18:16,250
of importance if again, so

563
00:18:16,250 –> 00:18:21,220
much engagement is going to

564
00:18:21,250 –> 00:18:23,970
self- service, to digital channels,

565
00:18:23,970 –> 00:18:26,450
voice channels that are automated?

566
00:18:27,440 –> 00:18:29,040
So the next trend is

567
00:18:29,040 –> 00:18:32,680
about agents and the technologies

568
00:18:32,680 –> 00:18:35,010
that agents need to be

569
00:18:35,010 –> 00:18:38,820
able to effectively support their

570
00:18:38,820 –> 00:18:41,470
customers. So to be able

571
00:18:41,470 –> 00:18:43,040
to talk about this, let’s

572
00:18:43,040 –> 00:18:44,280
look at this data again,

573
00:18:44,440 –> 00:18:46,300
the dimension data that I

574
00:18:46,300 –> 00:18:48,380
had brought up before. So

575
00:18:48,380 –> 00:18:50,700
the bottom set of data is

576
00:18:50,700 –> 00:18:54,010
really interesting. It’s all about

577
00:18:54,520 –> 00:18:56,050
phone volumes and it’s not

578
00:18:56,440 –> 00:18:58,350
voice self- service, this is

579
00:18:58,350 –> 00:19:01,110
live agents on the phone

580
00:19:01,200 –> 00:19:03,910
answering customer calls. And what

581
00:19:03,910 –> 00:19:06,480
we find is that 64%

582
00:19:06,480 –> 00:19:08,490
of contact center decision makers

583
00:19:08,720 –> 00:19:13,200
believe that voice volumes will

584
00:19:13,200 –> 00:19:17,350
drop. And this is understandable

585
00:19:17,350 –> 00:19:19,680
because we’re moving to a

586
00:19:19,680 –> 00:19:23,920
digital first self- service first

587
00:19:23,920 –> 00:19:26,530
world. But what’s actually getting

588
00:19:26,530 –> 00:19:29,480
into the contact center? It’s

589
00:19:29,480 –> 00:19:31,600
the harder calls, the calls

590
00:19:31,730 –> 00:19:33,730
that weren’t able to be

591
00:19:33,730 –> 00:19:37,700
answered via self surface, where a

592
00:19:37,860 –> 00:19:40,010
customer has already gone to

593
00:19:40,010 –> 00:19:41,400
your website, to your mobile

594
00:19:41,400 –> 00:19:43,790
site, looked for information, perhaps

595
00:19:43,790 –> 00:19:46,240
even chatted with an agent,

596
00:19:46,300 –> 00:19:47,900
isn’t able to really get

597
00:19:47,900 –> 00:19:49,630
the answer. So they’re picking

598
00:19:49,630 –> 00:19:50,860
up the phone and they’re

599
00:19:50,860 –> 00:19:53,440
calling a contact center. So

600
00:19:53,830 –> 00:19:56,300
voice calls, the actual volume

601
00:19:56,300 –> 00:19:58,470
is dropping. Again, because self

602
00:19:58,470 –> 00:20:00,280
service is picking off a

603
00:20:00,280 –> 00:20:01,900
lot of the easy inquiries.

604
00:20:02,140 –> 00:20:05,410
But the length of calls

605
00:20:05,470 –> 00:20:08,220
is actually getting longer again,

606
00:20:08,220 –> 00:20:09,260
because you’re getting the more

607
00:20:09,260 –> 00:20:11,660
complicated calls. It could be

608
00:20:11,660 –> 00:20:13,470
the exceptions, it could be

609
00:20:13,470 –> 00:20:18,470
the calls where there’s multiple

610
00:20:18,470 –> 00:20:22,090
questions within a call. So

611
00:20:22,090 –> 00:20:23,140
your agents are getting the

612
00:20:23,140 –> 00:20:25,860
harder calls. Something else is

613
00:20:25,860 –> 00:20:31,120
happening as well, your customers

614
00:20:32,030 –> 00:20:34,190
are frustrated as they get

615
00:20:34,190 –> 00:20:36,830
to the agent. So the

616
00:20:36,830 –> 00:20:39,090
agent doesn’t necessarily only need

617
00:20:39,090 –> 00:20:41,420
to deal with the harder

618
00:20:41,420 –> 00:20:43,850
call, but they may be

619
00:20:43,910 –> 00:20:45,530
having to deal with the customer

620
00:20:45,530 –> 00:20:47,530
who’s frustrated because their time

621
00:20:47,530 –> 00:20:49,020
has been wasted by going

622
00:20:49,020 –> 00:20:50,240
to self- service and not

623
00:20:50,240 –> 00:20:51,470
finding what they’re looking for.

624
00:20:51,820 –> 00:20:53,680
Or are they maybe anxious. They

625
00:20:53,680 –> 00:20:55,360
have a medication that’s not

626
00:20:55,360 –> 00:20:58,570
covered by their policy and

627
00:20:58,570 –> 00:21:00,350
it’s a medication that’s prescribed

628
00:21:00,350 –> 00:21:01,860
that they really need. Or

629
00:21:01,860 –> 00:21:04,050
they’re angry because on their

630
00:21:04,050 –> 00:21:05,750
bills there’s a surcharge that

631
00:21:05,750 –> 00:21:07,730
they don’t understand. And so

632
00:21:07,730 –> 00:21:09,300
they’re in a combative mood.

633
00:21:09,500 –> 00:21:11,500
So the agent actually has a

634
00:21:11,500 –> 00:21:13,460
tough time. They’re getting this

635
00:21:13,460 –> 00:21:17,910
escalated call which is a

636
00:21:18,120 –> 00:21:20,610
harder call because the work

637
00:21:20,610 –> 00:21:22,330
is more complex and they’re

638
00:21:22,330 –> 00:21:24,130
having to understand the emotional

639
00:21:24,130 –> 00:21:26,020
state of the customer and

640
00:21:26,020 –> 00:21:28,480
react to that emotional state,

641
00:21:28,830 –> 00:21:32,300
turn the conversation around and

642
00:21:32,300 –> 00:21:34,350
do the right thing for

643
00:21:34,350 –> 00:21:36,760
the customer. So where does

644
00:21:36,860 –> 00:21:38,820
this all tie into the

645
00:21:38,820 –> 00:21:43,410
prediction about the agent desktop

646
00:21:44,170 –> 00:21:46,450
evolve? And this is because

647
00:21:46,450 –> 00:21:48,700
your agents today have to

648
00:21:48,700 –> 00:21:50,860
be supported by a much

649
00:21:50,860 –> 00:21:53,490
greater range of technologies to

650
00:21:53,490 –> 00:21:55,450
be able to serve the

651
00:21:55,450 –> 00:21:58,550
customer and provide the quality

652
00:21:58,550 –> 00:22:00,660
of service that they expect.

653
00:22:01,170 –> 00:22:03,500
So what we find is

654
00:22:03,500 –> 00:22:04,520
if you look at most

655
00:22:04,520 –> 00:22:07,550
agent desktops, they have a customer

656
00:22:07,550 –> 00:22:10,130
service solution that they’re doing

657
00:22:10,130 –> 00:22:11,530
their work in. And their

658
00:22:11,530 –> 00:22:13,920
customer service solution does things like

659
00:22:14,470 –> 00:22:16,410
help you identify the customer,

660
00:22:16,410 –> 00:22:18,210
pull up the customer history,

661
00:22:18,910 –> 00:22:21,460
being able to capture the

662
00:22:21,460 –> 00:22:24,350
inquiry details, workflow the inquiry.

