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Analyst Webinar

Megatrends shaping customer service in 2020

Feat. Guest speaker, Kate Leggett from Forrester Research

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The future of customer service starts with making your call center more human. It’s about understanding your customers and their specific problems. To do this, you need to give agents the right training and modern tools to provide a complete picture of your customers. Then, agents can make decisions and solve problems quickly.

Get a glimpse of the future. Join guest speaker, Kate Leggett from Forrester Research and Joe Ciuffo from Genesys as they discuss megatrends that are redefining contact center protocols in 2020. In this on-demand webinar, you’ll discover:

  • How artificial intelligence (AI) is driving change in the contact center
  • The evolution of customer-agent interactions
  • How technology affects workforce management

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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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viewing or listening to today’s

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presentation, refresh your browser and

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make sure that it’s up

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to date to support HTML

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5 as this usually fixes

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any console issues you may

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experience. Also it my help

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to switch over to Chrome

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or Firefox as well are

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as these are the best

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browsers to support the webcast

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platform. And if you’re having

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trouble seeing the slides or

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the webcams today, you’re welcome

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to enlarge those by dragging

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the bottom right corner of

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each window. Also, note this

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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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questions into the Q& A

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window in the middle of

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your screen and we’ll answer

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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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via email within the next

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few business days. And to

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note that if something happens

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during the webinar and you

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miss something, don’t worry, you

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will receive the on demand

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recording via email from ON24

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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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won’t take you away, so

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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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00:09:48,330 –> 00:09:50,090
AI and automation being able

274
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to increase efficiency on technology

275
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like RPA or automatic case

276
00:09:56,510 –> 00:09:59,250
classification or automatic routing to

277
00:09:59,250 –> 00:10:01,460
be able to offload all

278
00:10:01,460 –> 00:10:04,100
the reproducible or lower value

279
00:10:04,100 –> 00:10:06,900
tasks from agents. Moving up

280
00:10:06,900 –> 00:10:08,680
the value curve, you’ve got

281
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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
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sure to click them before

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today’s session closes out or

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you will receive an on

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00:47:22,130 –> 00:47:23,340
demand recording within the next

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few business days. So just

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be on the lookout. And

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with that, on behalf of

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Joe, Kate and the entire

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Genesys team, we thank you

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again for joining today’s webcast,

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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.

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Meet the Speakers

Kate leggett webinar image

Kate Leggett
VP & Principal Analyst
Forrester Research

Joe cuiffo webinar image

Joe Ciuffo
Product Marketing Director
Genesys

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