Demo Webinar

Connect the dots with Genesys and Adobe

Featuring guest speakers from Adobe and IDC

Break down the data silos between sales, marketing and service for more connected customer experiences
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The events of 2020 changed the customer experience irrevocably. And 2021 is on course to do the same. Keeping up with customer expectations for personalization requires creating truly connected experiences.

This all starts with the data your brand uses daily. Organizations must break down data silos between marketing, commerce, sales and service departments, ensuring every interaction is as seamless as possible.

Join IDC, Adobe and Genesys in this live webinar to learn how to:

  • Manage the rise of digital-first customer expectations
  • Eliminate data silos by integrating Genesys Cloud CX and Adobe Experience Platform
  • Turn data into actionable insights that deliver results
  • Enable more intelligent, individualized customer interactions

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Good morning, evening and afternoon everyone, this is Josh Reed from

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the digital events team here at Genesys. And let me

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be the first to say welcome to our live demonstration

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today, ” Connect the Dots with Genesys and Adobe.” As I

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always do, I’m going to keep it short and sweet,

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but I do have just a couple housekeeping items to go

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through before we get started today. So first off, if you

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experience any problems viewing or listening to today’s demo, do

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me a favor and just do a quick refresh on

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your browser and maybe switch over to something like Chrome

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or Firefox as well, as these are the best browsers

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that support the webcast platform. You also have the ability

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to enlarge the slide window and the video window by

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dragging any of the corners of said windows, and you

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can adjust those throughout the presentation. And also note that

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this webcast is designed to be an interactive experience between

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you and our presenters today. So first off, if you

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have any questions throughout the presentation, throw those into the

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Q& A window below the slide window. Now, we will

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have a live Q& A at the end of the

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presentation today but sometimes time gets away from us. So,

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if that does happen, what we’ll do is we’ll follow

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up with you via email in a few business days.

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And also note that this is being recorded, so if

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you miss anything throughout today’s presentation, don’t fret. You’ll receive

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a link to the recording from ON24 within the next

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few business days, and that will have full playback capabilities.

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I also encourage you to check out the resource box

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below the slides as well. Clicking through won’t take you

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away, they’ll just open up in a new tab in

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your browser. But these resources expand on today’s topic. And

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lastly, we welcome and appreciate your feedback, so you’ll have

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the opportunity to fill out a short survey that’s going

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to show up at the end of today’s presentation. You

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can also click on the last icon below in that

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survey widget if you want to knock it out early,

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but we tailor these presentations to exactly what you want

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to learn more about, so I encourage you to go

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ahead and check that out whenever you get a chance

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throughout today’s presentation. And like I said, short and sweet.

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So, today we have three excellent presenters excited to discuss

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how to manage the rise of digital first customer expectations,

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eliminate data silos by integrating Genesys Cloud CX and Adobe Experience

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platforms, turn data into actionable insights that deliver results and

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enable more intelligent, individualized customer interactions. I’m happy to introduce

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you all to Alan Webber, the program vice president of

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customer experience at IDC. We have Sridhar Jayakumar, the principal

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product manager at Adobe. And finally, we have Dan Arra,

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the vice president of sales for Genesys AI. So with

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that being said, I’m going to hand things off to

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our first presenter today. Alan, the floor is yours. Thank

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you so much, and thank you everybody for joining us. Man, 2020, what a

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year. Wasn’t quite what we were expecting going into it,

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but now that we’re through it and we’re on to

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the next one, 2021, let’s talk a little bit about what it

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means to be a post- COVID customer and how companies

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are driving empathy at scale. But to start with, I’d

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like to start with a little survey, if you don’t

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mind. And if you can go ahead and answer these

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question, the question we have up in the window. What

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are the biggest changes you’ve seen over the past year

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that are driving digital transformation? And please select all that

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apply. One, need to reassure customers they can reach you

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whenever needed. Two, increase interaction volume, more calls, chats, SMS,

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messages. Three, higher customer expectations to show me that me.

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Four, demographic change mix over different channels. And five, all

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of the above. Go ahead and give you just a

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second to go ahead and answer that question for us,

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if you would please. Okay, let’s see what the results

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are showing. So, the number one, as we probably would

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have expected, at 43 1/2% was all of the above.

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The second largest was increased interaction volume. More calls, more

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chats, SMS, and messages. Third was demographic change mix over

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channels. And fourth was need to reassure customers they can

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reach you whenever needed. And lastly was the higher customer

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expectation. And that actually leads real well into what my

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presentation is about and where things are going. with the

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pandemic there were a number of effects that changed, a

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number of things that affected the consumer and the customer.

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It created unique situations for some industries, such as retail,

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healthcare, and look at hospitality. And before we got on

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the call we were talking a little bit about travel.

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The airlines have really had to change what’s going on. It has

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forced them to rethink the way they use technology to

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engage with their customers. And what I want to highlight

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is some of the effects that we’re seeing from COVID

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that are happening already. There are three cracks in what

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we call customer experience, specifically COVID cracks. And what we

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mean by that is that, with COVID we saw an acceleration

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in the digital transformation forced upon companies. They had to

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digitally transform because they weren’t ready for all of the

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impacts that the pandemic would have on them. And so,

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the three cracks, what we call, essentially exposing the digital

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underbelly are contactless experiences, customer automation and self- service, and

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apps become the interface. And we’re going to start off

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with the contactless experience. Basically what happened is, because of

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COVID we went from an in- person, person- to- person

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type of engagement and interaction to these contactless experiences. And

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it’s everything from contactless product delivery, to payment, to product