663
00:22:25,340 –> 00:22:26,670
It’s got components of case

664
00:22:26,670 –> 00:22:29,390
management. You may also be

665
00:22:29,390 –> 00:22:31,050
able to pop up some

666
00:22:31,580 –> 00:22:33,770
associated knowledge from the knowledge

667
00:22:33,770 –> 00:22:41,530
base. And this customer service

668
00:22:41,530 –> 00:22:43,770
solution is also able to

669
00:22:43,770 –> 00:22:45,760
work omnichannel inquiries. So not

670
00:22:45,760 –> 00:22:47,390
only phone calls but digital

671
00:22:47,390 –> 00:22:49,530
inquiries as well. But what we

672
00:22:49,530 –> 00:22:52,420
also find is that many

673
00:22:52,420 –> 00:22:54,420
contact centers are layering on

674
00:22:54,480 –> 00:22:57,910
additional technologies to make agents

675
00:22:57,910 –> 00:23:01,070
more efficient, more effective, and

676
00:23:01,070 –> 00:23:03,860
to be able to prescribe

677
00:23:03,860 –> 00:23:05,470
the right set of actions

678
00:23:05,470 –> 00:23:07,940
for the agent. So on

679
00:23:07,940 –> 00:23:10,430
desktops, in terms of efficiency

680
00:23:10,430 –> 00:23:12,600
tools, we see many companies

681
00:23:12,600 –> 00:23:15,130
adopting things like RPA or

682
00:23:15,130 –> 00:23:19,030
process guidance that handhold agents

683
00:23:19,030 –> 00:23:24,890
through predefined processes around effectiveness

684
00:23:24,890 –> 00:23:26,740
tool to make agents more

685
00:23:26,740 –> 00:23:30,420
effective. We see, for example

686
00:23:30,420 –> 00:23:32,450
cognitive search solutions that are

687
00:23:32,450 –> 00:23:36,530
layered on top of silos

688
00:23:36,530 –> 00:23:39,380
of data like bug databases

689
00:23:39,380 –> 00:23:42,040
or content repositories to be

690
00:23:42,040 –> 00:23:43,380
able to pull up the

691
00:23:43,380 –> 00:23:45,710
right content or the right

692
00:23:45,960 –> 00:23:48,640
related data based on the

693
00:23:48,640 –> 00:23:51,380
customer’s inquiry. We also see

694
00:23:51,380 –> 00:23:53,250
tools like agent facing chat

695
00:23:53,250 –> 00:23:56,430
bots to help the agent

696
00:23:56,640 –> 00:23:58,540
surface the right data, the

697
00:23:58,540 –> 00:24:00,680
right information that they need

698
00:24:00,940 –> 00:24:02,720
depending on the intent that’s

699
00:24:02,720 –> 00:24:05,490
captured from the customer. We

700
00:24:05,490 –> 00:24:08,710
also see collaboration tools where

701
00:24:08,710 –> 00:24:10,760
agents can collaborate with other

702
00:24:10,760 –> 00:24:11,940
agents to work on the

703
00:24:11,940 –> 00:24:14,360
harder work. And we also

704
00:24:14,360 –> 00:24:16,030
see in terms of prescriptive

705
00:24:16,030 –> 00:24:17,020
tool, a lot of the

706
00:24:17,020 –> 00:24:21,360
AI or intelligence fueled solutions

707
00:24:21,360 –> 00:24:23,150
to be able to push

708
00:24:23,150 –> 00:24:25,660
the next best action to

709
00:24:25,660 –> 00:24:26,930
the agent. What’s the next

710
00:24:26,930 –> 00:24:29,150
best conversation the agent needs

711
00:24:29,150 –> 00:24:30,570
to have or the right

712
00:24:30,640 –> 00:24:32,500
offer to be able to

713
00:24:32,960 –> 00:24:34,980
present to the agent that

714
00:24:34,980 –> 00:24:38,690
has the highest rate of

715
00:24:38,690 –> 00:24:40,620
being accepted. So what we

716
00:24:40,620 –> 00:24:43,060
find is again, the agents

717
00:24:43,130 –> 00:24:44,680
are working on the harder work

718
00:24:45,040 –> 00:24:49,010
and they are helped along

719
00:24:49,240 –> 00:24:52,420
by this set of tooling

720
00:24:52,640 –> 00:24:54,580
that tends to be assembled

721
00:24:55,260 –> 00:24:56,780
by starting off with a customer

722
00:24:56,780 –> 00:24:58,610
service solution and then layering

723
00:24:58,610 –> 00:25:00,770
on the technologies that are

724
00:25:00,770 –> 00:25:02,100
needed to be able to

725
00:25:02,100 –> 00:25:11,650
adequately support the agent. So

726
00:25:11,750 –> 00:25:14,460
Joe, does that resonate? Yeah. And

727
00:25:15,640 –> 00:25:16,460
it almost looks like I

728
00:25:16,460 –> 00:25:17,310
stole your homework a little

729
00:25:17,570 –> 00:25:18,690
bit, but I love what

730
00:25:18,690 –> 00:25:19,830
you just brought up because

731
00:25:19,830 –> 00:25:21,010
it ties right into our

732
00:25:21,010 –> 00:25:23,640
second prediction that employees become

733
00:25:23,640 –> 00:25:25,590
a brand differentiator that are

734
00:25:25,590 –> 00:25:27,790
augmented by AI. And Kate,

735
00:25:27,790 –> 00:25:28,440
if there’s one thing I think that

736
00:25:28,950 –> 00:25:29,910
you hit on really nicely

737
00:25:29,950 –> 00:25:31,550
that I got from that was as

738
00:25:31,550 –> 00:25:33,420
AI is becoming increasingly more

739
00:25:33,420 –> 00:25:36,460
consistent and capable, we’re finding

740
00:25:36,460 –> 00:25:37,850
that in the contact center,

741
00:25:37,850 –> 00:25:39,160
the agent’s work is going

742
00:25:39,160 –> 00:25:40,050
to become not just more

743
00:25:40,050 –> 00:25:41,980
difficult, but empathetic as well.

744
00:25:42,610 –> 00:25:43,880
So it’s important that we

745
00:25:43,880 –> 00:25:46,110
understand that really AI will

746
00:25:46,110 –> 00:25:47,530
enable agents to make better

747
00:25:47,720 –> 00:25:49,680
decisions and focus on empathy

748
00:25:49,860 –> 00:25:52,350
within those customer interactions. And

749
00:25:52,350 –> 00:25:53,900
we’re seeing that now through

750
00:25:53,900 –> 00:25:55,450
just- in- time interfaces that

751
00:25:55,450 –> 00:25:57,540
are surfacing both information and

752
00:25:57,540 –> 00:25:59,220
even applications as they’re needed

753
00:25:59,220 –> 00:26:01,220
in real time. What I’ve

754
00:26:01,320 –> 00:26:02,360
gotten away from this, and

755
00:26:02,360 –> 00:26:03,110
even as I was a

756
00:26:03,110 –> 00:26:05,220
support engineer, is that complicated

757
00:26:05,220 –> 00:26:07,350
interfaces and integrations and other

758
00:26:07,350 –> 00:26:09,250
systems, those should no longer

759
00:26:09,250 –> 00:26:10,410
be the obligation of the

760
00:26:10,410 –> 00:26:12,290
agent, rather the bandwidth to

761
00:26:12,290 –> 00:26:14,070
pay attention to this interaction.

762
00:26:14,650 –> 00:26:16,430
So as we’re tying that

763
00:26:16,430 –> 00:26:18,130
up for our prediction around

764
00:26:18,130 –> 00:26:20,100
employees becoming a brand differentiator,

765
00:26:20,620 –> 00:26:21,670
we think about that the

766
00:26:21,670 –> 00:26:23,420
rising interactions are happening across

767
00:26:23,470 –> 00:26:25,510
channels, but what’s going to

768
00:26:25,580 –> 00:26:26,960
the agents are more difficult.

769
00:26:27,130 –> 00:26:28,110
So the tools that they

770
00:26:28,110 –> 00:26:30,210
need are companion tools that

771
00:26:30,210 –> 00:26:31,860
are infused in those applications,

772
00:26:31,860 –> 00:26:33,760
not beside them, and AI

773
00:26:33,760 –> 00:26:35,370
is helping agents make these

774
00:26:35,370 –> 00:26:36,890
great judgment calls while they’re

775
00:26:36,890 –> 00:26:38,970
on an interaction not replacing

776
00:26:38,970 –> 00:26:40,900
agents. So if you remember

777
00:26:40,900 –> 00:26:41,670
a few years ago, Apple

778
00:26:41,670 –> 00:26:42,960
music was really popular for

779
00:26:42,970 –> 00:26:45,570
having real DJs curating playlists.

780
00:26:46,210 –> 00:26:47,910
It was about humans being

781
00:26:47,910 –> 00:26:49,220
important and part of that new

782
00:26:49,220 –> 00:26:50,500
offering they had around Apple

783
00:26:50,500 –> 00:26:52,000
music. I think we’re seeing

784
00:26:52,000 –> 00:26:53,290
that pendulum swing come back

785
00:26:53,290 –> 00:26:55,640
again to humans being crucial

786
00:26:55,690 –> 00:26:57,710
to the experience. And just

787
00:26:57,710 –> 00:26:59,180
a quick story there before

788
00:26:59,180 –> 00:27:00,700
I babble like I always

789
00:27:00,700 –> 00:27:02,210
do. When we look at

790
00:27:02,210 –> 00:27:04,050
large retailers, imagine you’re a

791
00:27:04,050 –> 00:27:05,670
parent moving your son or

792
00:27:05,670 –> 00:27:07,530
daughter into college. What if

793
00:27:07,530 –> 00:27:08,090
you went to one of

794
00:27:08,090 –> 00:27:09,240
those large retailers and bought

795
00:27:09,240 –> 00:27:10,130
all the items you need

796
00:27:10,130 –> 00:27:11,410
for a dorm, right? The

797
00:27:11,720 –> 00:27:13,530
air conditioning unit, maybe a

798
00:27:13,530 –> 00:27:15,100
mini fridge, maybe some food,

799
00:27:15,540 –> 00:27:17,440
all these different items. If

800
00:27:17,490 –> 00:27:18,570
the roommate already had them,

801
00:27:18,570 –> 00:27:19,520
you might want to return

802
00:27:19,520 –> 00:27:20,690
them. But think about how

803
00:27:20,690 –> 00:27:22,070
many return policies that is.

804
00:27:22,070 –> 00:27:23,160
And we’ve done the research

805
00:27:23,160 –> 00:27:24,740
and seen on average, these

806
00:27:24,740 –> 00:27:26,020
large retailers have upwards of

807
00:27:26,040 –> 00:27:28,500
19 different return policies. So

808
00:27:28,500 –> 00:27:29,420
if you call in just

809
00:27:29,420 –> 00:27:30,180
to figure out and what

810
00:27:30,180 –> 00:27:31,250
you can actually bring back

811
00:27:31,250 –> 00:27:33,010
and what’s a lost cause,

812
00:27:33,370 –> 00:27:34,240
that’s a lot for the

813
00:27:34,240 –> 00:27:35,660
agent to dig through. That

814
00:27:35,660 –> 00:27:36,690
means they’re going on hold.

815
00:27:36,690 –> 00:27:37,500
That means there’s a lot

816
00:27:37,540 –> 00:27:38,870
of ums and uhs as they try

817
00:27:38,870 –> 00:27:39,740
to figure it out on

818
00:27:39,740 –> 00:27:41,930
their end. Using these AI

819
00:27:41,930 –> 00:27:43,960
assisted technologies mean I can

820
00:27:43,960 –> 00:27:45,650
pull up the closest location

821
00:27:45,650 –> 00:27:46,650
to you based around your

822
00:27:46,650 –> 00:27:47,650
call and what you said,

823
00:27:47,650 –> 00:27:49,610
where you’re located, what college

824
00:27:49,950 –> 00:27:50,770
and then I can let

825
00:27:50,770 –> 00:27:53,040
the AI identify the nuances

826
00:27:53,040 –> 00:27:54,220
of what items are you

827
00:27:54,220 –> 00:27:56,260
returning and what’s the gotchas

828
00:27:56,260 –> 00:27:57,500
there that are important in

829
00:27:57,500 –> 00:27:59,470
that return process. This means

830
00:27:59,470 –> 00:28:00,480
I’m focused on you, the

831
00:28:00,480 –> 00:28:02,840
person calling in, the son

832
00:28:02,840 –> 00:28:03,850
or daughter you’ve just moved

833
00:28:03,850 –> 00:28:04,880
in and the situation you

834
00:28:04,880 –> 00:28:06,100
have at hand, not on

835
00:28:06,100 –> 00:28:08,280
these individual line items. So

836
00:28:08,280 –> 00:28:09,570
Kate, I’ll hand it back

837
00:28:09,570 –> 00:28:10,530
to you here for your

838
00:28:10,530 –> 00:28:11,820
final point and any questions

839
00:28:11,820 –> 00:28:12,790
or comments you have on

840
00:28:12,790 –> 00:28:14,970
this one too? Yeah. The

841
00:28:14,970 –> 00:28:16,020
one thing that I forgot

842
00:28:16,020 –> 00:28:17,150
to say is, and you

843
00:28:17,150 –> 00:28:18,640
said it really well, is

844
00:28:18,760 –> 00:28:21,280
agents have to be supported

845
00:28:21,280 –> 00:28:22,890
by these companion tools or

846
00:28:22,890 –> 00:28:24,660
desktop technologies to be able

847
00:28:24,660 –> 00:28:26,220
to focus on the conversation

848
00:28:26,220 –> 00:28:28,100
at hand. And there’s also

849
00:28:28,240 –> 00:28:30,610
technologies that are helping make

850
00:28:30,610 –> 00:28:32,500
agents more empathetic. For example,

851
00:28:32,500 –> 00:28:34,850
behavioral routing, being able to

852
00:28:34,850 –> 00:28:37,220
understand the conversation style of

853
00:28:37,220 –> 00:28:38,420
the customer and routed to

854
00:28:38,790 –> 00:28:40,140
the agent that’s got the same

855
00:28:40,260 –> 00:28:43,370
conversational style. Or for example,

856
00:28:43,440 –> 00:28:45,370
popping up on the agent’s

857
00:28:45,370 –> 00:28:49,530
screen for example, indicators of

858
00:28:52,030 –> 00:28:55,280
the customer’s emotion. Are they

859
00:28:55,360 –> 00:28:57,540
anxious or are they angry?

860
00:28:57,800 –> 00:28:59,090
And again, these are tools, they’re

861
00:28:59,500 –> 00:29:02,010
companion tools to not only

862
00:29:02,010 –> 00:29:03,820
help the agent work on the

863
00:29:03,820 –> 00:29:05,840
harder work, but as well

864
00:29:05,840 –> 00:29:09,500
emotionally connect with the customer. Because

865
00:29:09,500 –> 00:29:11,070
if you get these interactions,

866
00:29:11,070 –> 00:29:12,880
these live agent interactions right

867
00:29:12,880 –> 00:29:14,680
it actually has a disproportionate

868
00:29:14,850 –> 00:29:18,140
effect on customer satisfaction and

869
00:29:18,140 –> 00:29:21,010
they’re all for overall retention

870
00:29:21,550 –> 00:29:23,480
and loyalty to the brand.