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pickup. All kinds of different things, and the whole reason

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behind that was because it made consumers and customers feel

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safer. Now, in my experience, one of the more interesting

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ones to watch was Best Buy. Before, you used to

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go into the showroom, you’d look around, there’d be stuff

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in there, lots of people in there looking at everything

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from refrigerators, to new iPhones, to new computers. In the

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pandemic, actually Best Buy was well- positioned to deal with

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this transformation, and just a few months in they’d already

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switched over to the ability to do curbside pickup, order

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online, look into their supply chain. What we found is

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that, with customers, 38% of them said during our surveys

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that contactless experiences make them feel a lot safer, or

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much safer. What kind of contactless experiences? Specifically, contactless product

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delivery. That was the most preferred contactless approach. So everything

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from DoorDash to something coming from Amazon. But right up

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there was contactless payment, product pick up, check- in, customer

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service, and no- signature checkout. But that was just the

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start. That was just the first crack that we saw.

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But how does this actually manifest in the world it

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is that we’re dealing with today? And probably the best

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example we have out there is actually grocery pick up.

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30. 5, almost 31% of consumers said they would like

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curbside pick up to continue. Not really unexpected because by

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2023, IDC expects 75% of grocery e- commerce orders will be

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picked up curbside or in- store at a special type

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of kiosk, driving a 35% increase in investment in on-

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site or nearby micro- fulfillment centers to meet that need

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of the customer. We talked a little bit about contactless

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experience, what’s the next one? Well, that’s customer automation and

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self- service. That’s one of the things that grew fairly

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quickly during the whole pandemic, is customer automation and self-

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service. How do customers take care of things themselves? How

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do they actually, instead of going into a store or

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going into a location somewhere, how are they able to

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do it digitally, online, and move that forward? And so,

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what we found is that, during COVID- 19 and the

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pandemic, there were a lot of increases in adoption around

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the usage and adoption of automated self- service technologies. So

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for example, the use of telemedicine, first- time use of

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telemedicine, was up 28. 2%. Video calls, Zoom calls, Teams

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calls, WebEx calls went up 26.2%. Mobile ordering went up

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20. 5%, people actually ordering things on their phone. Everything

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from Amazon to, say Chipotle or Chic- Fil-A. But that

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wasn’t just it. What’s also increased is that push towards

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mobile apps and how we do automated and self- service

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through mobile apps. In fact, mobile applications, their usage of

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by consumers and customers went up almost 36%. Mobile ordering

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was up 27. 5% previously. Mobile payments, and this is

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one of the really interesting ones, between that and alternative payments, mobile

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payments was up 18%. Alternative payments, using things like PayPal,

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Venmo, things like that, was up almost 23%. So first

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of all, we saw the digital transformation around contactless experiences.

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And then we see this move into customer support and

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self- service, all being automated and again, in a lot

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of ways, contactless. So how has this manifested out into

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the world? Well specifically when you talk about telemedicine, this

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is probably one of the biggest changes because with healthcare,

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a lot of that is that personal interaction back and

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forth with the doctor or other healthcare provider, and IDC

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believes that telemedicine is here to stay just because it’s

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a simpler and easier way to interact and engage with

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your healthcare providers. For example, one of my children goes

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and sees a speech therapist and it used to be

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this was all done in person. Now what happens is

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that he has a weekly appointment with his speech therapist

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through Zoom and they’re able to interact and engage, and the

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audio and the video is good enough that they can

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actually make those necessary changes and teach him what it

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is he needs to adapt to his speech therapy. So,

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telemedicine is one of those things that are here to

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stay, and IDC believes by 2023, providers and life science companies

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will increase spending by 70% on health technologies, specifically those

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health telemedicine type technologies. So we continue to move that

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forward. Now we’ve talked about contactless experiences, we’ve talked about

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the consumer automation. Lastly, it’s really the applications become the

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primary interface for how we interact and engage with the

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world. With COVID- 19, it introduced a whole lot of new

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task apps that we’ve never had on our phones before.

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We’re used to having things like email, we’re used to

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having maybe FaceTime. Maybe you’re checking Facebook or doing doomscrolling

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on TikTok or maybe Instagram. But the fact of the

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matter is that we have a lot of new apps

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that were task- oriented that were introduced during COVID- 19.

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Probably the biggest one introduced, obviously, was the COVID incident

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management applications and symptom tracking. But the fascinating thing is,

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as we get through the pandemic, those are going to

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drop away but what’s going to stay in place? The

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workplace safety applications, the curbside pickup applications, and the office

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space utilization applications. You’re going to see a whole lot

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more new applications that lay out how it is we

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interact and engage, both with other employees and with customers

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and consumers as they’re out there. So this is the

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new world that we’re dealing with. A lot of this

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is going to be happening through those mobile devices. In

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fact, probably one of the best examples that I can give

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during this whole process was the whole food delivery. One

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of the fabulous things about being home now, when I

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used to travel so much, is that I had a

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number of dollars stashed up in Uber Cash. And so,

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it was fun when my kids were home from school

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that they would come in and they would say, ” Okay,

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he Dad, can we order Uber Eats using the money

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you have on your Uber app?” And that was kind

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of cool for a couple of times. After the third

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time, it’s like, ” No, you can go downstairs and make

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yourself a PB& J. But the fact of the matter

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is that food delivery is not going away. It’s something

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to stay. And it’s going to increase for restaurants and

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eats into their profits just as a way to be

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able to survive. 31% of consumers would like home delivery

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of goods to continue. And honestly, I think this is

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going to grow. I don’t know how many of you

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remember the old Peapod and places like that where they

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would actually deliver groceries. This is going to grow even

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more. In fact, IDC believes by 2023, 30% of restaurants

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using third party delivery platforms will deploy native delivery options

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to eliminate that third party feed because they want to

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retain that money. What that means is that they’re using

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technology to continue to build up their baseline revenue in

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this new type of world it is that we’re dealing

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with. So, if you work in technology, if you’re CIO,

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a CTO, a CMO, what does this mean for you? Well, here’s

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what it means for you. CX is now becoming urgent

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for the C- Suite. IDC does a lot of research,

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we take a lot of surveys, and back in January

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or February we surveyed what we call our future enterprise

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survey. And we had CxO type officers outline where the

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primary priorities were for the C- Suite in their companies.