871
00:29:23,480 –> 00:29:25,410
So again, these companion tools

872
00:29:25,410 –> 00:29:26,970
are really important to make

873
00:29:26,970 –> 00:29:27,970
sure that the agents are

874
00:29:28,060 –> 00:29:30,070
fully supported and that they’re

875
00:29:30,070 –> 00:29:32,640
able to concentrate on the

876
00:29:32,640 –> 00:29:34,790
conversation of the customer. So

877
00:29:38,400 –> 00:29:40,220
that goes to our next

878
00:29:40,220 –> 00:29:44,360
trend where as you infuse

879
00:29:44,710 –> 00:29:47,610
all of these companion tools, all

880
00:29:47,610 –> 00:29:49,710
this automation, all this AI

881
00:29:49,710 –> 00:29:52,140
into your contact center, the

882
00:29:52,140 –> 00:29:54,790
way that you staff your

883
00:29:54,790 –> 00:29:57,560
contact center has to change.

884
00:29:57,970 –> 00:30:00,310
And this is really interesting.

885
00:30:00,350 –> 00:30:02,470
Think about it this way.

886
00:30:04,040 –> 00:30:06,180
You now have great self

887
00:30:06,180 –> 00:30:10,210
service technology, self service process,

888
00:30:10,580 –> 00:30:12,970
knowledge management, FAQs on your

889
00:30:12,970 –> 00:30:16,300
websites, chat bots that are

890
00:30:16,300 –> 00:30:18,920
able to help answer the

891
00:30:18,920 –> 00:30:23,210
simple, the reproducible questions that your

892
00:30:23,350 –> 00:30:25,900
customers have. So ultimately what

893
00:30:25,900 –> 00:30:27,830
happens to your generalists, what

894
00:30:27,830 –> 00:30:28,990
happens to your tier one

895
00:30:28,990 –> 00:30:31,440
agents? And what many companies

896
00:30:31,440 –> 00:30:33,640
find is that these roles

897
00:30:34,310 –> 00:30:37,620
aren’t needed as much as

898
00:30:37,620 –> 00:30:39,100
they were a couple of

899
00:30:39,100 –> 00:30:41,320
years ago. So jobs are

900
00:30:41,320 –> 00:30:45,940
changing where companies need fewer

901
00:30:46,160 –> 00:30:48,220
of the lower tiered agents

902
00:30:48,440 –> 00:30:49,710
and they may take these

903
00:30:49,710 –> 00:30:52,790
agents and retrain them or

904
00:30:52,790 –> 00:30:55,070
repurpose them into new roles.

905
00:30:55,300 –> 00:30:57,030
What about having a tier

906
00:30:57,030 –> 00:30:58,720
one agent now be the

907
00:30:58,720 –> 00:31:01,750
bot supervisor who is supervising

908
00:31:01,750 –> 00:31:04,830
the bot who’s answering all

909
00:31:04,830 –> 00:31:06,930
the routine questions that the

910
00:31:07,500 –> 00:31:09,560
agent used to answer? The

911
00:31:09,560 –> 00:31:11,640
agent can take over when

912
00:31:11,640 –> 00:31:13,760
the automation fails or the

913
00:31:13,810 –> 00:31:17,700
agent can recommend new automations

914
00:31:17,800 –> 00:31:20,450
dependent on the customers’ incoming

915
00:31:20,450 –> 00:31:22,870
requests. But again, this bot

916
00:31:22,870 –> 00:31:25,670
supervisor or bot manager is

917
00:31:25,670 –> 00:31:27,430
a new role that is

918
00:31:27,430 –> 00:31:29,220
opening up in the contact center

919
00:31:29,220 –> 00:31:31,210
that’s perfect for a tier

920
00:31:31,210 –> 00:31:33,210
one agent and it’s a

921
00:31:33,210 –> 00:31:34,590
role that didn’t exist a couple of

922
00:31:34,910 –> 00:31:36,970
years ago. So what we

923
00:31:36,970 –> 00:31:37,980
also see is that some

924
00:31:37,980 –> 00:31:40,390
jobs are going to become

925
00:31:40,640 –> 00:31:42,660
a lot more important. For

926
00:31:42,660 –> 00:31:46,250
example, think about the roles

927
00:31:46,310 –> 00:31:49,310
that script or create the

928
00:31:49,310 –> 00:31:52,480
content that it fills your FAQs or your

929
00:31:52,480 –> 00:31:54,430
knowledge bases, here on the

930
00:31:54,430 –> 00:31:55,630
screen I call them knowledge

931
00:31:55,630 –> 00:31:58,440
workers. Or think about the

932
00:31:58,590 –> 00:32:00,730
tier three, tier four agents.

933
00:32:02,630 –> 00:32:03,630
The harder work is now

934
00:32:03,630 –> 00:32:05,060
getting to the contact center

935
00:32:05,060 –> 00:32:07,210
agent. And so your agents

936
00:32:07,210 –> 00:32:08,670
have to be retrained, they

937
00:32:08,670 –> 00:32:09,830
have to be up scaled

938
00:32:09,830 –> 00:32:10,870
or perhaps you need a

939
00:32:10,870 –> 00:32:13,130
whole new profile of agents

940
00:32:13,730 –> 00:32:14,900
to work on the really

941
00:32:14,900 –> 00:32:17,950
complex work. We call these

942
00:32:18,000 –> 00:32:19,780
folks super agents. Not only

943
00:32:19,780 –> 00:32:22,760
are they technically competent, they

944
00:32:22,760 –> 00:32:25,070
have all the skills to

945
00:32:25,070 –> 00:32:26,900
be able to answer the

946
00:32:26,900 –> 00:32:29,360
harder questions, but they also

947
00:32:29,360 –> 00:32:33,570
have great emotional intelligence to

948
00:32:33,570 –> 00:32:36,100
be able to relate to

949
00:32:36,100 –> 00:32:38,800
the customer in their anxious

950
00:32:38,800 –> 00:32:41,670
or angry or frustrated state.

951
00:32:42,100 –> 00:32:42,680
And then you’re going to

952
00:32:42,680 –> 00:32:43,680
have a whole new set

953
00:32:43,710 –> 00:32:44,960
of jobs that didn’t exist

954
00:32:44,960 –> 00:32:47,140
in the contact center. All

955
00:32:47,140 –> 00:32:49,800
the data science roles to

956
00:32:49,800 –> 00:32:50,770
be able to create the

957
00:32:50,780 –> 00:32:52,680
automations, to be able to

958
00:32:53,060 –> 00:32:55,650
create and manage and optimize

959
00:32:55,650 –> 00:32:58,560
the machine learning models. And

960
00:32:58,560 –> 00:33:01,010
then conversational designers. These are

961
00:33:01,010 –> 00:33:05,430
actually business analysts or they

962
00:33:05,430 –> 00:33:09,050
could even be former agents

963
00:33:09,300 –> 00:33:11,230
that are responsible for scripted

964
00:33:11,520 –> 00:33:13,720
bot conversations. So when we

965
00:33:13,720 –> 00:33:16,570
find is that the more

966
00:33:16,570 –> 00:33:18,980
you automate within your contact

967
00:33:18,980 –> 00:33:20,130
center, the more you add

968
00:33:20,130 –> 00:33:23,670
AI, your jobs will slowly

969
00:33:23,670 –> 00:33:25,080
change over time. And let

970
00:33:25,080 –> 00:33:26,530
me tell you two stories.

971
00:33:27,360 –> 00:33:28,480
First of all, there’s the

972
00:33:29,420 –> 00:33:33,850
tax service that we probably

973
00:33:33,850 –> 00:33:38,300
all use. They don’t hire

974
00:33:38,300 –> 00:33:39,860
agents anymore. They hire two

975
00:33:39,860 –> 00:33:42,150
different roles. The first role

976
00:33:42,150 –> 00:33:44,940
is a software engineer. Somebody

977
00:33:44,940 –> 00:33:46,610
who can trouble shoot their

978
00:33:46,610 –> 00:33:49,410
tax software. The second role

979
00:33:49,410 –> 00:33:50,810
that they hire for is

980
00:33:50,810 –> 00:33:53,040
a tax accountant, somebody who

981
00:33:53,040 –> 00:33:56,240
is able to answer the

982
00:33:56,240 –> 00:33:59,140
harder tax questions that customers

983
00:33:59,140 –> 00:34:02,450
have. So again, they’ve seen

984
00:34:03,110 –> 00:34:05,160
their jobs change over time.