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And customer experience was down number eight because there’s so

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many other things that are going on out there. And

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most companies honestly believe that they can always find another

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customer. What we found is that with the pandemic, customer

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experience went from number eight to number two, and it

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remained in one of the top three slots every month

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for the rest of the year. This is where the

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priority was. Customer experience become a top priority because companies

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realize that without a customer, they didn’t have a company.

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So what are the challenges to driving a differentiated experience?

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Again, from our survey, the biggest challenge out there is

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that cross- channel experience. How do you move from a

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mobile device to a website to an in- person, and

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maybe back to a mobile device? 69% of our respondents

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said that was the number- one thing they were challenged

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with. The number two was how do you switch, in

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a pandemic environment, into a customer- centric organization? What does

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it take and how do you move that forward? Third

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was tying that improvement in customer experience to revenue, and

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that’s absolutely critical because if you can’t prove the revenue,

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then you can’t prove the value of the experience. And

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the last one, all the way down at 38% was

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integrated the customer data. Now I’ll just tell you from

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experience, you’re going to see this one rise because this

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is the lifeblood of customer experience. If you look at

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the work that Genesys and Adobe do, this is really

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the lifeblood. The customer data, moving that customer data around

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and being able to drive all of the intelligence and

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analytics, and therefore the better experience that they’re able to

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provide. So, when you think about where this goes in

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the future, think about starting with the customer at the

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center. This is where the future is going to be

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and this is how companies like Adobe and Genesys are

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pulling this together. Customers at the center, and they want

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four things. They want a conversation with the brand, they

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want a clear customer journey, they want a good experience,

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and they want satisfaction and being able to express that

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satisfaction. So really, what it comes down to is an

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empathetic relationship between customers and brands built upon what the

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customer wants and how they want to be treated. But

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that’s just the beginning because this all happens through this

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lens of technology. And it’s this technology around awareness, engaging,

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learning and measuring. Every customer engagement now happens through a

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lens of technology, and that’s the critical piece here. Lastly,

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you’ve got the customer, you’ve got the technology. It has

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to drive business outcomes that drives empathy at scale. That’s

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really where the future is now, is empathy at scale.

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And that’s what technology is driving, and that’s what that

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relationship with the customer is all about. So this is what

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I’d like to leave you with because this is what

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it means. The customer experience of today and tomorrow is

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all about awareness. It’s about engagement. It’s about learning. It’s

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about measurement. And it’s about data. And these, together, are

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what drive empathy at scale, and all this happens through

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the lens of technology. Now I’d like to turn it

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over to my colleagues and let them take this forward

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from here. Great. Thank you very much, Alan. A lot

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of good information. We’d like to learn a little bit

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more about your specific CX challenges before Sridhar and I

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start talking about the Adobe Genesys partnership and integrations. So

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we have a question here and a few options. What

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are your CX challenges in the new digital world? One,

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improve customer retention/ loyalty, reduce churn. That’s probably pretty important.

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Personalize the experiences to impact very specific KPIs. CSAT, increase

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revenue, increase lead velocity, and for those contact center people

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out there, first call resolution, average handle time. Are those

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important KPIs? Are you focused on reducing the call interaction

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volume through automation, using bots or chats rather than calls?

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And do you have commitments to your customers, whether they’re

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internal within your organization or to external customers, to meet

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service level agreements? So, let’s take a look at the

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results. So, reducing call interaction volume. That’s pretty interesting, that’s

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very good. So, as Alan was describing, the increase of

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volume of interactions going up, being able to handle those

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using AI. And second, these KPI metrics or specific revenue

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and lead velocity metrics, they’re pretty important as well. And

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reducing churn, increasing loyalty. And then finally, meeting your service

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level agreements. Great. All right, so as I mentioned, we’re

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here to talk about a new strategic partnership between Genesys

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and Adobe, a very exciting partnership. We’ll describe what that

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partnership is, talk about the integrations between our two platforms,

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and then we’re going to give you a demonstration that actually shows you

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how we break down these silos between marketing, commerce, sales

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and service across the entire customer life cycle, customer journey,

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to utilize the data that exists, the behaviors that occur,

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so we can link the marketing profiles with contact history

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for real- time engagement. And the goal is to allow

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you to see and know more about your customers, personalize

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better, and engage smarter and faster to achieve some of

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those KPIs that we were just talking about. And another

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way to look at this is we’re leveraging each other’s

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strengths, Adobe and Genesys. Genesys is a contact center platform,

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customer experience platform, focused on real- time engagement. Most of

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the engagement is one- to- one between an agent or

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an employee and a customer, and a marketing or digital

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experience platform from Adobe, they’re focused on communicating one- to-

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many, as marketeers do. And we want to leverage the

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data that exists in the contact center and all of

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the other connection points that we have in the contact

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center, with the data and marketing technology stack that exists

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in the sales and marketing part of the organization to

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develop this single unified view so we have better conversations,

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as Alan was describing, clearer journeys, and good experiences for

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the customer in context. So we’re engaging at the right

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moments with the right information to make the employees smarter,

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the customers happier, and the outcomes better so that those

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customers are satisfied. So Alan was describing the trends that

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he’s seen in the COVID world. Let’s hear a little

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bit from Sridhar about the trends that Adobe is seeing.