985
00:34:05,460 –> 00:34:07,570
Pier 1 Imports is really

986
00:34:07,570 –> 00:34:10,780
interesting example. So Pier 1

987
00:34:10,780 –> 00:34:16,480
sells modern furniture over the

988
00:34:16,480 –> 00:34:20,370
web. They don’t hire agents

989
00:34:20,370 –> 00:34:23,170
anymore, they hire folks with

990
00:34:24,230 –> 00:34:26,630
design degrees or folks who

991
00:34:26,630 –> 00:34:27,980
have a passion for home

992
00:34:27,980 –> 00:34:29,540
decorating because the questions that

993
00:34:29,540 –> 00:34:30,930
they get aren’t about the

994
00:34:30,930 –> 00:34:32,860
dimensions of table or chair

995
00:34:32,860 –> 00:34:36,040
for example. But questions like,

996
00:34:36,270 –> 00:34:38,060
I have yellow walls and

997
00:34:38,060 –> 00:34:39,450
I have a green carpet.

998
00:34:39,480 –> 00:34:41,250
Would the orange couch look

999
00:34:41,250 –> 00:34:42,640
better, would the green couch

1000
00:34:42,640 –> 00:34:44,030
look better? So it’s more

1001
00:34:44,030 –> 00:34:47,500
consultancy and advice and they

1002
00:34:47,500 –> 00:34:50,470
find that there’s only a

1003
00:34:50,470 –> 00:34:52,220
select number of folks that

1004
00:34:52,220 –> 00:34:54,780
have a real passion for

1005
00:34:54,780 –> 00:34:56,720
home decorating or design and

1006
00:34:56,720 –> 00:34:58,510
they go after those roles.

1007
00:34:58,510 –> 00:34:59,870
What they’ve also found is

1008
00:34:59,870 –> 00:35:02,000
that they can’t source those

1009
00:35:02,000 –> 00:35:05,170
roles within a small geographic

1010
00:35:05,170 –> 00:35:07,070
area to be able to

1011
00:35:07,070 –> 00:35:08,540
staff their contact center. And

1012
00:35:08,540 –> 00:35:10,170
so they actually have had

1013
00:35:10,170 –> 00:35:12,280
to move to a remote

1014
00:35:12,760 –> 00:35:14,300
work at home model for

1015
00:35:14,300 –> 00:35:17,810
their contact center. The other

1016
00:35:17,810 –> 00:35:19,460
big change that’s going to

1017
00:35:19,460 –> 00:35:22,110
happen is as the harder work

1018
00:35:22,360 –> 00:35:23,760
gets into your contact center,

1019
00:35:25,710 –> 00:35:27,300
the way that you measure

1020
00:35:27,360 –> 00:35:30,010
outcomes has to change. You

1021
00:35:30,010 –> 00:35:31,850
may not want to hold

1022
00:35:31,920 –> 00:35:34,110
your agents’ feet to the

1023
00:35:34,110 –> 00:35:37,230
fire anymore and monitor their

1024
00:35:37,230 –> 00:35:39,410
handle times and their speed

1025
00:35:39,410 –> 00:35:41,330
of answer and all the

1026
00:35:41,330 –> 00:35:43,210
other productivity measures that we

1027
00:35:43,210 –> 00:35:45,350
use in the contact center. You may

1028
00:35:45,350 –> 00:35:47,670
want to be more focused on

1029
00:35:47,730 –> 00:35:49,890
outcomes. How good was the

1030
00:35:49,890 –> 00:35:53,970
interaction, customer satisfaction, quality of

1031
00:35:53,970 –> 00:35:56,440
service metrics that then can

1032
00:35:56,440 –> 00:36:00,100
be tied to customer retention

1033
00:36:00,850 –> 00:36:04,150
and customer lifetime value and

1034
00:36:04,150 –> 00:36:09,040
ultimately company revenue. Shopify for

1035
00:36:09,040 –> 00:36:12,320
example, in one of their

1036
00:36:12,320 –> 00:36:14,300
contact centers they have over 500

1037
00:36:14,300 –> 00:36:17,530
agents and they have moved

1038
00:36:17,580 –> 00:36:19,780
to a quality of service

1039
00:36:19,780 –> 00:36:22,300
metric. They still measure handle

1040
00:36:22,300 –> 00:36:25,150
times mainly to be able

1041
00:36:25,150 –> 00:36:28,610
to appropriately staff their contact

1042
00:36:28,610 –> 00:36:32,070
center, but their agents aren’t

1043
00:36:32,430 –> 00:36:37,060
emphasized and penalized on handle

1044
00:36:37,060 –> 00:36:39,740
time or of speed of answers. Again,

1045
00:36:39,780 –> 00:36:42,110
the only measure of success

1046
00:36:42,550 –> 00:36:45,070
and measure of agent success is

1047
00:36:45,070 –> 00:36:48,600
the quality of service. So

1048
00:36:48,600 –> 00:36:50,000
again, as you add AI

1049
00:36:50,000 –> 00:36:51,040
and automation, you’ve got to

1050
00:36:51,040 –> 00:36:54,410
rethink not only the jobs

1051
00:36:54,920 –> 00:36:57,880
but measures of success metrics

1052
00:36:58,170 –> 00:36:59,690
and as well as your

1053
00:36:59,690 –> 00:37:04,940
workforce staffing policies. So Joe,

1054
00:37:05,670 –> 00:37:07,560
what do you think? I

1055
00:37:07,560 –> 00:37:08,900
love how you brought about

1056
00:37:08,970 –> 00:37:09,850
all of the changes that

1057
00:37:09,850 –> 00:37:10,830
are happening. I think this

1058
00:37:10,830 –> 00:37:12,840
is a really big thing

1059
00:37:12,840 –> 00:37:14,370
and we talk a lot

1060
00:37:14,370 –> 00:37:16,100
about experiences today, right? I

1061
00:37:16,100 –> 00:37:18,070
think we look at experience

1062
00:37:18,070 –> 00:37:19,460
as the platform being our third

1063
00:37:19,460 –> 00:37:20,460
one, and that is about

1064
00:37:20,460 –> 00:37:22,010
as umbrella as umbrella statements

1065
00:37:22,010 –> 00:37:22,940
can get. I want to

1066
00:37:22,940 –> 00:37:24,990
give some detail here around

1067
00:37:24,990 –> 00:37:25,770
what we mean when we

1068
00:37:25,770 –> 00:37:27,390
say experience of the platform

1069
00:37:27,390 –> 00:37:29,430
and why that’s important. So

1070
00:37:29,430 –> 00:37:30,910
many companies are going for

1071
00:37:31,290 –> 00:37:33,600
personalized at scale, right? Making

1072
00:37:33,600 –> 00:37:35,060
sure that every customer gets

1073
00:37:35,060 –> 00:37:36,440
the interaction they’re looking for.

1074
00:37:36,800 –> 00:37:37,610
There’s a few that do

1075
00:37:37,610 –> 00:37:39,190
this really well. When you look

1076
00:37:39,190 –> 00:37:40,480
at Netflix, you don’t want

1077
00:37:40,480 –> 00:37:42,550
to browse 20000 movies, that’s

1078
00:37:42,630 –> 00:37:43,750
probably not why you’re paying

1079
00:37:43,750 –> 00:37:45,310
for it. What do you want to do is

1080
00:37:45,310 –> 00:37:46,360
watch a comedy on a

1081
00:37:46,480 –> 00:37:47,560
Thursday night and you only

1082
00:37:47,560 –> 00:37:48,110
have an hour and a

1083
00:37:48,110 –> 00:37:49,800
half. And when you look

1084
00:37:49,800 –> 00:37:50,930
at other servers there’s like

1085
00:37:50,930 –> 00:37:52,310
Lynda which is now LinkedIn

1086
00:37:52,370 –> 00:37:54,990
Learning. I don’t want to just take an

1087
00:37:54,990 –> 00:37:56,970
Adobe premiere pro 101 course

1088
00:37:57,080 –> 00:37:57,520
to learn how I’ll be

1089
00:37:58,350 –> 00:38:00,220
using this software and I want to be

1090
00:38:00,220 –> 00:38:02,190
a film producer. So it’s on

1091
00:38:02,190 –> 00:38:04,220
these companies to curate the

1092
00:38:04,710 –> 00:38:06,620
just wild amounts of content

1093
00:38:06,850 –> 00:38:08,500
they have and make it

1094
00:38:08,500 –> 00:38:10,210
personalized to the person using

1095
00:38:10,210 –> 00:38:11,880
it. This is the year

1096
00:38:11,880 –> 00:38:13,270
that we have that capability

1097
00:38:13,550 –> 00:38:14,670
and this is the year that I think

1098
00:38:14,670 –> 00:38:15,810
we started to see that being

1099
00:38:15,810 –> 00:38:17,670
necessary in the contact centers.

1100
00:38:18,620 –> 00:38:20,050
Today we talked about new

1101
00:38:20,050 –> 00:38:21,840
channels opening up these homes

1102
00:38:21,840 –> 00:38:23,940
or self service agents being

1103
00:38:25,260 –> 00:38:26,830
nudged in certain ways because of

1104
00:38:27,060 –> 00:38:28,350
AI and AI getting this

1105
00:38:28,350 –> 00:38:29,500
new insight. Well something that

1106
00:38:29,500 –> 00:38:31,530
was actually brought up in

1107
00:38:31,530 –> 00:38:32,750
a recent webinar with Ian

1108
00:38:32,750 –> 00:38:34,630
Jacobs towards the notion that

1109
00:38:34,680 –> 00:38:36,920
data science doesn’t always know

1110
00:38:36,920 –> 00:38:38,500
contact center and contact center may

1111
00:38:38,790 –> 00:38:40,050
not always know data science.

1112
00:38:40,560 –> 00:38:42,420
So having a platform that

1113
00:38:42,420 –> 00:38:44,090
is unified in that its

1114
00:38:44,090 –> 00:38:46,140
ability to understand why are

1115
00:38:46,140 –> 00:38:47,590
we engaging with that customer

1116
00:38:47,590 –> 00:38:48,830
at this moment of truth

1117
00:38:48,830 –> 00:38:51,070
here and are we personalizing

1118
00:38:51,070 –> 00:38:53,320
this current interaction, the realtime

1119
00:38:53,320 –> 00:38:54,550
data we have about them

1120
00:38:55,060 –> 00:38:56,680
and historical context that we’re

1121
00:38:56,680 –> 00:38:58,360
pulling in from integrations around

1122
00:38:58,360 –> 00:39:00,820
them. Lastly, what about that

1123
00:39:00,820 –> 00:39:02,900
context? That context is so

1124
00:39:02,900 –> 00:39:05,020
important so that every conversation

1125
00:39:05,370 –> 00:39:06,500
feels like that customer is

1126
00:39:06,500 –> 00:39:08,420
reaching out to some conversation of

1127
00:39:08,420 –> 00:39:10,150
the company, not just another

1128
00:39:10,150 –> 00:39:11,460
agent that is only talking to them

1129
00:39:11,460 –> 00:39:13,300
right now, but an ongoing

1130
00:39:13,300 –> 00:39:14,970
conversation that not only feeds

1131
00:39:14,970 –> 00:39:17,010
into what’s happening between this

1132
00:39:17,010 –> 00:39:19,280
customer and agent relationship but

1133
00:39:19,280 –> 00:39:20,910
also what type of training

1134
00:39:20,910 –> 00:39:22,520
are we providing. We talked

1135
00:39:22,520 –> 00:39:23,350
a lot about that on

1136
00:39:23,350 –> 00:39:25,780
the WEM side around if

1137
00:39:25,780 –> 00:39:27,400
we’re training our agents, the

1138
00:39:27,400 –> 00:39:28,410
culture we’re building for them

1139
00:39:28,410 –> 00:39:30,130
should be personalized to what they

1140
00:39:30,280 –> 00:39:31,800
need to excel on their

1141
00:39:31,800 –> 00:39:32,980
own as well. I think

1142
00:39:33,120 –> 00:39:34,130
that’s really important here is

1143
00:39:34,130 –> 00:39:35,980
that as personalization comes into

1144
00:39:35,980 –> 00:39:37,370
the tools provided to everyone

1145
00:39:37,380 –> 00:39:38,680
in the company, not just

1146
00:39:38,680 –> 00:39:40,200
the interactions that we have here. There’s lot

1147
00:39:40,200 –> 00:39:42,800
we can learn. So I

1148
00:39:42,800 –> 00:39:44,260
have babbled, but what I want

1149
00:39:44,300 –> 00:39:46,750
to talk about is experience of platform being

1150
00:39:46,750 –> 00:39:48,500
important as having a commonality

1151
00:39:48,500 –> 00:39:49,760
to do this in unison

1152
00:39:50,060 –> 00:39:51,130
across all the things we

1153
00:39:51,130 –> 00:39:52,900
talked about today. And with

1154
00:39:52,900 –> 00:39:54,450
that I want to end

1155
00:39:54,450 –> 00:39:55,400
on our, what it means

1156
00:39:55,400 –> 00:39:56,540
slides before we open up

1157
00:39:56,540 –> 00:39:57,130
to that Q& A. So

1158
00:39:58,250 –> 00:39:58,850
Kate to kind of bring

1159
00:39:58,850 –> 00:40:00,090
it back to you here,

1160
00:40:00,350 –> 00:40:02,170
is there any of these five points

1161
00:40:02,170 –> 00:40:03,640
that you wanted to highlight as

1162
00:40:03,640 –> 00:40:04,880
we end today, before the

1163
00:40:04,880 –> 00:40:08,280
Q& A? I think it

1164
00:40:08,670 –> 00:40:10,010
all starts with the customer,

1165
00:40:10,390 –> 00:40:14,500
understanding your customer, whether you’re

1166
00:40:14,500 –> 00:40:16,170
a consumer brand or you’re a B2B

1167
00:40:17,140 –> 00:40:19,750
brand, understand the customer and

1168
00:40:19,750 –> 00:40:22,830
understand the value of supporting

1169
00:40:22,830 –> 00:40:24,470
your customer in the way

1170
00:40:24,470 –> 00:40:25,670
that they want to be

1171
00:40:25,670 –> 00:40:28,280
supported because better customer experiences

1172
00:40:28,510 –> 00:40:30,740
will ultimately translate into a

1173
00:40:30,740 –> 00:40:32,890
more loyal customer base that

1174
00:40:32,890 –> 00:40:35,630
will then translate into increased

1175
00:40:35,630 –> 00:40:38,380
customer retention and ultimately revenue.