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Sridhar? Thank you, Dan. And like Alan was pointing out,

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in this COVID and post- COVID era, with cashless payments

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or contactless experiences, one of the key motivators for customers and businesses,

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we find ourselves to be in a very fortunate position

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to be the nexus of many of these micro trends.

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I’m not going to go through all of them but I’ll

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just pick a few of them which actually might resonate

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with you. The first one is, this is actually the

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golden age of design and creativity. So design is actually

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competitive advantage for businesses, large and small. How they present

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themselves, how they make things easier for customers to interact

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with, as well as how they use technology as a

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canvas for exploring new areas, whether it is voice, or

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VR, AR, or artificial intelligence, things like that. The second

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one is, technology is actually reshaping productivity. So documents continue

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to still be the core of how people work, so if we look

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at realtors, if we look at any major business, they are moving

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non- paper artifact whether it is using Adobe Sign or

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any other mechanism of creating via the web or email

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without having to actually having to pick up a paper and

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sign the paper. The third one is around how experiences

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matter more now than ever. The first one is customers will

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need to know and anticipate what their customers will want

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from them, and why they are contacting them in the first place.

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And second one is the data itself. The more data

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which they collect today becomes a mandate of how they’re

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going to protect the data, as well as being transparent

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about how they are going to use this data. And

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all these trends are all by artificial intelligence and machine

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learning. They accelerate these trends. Speaking about Adobe, what Adobe

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does, Adobe was founded in 1982 and since then, from

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the beginning, setting the standard for digital experiences has been

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one of the prime goals or motivation factors for us. At Adobe,

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our mission is to change the world through digital experiences. So,

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from the moment you wake up in the morning until

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you go to bed at night are the media you

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consume, the content that you see on your mobile phone,

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or the websites you browse through, or the apps that you use, at some

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point they all actually have some sort of connectivity or

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touched by Adobe technology, whether it is the creations that you

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see done via Photoshop or Illustrator, or whether it is

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the pages that are delivered via the Experience Manager or

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the personal Adobe Target, there is always some sort of

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Adobe’s presence in the gadgets that you use, as well

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as the content that we consume today. At its essence, Adobe

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00:24:40,870 –> 00:24:45,050
gives everyone, from emerging artists to global grants, everything they

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need to design and deliver exceptional digital experiences. I would

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00:24:50,080 –> 00:24:57,310
like to walk through how we see current technology landscape

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00:24:57,430 –> 00:25:04,130
in this specific era. So, coming to strategy, right? So,

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originally Adobe was known as a company that deals with

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creating creative tools that will help creators, designers, whether it

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is creating a nice image in Photoshop or to create

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00:25:16,250 –> 00:25:21,540
banners or content within Acrobat, or InDesign, or Dreamweaver. Over the

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last decade or so, we have been slowly merging both the creative

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business as well as the marketing business, so all the

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creative artifacts that are developed by these creator people will

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need to get into the consumer’s hands. Whether it is

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via web or whether it is via print media. So

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we do all that, so within the marketing business, we

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have our Experience Manager for edge delivery, we have analytics

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to get insights of what is currently being viewed, what

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00:25:49,460 –> 00:25:52,370
customers like, what they don’t like, where they currently need.

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And then we also provide targeting campaigns and other via

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marketing cloud products. And then, like I said earlier, the

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00:26:00,210 –> 00:26:03,660
move from paper to print, or paper to digital, that

403
00:26:03,660 –> 00:26:06,110
transformation has also been happening for quite some time now.

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00:26:06,850 –> 00:26:09,880
We have Adobe Sign as well as we also have Acrobat,

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which in that aspect. Great, so we’ve heard a bit

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00:26:16,520 –> 00:26:20,690
about Adobe strategy. Oops, let me back up here. Adobe

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00:26:20,690 –> 00:26:25,550
strategy, and I’ll highlight how Adobe strategy and the Genesys

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00:26:25,550 –> 00:26:29,430
strategy are very complementary. We’ve been around a long time

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00:26:29,430 –> 00:26:32,750
as well, 30 years, and we have many of the

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00:26:32,750 –> 00:26:36,990
same customers. We are listing some very recognizable brands here

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and large companies, but I want to emphasize that we

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00:26:39,980 –> 00:26:46,310
also have customers in the small, medium, large business category,

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not just enterprise customers. And many of these customers are

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very large, and they have many, many agents interacting with,

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00:26:55,340 –> 00:26:59,850
and customers, which results in a great deal of data

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00:27:00,010 –> 00:27:04,570
that is derived from the massive amount of interactions that

417
00:27:04,570 –> 00:27:09,020
are occurring on a daily basis. So, we are utilizing

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00:27:09,080 –> 00:27:16,890
that data to become smarter and to optimize the customer

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00:27:16,890 –> 00:27:25,610
experience. And leveraging the customer service tech stack, the agents,

420
00:27:25,830 –> 00:27:28,620
and users of the platform on the left side of

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00:27:28,620 –> 00:27:33,360
the wall. And we’re also leveraging now with the partnership

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00:27:33,360 –> 00:27:39,270
by breaking down that wall to leverage the data and