1176
00:40:38,840 –> 00:40:40,470
And so understanding your customer,

1177
00:40:40,470 –> 00:40:41,880
you also have to understand

1178
00:40:41,880 –> 00:40:42,740
that they want their time

1179
00:40:42,740 –> 00:40:44,380
to be valued and that

1180
00:40:44,380 –> 00:40:45,920
they want to self serve

1181
00:40:46,150 –> 00:40:47,030
as a first point of

1182
00:40:47,030 –> 00:40:48,840
contact with the company and

1183
00:40:48,840 –> 00:40:50,390
that they are moving to

1184
00:40:50,390 –> 00:40:53,210
digital interactions. Whether it’s voice

1185
00:40:53,210 –> 00:40:56,140
self service, whether it’s asynchronous

1186
00:40:56,140 –> 00:40:58,580
messaging or whether it’s synchronous

1187
00:40:58,580 –> 00:41:00,280
chat, but you really have

1188
00:41:00,280 –> 00:41:03,680
to understand your customers, the

1189
00:41:03,680 –> 00:41:04,850
way they want to interact

1190
00:41:04,850 –> 00:41:06,100
with you and support you’re

1191
00:41:06,100 –> 00:41:08,490
customers and the modalities that they

1192
00:41:08,490 –> 00:41:10,450
want to use. As you

1193
00:41:10,450 –> 00:41:11,460
do that, you’re going to

1194
00:41:11,460 –> 00:41:12,930
find that your customers want

1195
00:41:13,260 –> 00:41:14,230
to engage with you more

1196
00:41:14,230 –> 00:41:15,410
and more. It’s a two

1197
00:41:15,410 –> 00:41:17,220
way relationship but you can’t

1198
00:41:17,220 –> 00:41:19,430
keep up with the ballooning

1199
00:41:19,430 –> 00:41:21,520
volumes of interactions. So you’ve got to

1200
00:41:21,520 –> 00:41:23,800
turn to AI and automation

1201
00:41:23,800 –> 00:41:25,280
to be able to automate

1202
00:41:25,330 –> 00:41:29,020
as much of the interaction

1203
00:41:29,080 –> 00:41:30,900
or the engagement as possible

1204
00:41:31,220 –> 00:41:34,310
and then leave the value

1205
00:41:34,310 –> 00:41:37,030
added interactions to humans. So

1206
00:41:37,030 –> 00:41:38,660
it’s AI and automation, like

1207
00:41:38,660 –> 00:41:41,240
Joe said, working together with

1208
00:41:42,040 –> 00:41:44,540
your agents. As you add

1209
00:41:44,540 –> 00:41:46,470
AI and automation to your

1210
00:41:46,470 –> 00:41:50,870
operations, realize that the work

1211
00:41:51,030 –> 00:41:52,960
that your line agents do,

1212
00:41:53,000 –> 00:41:54,560
whether they’re digital agents or

1213
00:41:54,560 –> 00:41:55,770
whether they’re phone agents is

1214
00:41:56,150 –> 00:41:57,090
going to change, it’s going

1215
00:41:57,090 –> 00:41:59,430
to get harder. So your

1216
00:41:59,430 –> 00:42:00,650
interactions are going to get

1217
00:42:00,640 –> 00:42:03,090
longer, the work is going

1218
00:42:03,090 –> 00:42:05,160
to get harder. And so

1219
00:42:05,160 –> 00:42:08,030
you need to train to

1220
00:42:08,030 –> 00:42:09,670
up level your agents. You

1221
00:42:09,670 –> 00:42:11,540
need to staff them differently,

1222
00:42:11,540 –> 00:42:13,180
you need to measure them

1223
00:42:13,180 –> 00:42:15,750
differently. You need to think

1224
00:42:15,750 –> 00:42:18,240
about career pathing them. You need

1225
00:42:18,240 –> 00:42:20,170
to make your agents comfortable

1226
00:42:20,230 –> 00:42:22,630
with AI and automation and

1227
00:42:22,630 –> 00:42:24,270
explain the value of these

1228
00:42:24,270 –> 00:42:25,850
technologies to agents and then

1229
00:42:25,850 –> 00:42:30,830
career path them into roles

1230
00:42:30,830 –> 00:42:32,780
where they have a greater

1231
00:42:32,780 –> 00:42:35,840
impact to the end customer.

1232
00:42:36,140 –> 00:42:37,210
If you do that well, you’re going to

1233
00:42:37,480 –> 00:42:39,030
find out that your agents want to

1234
00:42:39,030 –> 00:42:42,340
stay with you longer. Your

1235
00:42:42,340 –> 00:42:44,240
contact center’s actually becoming a

1236
00:42:44,240 –> 00:42:45,590
more attractive place to work

1237
00:42:45,590 –> 00:42:51,060
in. And again, look at

1238
00:42:51,060 –> 00:42:52,260
the measures of success. I

1239
00:42:52,260 –> 00:42:53,700
guess that’s my bullet five

1240
00:42:54,390 –> 00:42:56,490
and think back to being

1241
00:42:56,490 –> 00:42:58,410
customer centric, think about customer

1242
00:42:58,410 –> 00:43:01,210
centric measures of success. And Joe what

1243
00:43:01,210 –> 00:43:01,940
else? What did I miss?

1244
00:43:03,000 –> 00:43:04,070
I know everyone has heard

1245
00:43:04,070 –> 00:43:05,220
enough from me today, but

1246
00:43:05,220 –> 00:43:05,870
if I think I can

1247
00:43:05,870 –> 00:43:07,170
end it with one sentiment,

1248
00:43:07,410 –> 00:43:08,440
it all comes down to

1249
00:43:08,440 –> 00:43:10,140
what you said, it’s trust.

1250
00:43:10,580 –> 00:43:11,390
Even before we get to

1251
00:43:11,390 –> 00:43:12,320
the data we’d like to

1252
00:43:12,320 –> 00:43:14,050
use to build machine learning

1253
00:43:14,050 –> 00:43:15,400
models to help our agents,

1254
00:43:15,400 –> 00:43:16,420
it just comes down to

1255
00:43:16,420 –> 00:43:17,460
do we have that trust

1256
00:43:17,460 –> 00:43:19,080
with the customer? And that’s

1257
00:43:19,080 –> 00:43:20,680
the seed. I think it’s

1258
00:43:20,680 –> 00:43:22,900
so important that you construct

1259
00:43:22,900 –> 00:43:25,340
these interactions and these experiences

1260
00:43:25,340 –> 00:43:26,290
that are built around the

1261
00:43:26,290 –> 00:43:28,090
notion of is this something

1262
00:43:28,090 –> 00:43:29,270
that’s good for the customer?

1263
00:43:29,610 –> 00:43:30,390
And then you’ll have the

1264
00:43:30,400 –> 00:43:31,810
data to make those insights.

1265
00:43:31,970 –> 00:43:32,580
And then if you take

1266
00:43:32,580 –> 00:43:33,500
care of that data and use

1267
00:43:33,500 –> 00:43:35,090
it effectively, you have those

1268
00:43:35,090 –> 00:43:36,470
insights to train your agents

1269
00:43:36,470 –> 00:43:37,300
and help them on those

1270
00:43:37,300 –> 00:43:39,230
interactions. But it all starts

1271
00:43:39,230 –> 00:43:40,510
with the notion that you

1272
00:43:40,510 –> 00:43:41,780
have to have that trust

1273
00:43:42,050 –> 00:43:44,410
to get that ability. And

1274
00:43:44,410 –> 00:43:45,960
with that, I think we

1275
00:43:45,960 –> 00:43:46,700
can open it up to a

1276
00:43:46,700 –> 00:43:48,110
few questions here today too.

1277
00:43:48,170 –> 00:43:49,310
Thanks so much to everyone

1278
00:43:49,310 –> 00:43:50,250
and again for sticking with

1279
00:43:50,250 –> 00:43:55,080
us here. Thanks Joe. So

1280
00:43:55,160 –> 00:43:56,970
to remind everybody, if you

1281
00:43:56,970 –> 00:43:57,900
want to participate in the

1282
00:43:57,900 –> 00:43:58,760
quick Q& A that we’re

1283
00:43:58,760 –> 00:43:59,910
going to have time for,

1284
00:44:00,900 –> 00:44:01,590
go ahead and throw those

1285
00:44:01,590 –> 00:44:03,130
questions into the Q& A window

1286
00:44:03,130 –> 00:44:03,980
in the top center of

1287
00:44:03,980 –> 00:44:05,870
your screen. And although we

1288
00:44:05,870 –> 00:44:06,900
are almost at a time,

1289
00:44:06,910 –> 00:44:08,660
we’ll have enough time for

1290
00:44:08,660 –> 00:44:09,790
about one question that we

1291
00:44:09,790 –> 00:44:11,050
have so far. But don’t

1292
00:44:11,050 –> 00:44:12,730
fret, throw your questions in

1293
00:44:12,730 –> 00:44:13,780
there and we’ll follow up

1294
00:44:13,780 –> 00:44:15,290
with you via email within

1295
00:44:15,290 –> 00:44:16,480
the next few business days.

1296
00:44:17,580 –> 00:44:18,730
So we did have one

1297
00:44:18,730 –> 00:44:21,290
question regarding demographics Kate, do

1298
00:44:21,860 –> 00:44:22,730
or do you have any

1299
00:44:22,730 –> 00:44:25,270
information of these trends that you discussed

1300
00:44:25,810 –> 00:44:27,970
today or are the same

1301
00:44:27,970 –> 00:44:29,680
across all age groups? Or

1302
00:44:29,680 –> 00:44:30,440
can you go into a

1303
00:44:30,440 –> 00:44:33,490
little bit about the demographics? Yeah,

1304
00:44:33,490 –> 00:44:34,960
they’re basically the same across

1305
00:44:34,960 –> 00:44:38,380
all age groups except the…

1306
00:44:41,570 –> 00:44:44,410
what’s the demographic of a 75

1307
00:44:44,410 –> 00:44:46,570
year old plus? I forget.

1308
00:44:46,570 –> 00:44:48,120
It’s not the golden generation.

1309
00:44:48,120 –> 00:44:53,470
Is it the silent generation? So

1310
00:44:53,500 –> 00:44:57,750
baby boomers, gen Xs, millennials,

1311
00:45:02,420 –> 00:45:06,440
gen Zs, all show that

1312
00:45:06,440 –> 00:45:10,050
they are… because self service has

1313
00:45:10,050 –> 00:45:12,010
gone so good, they are

1314
00:45:12,010 –> 00:45:13,350
self- serving as a first

1315
00:45:13,350 –> 00:45:14,520
point of contact. Of course

1316
00:45:14,520 –> 00:45:17,230
the younger generations self serve

1317
00:45:17,230 –> 00:45:19,350
at a rate that’s higher

1318
00:45:19,990 –> 00:45:21,310
and more frequent than the

1319
00:45:21,310 –> 00:45:24,320
older generations. But all demographics

1320
00:45:24,320 –> 00:45:26,110
self serve as a first point of contact

1321
00:45:26,110 –> 00:45:28,110
and all demographics have turned

1322
00:45:28,110 –> 00:45:29,610
to digital engagement to be

1323
00:45:29,610 –> 00:45:32,290
able to reduce friction with

1324
00:45:32,290 –> 00:45:34,410
the exception of the, I

1325
00:45:34,410 –> 00:45:35,410
think it’s the 70 or

1326
00:45:35,410 –> 00:45:37,830
75 plus age group that

1327
00:45:37,830 –> 00:45:40,030
is still very phone centric.

1328
00:45:40,320 –> 00:45:42,650
There are some geographic differences,

1329
00:45:42,690 –> 00:45:48,830
there are some slight demographic

1330
00:45:48,830 –> 00:45:51,070
differences. But the trends that

1331
00:45:51,070 –> 00:45:52,910
we have articulated on this

1332
00:45:52,910 –> 00:45:59,070
webinar are fairly common, again,

1333
00:45:59,070 –> 00:46:02,390
across all demographics. So the

1334
00:46:02,390 –> 00:46:03,420
data that I showed was

1335
00:46:03,420 –> 00:46:04,710
from dimension data. If you

1336
00:46:04,710 –> 00:46:06,280
go to their site and

1337
00:46:06,280 –> 00:46:09,100
you can actually segment it

1338
00:46:09,160 –> 00:46:10,780
and drill into it by

1339
00:46:10,780 –> 00:46:13,590
geography and by demographic and

1340
00:46:14,890 –> 00:46:17,410
again, you’ll see there are

1341
00:46:17,410 –> 00:46:19,300
regional differences, there are demographic

1342
00:46:19,300 –> 00:46:23,200
differences, but the overarching statements

1343
00:46:23,200 –> 00:46:24,710
that we made are accurate

1344
00:46:25,470 –> 00:46:26,510
and are reflected in the

1345
00:46:26,510 –> 00:46:29,680
data. Joe, anything you want

1346
00:46:29,680 –> 00:46:31,640
to add? I think that

1347
00:46:31,640 –> 00:46:32,670
was a great way to

1348
00:46:32,670 –> 00:46:34,150
end it. I know there’s

1349
00:46:34,150 –> 00:46:35,480
more questions in there and

1350
00:46:35,480 –> 00:46:36,880
we can absolutely follow up

1351
00:46:36,880 –> 00:46:38,410
on those, but definitely some

1352
00:46:38,410 –> 00:46:39,950
deeper dives into the nuances

1353
00:46:39,950 –> 00:46:41,300
of agent assist or even

1354
00:46:41,600 –> 00:46:43,210
how business users can have

1355
00:46:43,210 –> 00:46:44,300
a big effect on bot

1356
00:46:44,300 –> 00:46:45,650
building and not need a

1357
00:46:45,650 –> 00:46:47,010
data scientist and everything. But

1358
00:46:47,540 –> 00:46:48,340
we will make sure to

1359
00:46:48,340 –> 00:46:49,180
follow up on that as

1360
00:46:49,180 –> 00:46:53,700
well. And to that, we

1361
00:46:53,700 –> 00:46:54,720
will go ahead and start

1362
00:46:54,720 –> 00:46:56,650
to close out today. So

1363
00:46:56,650 –> 00:46:57,390
all of the data that we

1364
00:46:57,390 –> 00:46:59,100
talked about, all of these trends

1365
00:46:59,100 –> 00:47:00,920
that we discussed within the

1366
00:47:00,920 –> 00:47:02,580
resource list below the Q& A

1367
00:47:02,580 –> 00:47:03,370
window, we do have the

1368
00:47:03,370 –> 00:47:04,810
full report so be sure

1369
00:47:04,810 –> 00:47:06,130
to click and download that

1370
00:47:06,350 –> 00:47:07,650
today. And also be sure

1371
00:47:07,650 –> 00:47:09,260
to check out our upcoming

1372
00:47:09,260 –> 00:47:10,810
webinars and you can click

1373
00:47:10,810 –> 00:47:11,770
the links to that page

1374
00:47:11,770 –> 00:47:13,760
as well. Also as a

1375
00:47:13,760 –> 00:47:16,610
friendly reminder, when you click

1376
00:47:16,610 –> 00:47:17,480
on those, they’ll open up

1377
00:47:17,480 –> 00:47:18,190
in a new tab. Be

1378
00:47:18,190 –> 00:47:19,090
sure to click them before

1379
00:47:19,090 –> 00:47:21,230
today’s session closes out or

1380
00:47:21,230 –> 00:47:22,130
you will receive an on

1381
00:47:22,130 –> 00:47:23,340
demand recording within the next

1382
00:47:23,340 –> 00:47:24,820
few business days. So just

1383
00:47:24,820 –> 00:47:26,080
be on the lookout. And

1384
00:47:26,080 –> 00:47:27,200
with that, on behalf of

1385
00:47:27,200 –> 00:47:28,590
Joe, Kate and the entire

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00:47:28,590 –> 00:47:30,060
Genesys team, we thank you

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00:47:30,060 –> 00:47:31,790
again for joining today’s webcast,

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00:47:31,870 –> 00:47:33,780
Mega Trends Shaping Customer Service

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00:47:33,780 –> 00:47:35,850
in 2020. Until next time,

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00:47:35,980 –> 00:47:37,010
have a good one everyone.

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Bye bye.