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00:27:39,270 –> 00:27:43,730
the insights that exist from the Adobe platform. So we’ll

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00:27:43,730 –> 00:27:48,320
know things like which campaigns appealed to a customer who

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00:27:48,320 –> 00:27:52,150
might be calling in for support. And we can leverage

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00:27:52,150 –> 00:27:57,560
that to eliminate a disjointed customer experience. Many times we

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00:27:57,560 –> 00:28:05,510
think of receiving emails and information from businesses via one

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00:28:06,270 –> 00:28:12,700
channel and one experience, but as a consumer, we shop,

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00:28:12,840 –> 00:28:16,580
we buy, and we get customer care and we do

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00:28:16,580 –> 00:28:20,190
that with the business. And we want to make sure

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00:28:20,190 –> 00:28:27,560
that, as consumers, we have this very tightly integrated experience.

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And, as you highlighted in the survey question, one of

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your challenges to eliminate these disjointed experiences. And you also

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00:28:36,530 –> 00:28:40,040
want to deliver what the customer needs. Products that they

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00:28:40,040 –> 00:28:44,710
need, services that they need, to increase the value that

436
00:28:44,710 –> 00:28:49,660
you’re receiving and delivering to those customers. And again, leveraging

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00:28:49,660 –> 00:28:53,140
the data and the technology on both sides of that

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00:28:53,140 –> 00:28:57,910
wall, we can fulfill the brand promise that’s often made

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by the marketing side of the house but the customer

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00:29:00,690 –> 00:29:03,770
care and the people that are engaging in real- time

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00:29:04,280 –> 00:29:06,790
are often the people who have to deliver on that

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brand promise, to show the customer that we know them,

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to demonstrate that we value the information that they’ve shared.

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And we’ve been paying attention to these interactions so that

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we can know the best ways, the best moments, to

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engage. And this is really the essence of what we

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00:29:25,920 –> 00:29:30,720
call experience as a service. There are many companies that

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00:29:30,720 –> 00:29:34,560
we interact with, we categorize them here as the companies

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00:29:34,560 –> 00:29:38,660
you swear at, right? Where you’ve been invited to their

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00:29:38,660 –> 00:29:41,940
business and you call them and the first thing they

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00:29:41,940 –> 00:29:44,240
do is ask you questions as if they don’t know

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who you are. Who are you, and why are you calling? It’s like inviting

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00:29:48,910 –> 00:29:51,820
somebody over to your house for dinner, and you open

454
00:29:51,820 –> 00:29:54,760
the door and you say, ” Who are you and why

455
00:29:54,760 –> 00:29:59,740
are you here?” So, being able to understand the invitations,

456
00:29:59,740 –> 00:30:02,870
the marketing campaigns, that are driving people to your business,

457
00:30:03,130 –> 00:30:06,110
either to your website or to your call center over

458
00:30:06,110 –> 00:30:10,020
the phone, we want to make sure that we are

459
00:30:10,320 –> 00:30:13,620
aware of the entire customer life cycle so that we

460
00:30:13,620 –> 00:30:18,810
can deliver these empathetic experiences, what we call experiences as

461
00:30:18,810 –> 00:30:25,610
a service. The essence of empathy is paying attention. Observing,

462
00:30:25,710 –> 00:30:31,730
listening and understanding, and then remembering in context. When somebody

463
00:30:31,840 –> 00:30:36,330
is engaging with your business, they don’t necessarily follow a

464
00:30:36,380 –> 00:30:42,860
linear path from shopping, buying, et cetera. There is a

465
00:30:42,860 –> 00:30:46,770
lot of contextual changes. You may make a purchase and

466
00:30:46,770 –> 00:30:50,830
then you may have a support question. Or you may

467
00:30:50,830 –> 00:30:54,830
want to return a product. So being aware of what

468
00:30:54,830 –> 00:30:58,580
has happened across the entire customer life cycle is going

469
00:30:58,580 –> 00:31:02,780
to help you respond efficiently and in a way that

470
00:31:02,780 –> 00:31:06,500
shows the customer that you really value the information that

471
00:31:06,550 –> 00:31:10,160
they have shared with you. So Sridhar, maybe you could

472
00:31:10,160 –> 00:31:12,990
talk a little bit about the underlying technology and how

473
00:31:12,990 –> 00:31:18,260
we deliver this together. Sure, thank you. So, for the

474
00:31:18,260 –> 00:31:21,850
experience as a business, what we currently want to push here

475
00:31:22,110 –> 00:31:25,250
with this particular Genesys partnership is our Adobe Experience Cloud

476
00:31:25,250 –> 00:31:28,760
Solution that we are working to pair with Genesys. Adobe Experience

477
00:31:28,760 –> 00:31:33,570
Cloud is the industry’s most comprehensive solution for marketing, advertising,

478
00:31:33,630 –> 00:31:38,290
analytics and commerce, serving both B to C or B

479
00:31:38,290 –> 00:31:43,070
to B customers. Experience Cloud allows you to deliver exceptional experiences

480
00:31:43,070 –> 00:31:47,660
from creation all the way through to monetization, acquisition, retention,

481
00:31:47,660 –> 00:31:52,330
and even renewal of existing services that the brands might

482
00:31:52,330 –> 00:31:55,130
want to give to their customers. And at the core

483
00:31:55,340 –> 00:31:58,430
of the Experience Cloud, we have the Experience platform, which

484
00:31:58,830 –> 00:32:03,530
is our new strategic initiative for integrating with the different

485
00:32:03,580 –> 00:32:08,270
clouds versus different services, wherein we are able to pull in

486
00:32:08,270 –> 00:32:11,690
data from different parties, whether it is a customer’s online

487
00:32:11,690 –> 00:32:16,710
behavior via Adobe Analytics, or information that we collect via

488
00:32:16,710 –> 00:32:21,650
different DSPs or data providers and as well as campaigns

489
00:32:21,650 –> 00:32:25,160
that sends out emails to customers, and how a customer

490
00:32:25,170 –> 00:32:29,480
reacts to a particular targeted or personalized messages. All this

491
00:32:29,480 –> 00:32:32,380
data is now in the platform, and we use machine

492
00:32:32,380 –> 00:32:37,370
learning and artificial intelligence to uncover new insights and information

493
00:32:37,370 –> 00:32:43,590
that was not previously available. At the center of this

494
00:32:43,590 –> 00:32:48,210
platform, we have something called the real-time customer profile. So,

495
00:32:48,230 –> 00:32:51,330
when I go to a particular retail store, the store

496
00:32:51,330 –> 00:32:54,820
would know me as in and buying a particular device,

497
00:32:55,100 –> 00:32:59,950
or buying a particular product. As well as when I

498
00:32:59,950 –> 00:33:04,410
go online and search for similar for what I just

499
00:33:04,410 –> 00:33:07,670
bought, or even look up support information or any sort

500
00:33:07,670 –> 00:33:10,060
of issues that I want to get resolved, I’ll be

501
00:33:10,060 –> 00:33:11,950
using my email address to sign in, or I may

502
00:33:11,950 –> 00:33:14,710
be using my Facebook or my social media handles to

503
00:33:14,710 –> 00:33:18,270
connect. And this profile information is very disparate, right? So

504
00:33:18,270 –> 00:33:21,210
the way I interact with the particular brand could happen

505
00:33:21,210 –> 00:33:24,130
differently. And again, this could be happening from a mobile

506
00:33:24,130 –> 00:33:27,120
device, or from a laptop, or even in- person or

507
00:33:27,120 –> 00:33:31,730
offline, which is happening. And when we put all these

508
00:33:31,730 –> 00:33:36,010
data into one particular environment, it becomes very hard for

509
00:33:36,560 –> 00:33:38,900
analysts as well as marketers to figure out what is

510
00:33:38,900 –> 00:33:42,260
exactly happening. Why is a particular sale happening in the

511
00:33:42,260 –> 00:33:45,420
way it is happening? Or why exactly a consumer is

512
00:33:45,420 –> 00:33:47,980
not too happy with the brand? So what we are

513
00:33:47,980 –> 00:33:49,760
doing here is we are bringing in all the data

514
00:33:49,760 –> 00:33:54,330
into the platform, along with things like the customer’s interaction

515
00:33:54,350 –> 00:33:58,480
at the store level, the service that is complete online, as

516
00:33:58,480 –> 00:34:00,650
well as the point of sale, as well as a

517
00:34:00,650 –> 00:34:04,280
Genesys- provided customer experience data. That could be call center

518
00:34:04,280 –> 00:34:07,660
data, could be anything that Genesys gives to the platform. And

519
00:34:07,660 –> 00:34:10,880
all this data is stitched together, and it is available at

520
00:34:10,880 –> 00:34:14,730
the edge, meaning it is available for marketers, it’s available

521
00:34:14,730 –> 00:34:17,940
for call center agents, could be available for service people,

522
00:34:18,280 –> 00:34:21,130
at the edge instantaneously. Again, we are talking about less

523
00:34:21,130 –> 00:34:25,400
than a few milliseconds here. And this enables them to

524
00:34:25,400 –> 00:34:28,590
quickly interact with the person with the latest knowledge about

525
00:34:28,650 –> 00:34:31,180
what they have gone through, or what is the right

526
00:34:31,180 –> 00:34:35,560
level of interaction that they would want to come back. Empathy as

527
00:34:35,560 –> 00:34:44,830
a service. Let’s take a look at how this interaction

528
00:34:44,920 –> 00:34:50,630
would work. What would the experience feel like? So, we’re showing

529
00:34:50,630 –> 00:34:56,710
here a customer arriving on a website or calling into

530
00:34:56,710 –> 00:35:02,480
the contact center. And when they do that, the integrations

531
00:35:02,480 –> 00:35:06,650
we have between our platform, the Genesys platform, and the Adobe platform,

532
00:35:07,020 –> 00:35:11,140
we’re able to pull data out of the Experience Cloud

533
00:35:11,140 –> 00:35:17,650
platform, or the Adobe Experience platform, or Adobe Analytics, grab

534
00:35:17,650 –> 00:35:21,850
that information into the agent desktop in a consumable way

535
00:35:22,420 –> 00:35:26,460
to make the agent more knowledgeable about what brought the

536
00:35:26,460 –> 00:35:28,990
customer here, why are they calling us today? Why are

537
00:35:28,990 –> 00:35:33,270
they engaging? What were they doing before, during and after

538
00:35:33,270 –> 00:35:37,330
the interaction? And we can even pull product recommendations out

539
00:35:37,330 –> 00:35:41,740
so we have more relevant, contextual suggestions for the customer

540
00:35:42,000 –> 00:35:45,660
about products or services they may want to buy. And

541
00:35:45,660 –> 00:35:51,040
we can push and pull data during that interaction, and

542
00:35:51,040 –> 00:35:54,130
at the end of that interaction, to enrich the customer

543
00:35:54,130 –> 00:35:59,810
profile, to inform subsequent marketing campaigns about what the customer

544
00:35:59,810 –> 00:36:03,580
shared in that live conversation with an agent or support

545
00:36:03,580 –> 00:36:08,540
person so that at what stage that customer is in

546
00:36:08,540 –> 00:36:12,260
their buying decision. And that will inform what information to

547
00:36:12,260 –> 00:36:15,910
deliver to them to move them more efficiently, and in

548
00:36:16,040 –> 00:36:19,550
a better way, through the sales cycle, through the funnel.

549
00:36:20,240 –> 00:36:23,800
And we can also use these insights to trigger engagement

550
00:36:24,040 –> 00:36:28,040
more intelligently so you’re engaging at the right moments with

551
00:36:28,040 –> 00:36:32,420
the right information to drive the best outcome. So this

552
00:36:32,940 –> 00:36:37,590
real- time exchange of data really helps to optimize the

553
00:36:37,590 –> 00:36:45,310
experience and give you this integrated picture of who that

554
00:36:45,310 –> 00:36:50,210
customer is. So, we’re showing examples of customer experience data

555
00:36:50,210 –> 00:36:54,380
from the contact center, customer experience data from the various

556
00:36:55,140 –> 00:36:58,880
Adobe products that have been consolidated into the Adobe Experience

557
00:36:58,880 –> 00:37:04,560
platform to simplify and streamline the access to that data

558
00:37:04,830 –> 00:37:09,820
so that we can update the information in the moment

559
00:37:10,040 –> 00:37:15,980
and then use that during the conversation to impressionalize and

560
00:37:15,980 –> 00:37:20,120
optimize the experience for the customer. So, let me show

561
00:37:20,120 –> 00:37:27,020
you how this works by sharing my desktop. And I’m

562
00:37:27,170 –> 00:37:32,680
going to show you the solution, and I’m going to

563
00:37:32,800 –> 00:37:38,860
describe what the experience would look like with a customer

564
00:37:38,860 –> 00:37:43,980
either arriving on a website or dialing into the contact

565
00:37:43,980 –> 00:37:47,670
center. So, on the left we have a website, and

566
00:37:47,670 –> 00:37:50,530
the customer may have arrived on this website from a

567
00:37:50,530 –> 00:37:53,880
marketing campaign, or they may have just picked up the

568
00:37:53,880 –> 00:37:57,050
phone and called into the contact center. So on the

569
00:37:57,050 –> 00:38:00,150
right here, we’re taking a peek under the hood of

570
00:38:00,150 –> 00:38:06,300
the Genesys platform and we’re observing unknown visitors or callers,

571
00:38:06,520 –> 00:38:12,140
known visitors based upon their cookie, email, login, or recognized

572
00:38:12,140 –> 00:38:15,920
phone number. And we’re connecting in the back end with

573
00:38:15,920 –> 00:38:19,300
the Adobe Experience platform and other Adobe products. So we’ve

574
00:38:19,300 –> 00:38:23,450
got the Genesys platform, the Adobe platform, and we’re connecting

575
00:38:23,450 –> 00:38:27,160
these dots. And here we’re going to view what the

576
00:38:27,160 –> 00:38:31,910
platform can see about this one unknown visitor. So, first

577
00:38:31,910 –> 00:38:34,820
I’m showing you the platform view. This is not the

578
00:38:34,820 –> 00:38:39,270
agent view. This is what the platform can see. And as this

579
00:38:39,270 –> 00:38:41,970
visitor on the left, who may have arrived from a

580
00:38:41,970 –> 00:38:46,120
marketing campaign, begins to navigate around on the site, you’ll

581
00:38:46,120 –> 00:38:50,850
see that we’re observing, analyzing their behavior in real- time.

582
00:38:51,140 –> 00:38:53,960
We don’t know who this visitor is, they’re unknown, but we

583
00:38:53,960 –> 00:38:58,420
have some demographic and technographic information that may inform us

584
00:38:58,630 –> 00:39:01,520
a bit more about how, when, and with whom to

585
00:39:01,520 –> 00:39:05,240
engage. And then we can see here, previous visits that

586
00:39:05,240 –> 00:39:07,500
may have occurred, even though this person is unknown. They

587
00:39:07,500 –> 00:39:12,230
may have, without sharing their identity, visited our site over

588
00:39:12,380 –> 00:39:16,940
the past several months. And if the customer, visitor, starts

589
00:39:16,940 –> 00:39:20,240
to do things like add items to their shopping cart,

590
00:39:21,460 –> 00:39:24,780
or they could be filling out an application, we’re able

591
00:39:24,780 –> 00:39:28,620
to see that they’ve added items to their cart, what

592
00:39:28,620 –> 00:39:32,620
item, the dollar amount, the product category. We can track

593
00:39:32,750 –> 00:39:36,420
whether the cart value is increasing or decreasing, and we’re

594
00:39:36,420 –> 00:39:41,570
making predictions about whether they’re likely to make a purchase

595
00:39:41,820 –> 00:39:45,340
or fill out a lead form. These outcomes make sense

596
00:39:45,340 –> 00:39:49,560
for the G- Sol business. You would configure different outcomes.

597
00:39:49,740 –> 00:39:52,150
And once those outcomes are configured, we start to look

598
00:39:52,150 –> 00:39:55,540
at the attributes and behaviors of all those who achieve

599
00:39:55,920 –> 00:39:59,240
that outcome, and that’s how we’re making these predictions. So

600
00:39:59,240 –> 00:40:01,520
if this person decides they would like to log in

601
00:40:02,080 –> 00:40:05,980
and share a bit more information about themselves, maybe they’ve

602
00:40:05,980 –> 00:40:09,340
got stored payment information here and they’d like to log

603
00:40:09,340 –> 00:40:11,990
in so they can use that before proceeding to check

604
00:40:11,990 –> 00:40:15,900
out. Now you’ll see a few things changing here. Now

605
00:40:15,900 –> 00:40:19,330
that we know who this person is, we can start

606
00:40:19,330 –> 00:40:24,330
to decide which communication options to present to the customer.

607
00:40:25,240 –> 00:40:29,950
So we’ve presented here a live assistance widget that shows

608
00:40:29,950 –> 00:40:33,240
me, on the left, that I can call or initiate

609
00:40:33,240 –> 00:40:38,240
a web chat. We’ve got some customer- initiated options. But

610
00:40:38,240 –> 00:40:41,870
if the customer proceeds here in their journey to begin

611
00:40:41,870 –> 00:40:46,310
to check out, they are wondering about this discount code.

612
00:40:46,750 –> 00:40:50,250
Maybe they search for the discount code, and they don’t

613
00:40:50,250 –> 00:40:54,220
find what they’re searching for. What is going to happen

614
00:40:54,220 –> 00:40:59,280
is we will proactively offer that visitor a very personalized

615
00:40:59,280 –> 00:41:04,510
tailored interaction. A chat interaction will appear that asks them, ”

616
00:41:04,570 –> 00:41:08,370
Would you like a chat? You have a discount that

617
00:41:08,370 –> 00:41:11,730
applies.” So again, we’re leveraging the insights here, and we’re

618
00:41:11,730 –> 00:41:14,120
not asking, ” Hey, who are you? How can we help

619
00:41:14,120 –> 00:41:17,130
you?” We’re suggesting that you may need help with your

620
00:41:17,130 –> 00:41:21,320
purchase. And when the customer accepts that interaction and starts

621
00:41:21,320 –> 00:41:25,820
to chat, we’re first engaging with a chat bot. So

622
00:41:25,820 –> 00:41:29,900
the customer may ask questions about, ” When can you ship?”

623
00:41:31,910 –> 00:41:35,630
And the chat bot may be able to answer this question. And

624
00:41:35,630 –> 00:41:38,370
he does. And then now, ” Great, now I would like

625
00:41:38,370 –> 00:41:40,410
to talk to an agent, please, because I’d like to

626
00:41:40,410 –> 00:41:47,250
make a purchase.” So when the visitor requests and agent,

627
00:41:47,490 –> 00:41:51,610
you’ll see here an inbound alert notifying me that I

628
00:41:51,610 –> 00:41:56,220
have a incoming chat. So the first thing we’ll see

629
00:41:56,220 –> 00:41:59,700
is, as an agent… Now before, I want to remind

630
00:41:59,700 –> 00:42:03,290
you we were just observing, analyzing, predicting all of the

631
00:42:03,290 –> 00:42:07,310
visitors and callers, whether they needed help based upon their

632
00:42:07,310 –> 00:42:10,090
behaviors and the data that we have from the Adobe

633
00:42:10,090 –> 00:42:13,750
platform combined with what we were seeing in the Genesys

634
00:42:13,750 –> 00:42:17,670
platform and what was happening on the website. But now,

635
00:42:17,670 –> 00:42:20,190
we’re in an interaction and the agent, at a glance,

636
00:42:20,190 –> 00:42:24,520
can see what the bot conversation was before they were

637
00:42:24,520 –> 00:42:28,010
connected to the agent. We can see what outcomes the

638
00:42:28,010 –> 00:42:31,550
customer was likely to achieve or unlikely to achieve, what

639
00:42:31,550 –> 00:42:36,270
segments they may have matched. And we can even see

640
00:42:36,290 –> 00:42:39,670
this live behavior on the website. We can scroll down

641
00:42:39,670 –> 00:42:45,180
here and see what happened, say, six days ago. They

642
00:42:45,180 –> 00:42:47,730
placed a call. Who did they talk to? What was

643
00:42:47,730 –> 00:42:51,560
their outcome disposition? Maybe they placed an order and a

644
00:42:51,560 –> 00:42:54,950
product was shipped. So we have the ability to look

645
00:42:54,950 –> 00:42:59,970
more deeply into the customer experience across days, weeks, different

646
00:42:59,970 –> 00:43:04,550
channels, and we can observe here, in the moment, what

647
00:43:04,550 –> 00:43:08,050
the customer was searching for, what they may have in

648
00:43:08,050 –> 00:43:12,030
their shopping cart, what segments they may have matched. So

649
00:43:12,030 –> 00:43:15,890
now, I’m much more effective as an agent, much smarter,

650
00:43:15,890 –> 00:43:20,050
I can ideally be on the phone or in the

651
00:43:20,050 –> 00:43:22,120
chat in a shorter period of time. I can jump

652
00:43:22,120 –> 00:43:28,190
right to the canned responses that may be relevant to

653
00:43:28,190 –> 00:43:31,180
the customer. Probably searching for a discount code. I’ve got

654
00:43:31,180 –> 00:43:34,360
a discount code right here. I can pop that into

655
00:43:34,360 –> 00:43:37,410
the chat and share that with the customer. Guiding the

656
00:43:37,410 –> 00:43:40,750
customer back to their shopping cart so they can proceed

657
00:43:40,750 –> 00:43:45,580
to check out, enter that information, and achieve this outcome.

658
00:43:47,030 –> 00:43:50,140
And all this information is being updated as you see

659
00:43:50,140 –> 00:43:54,270
it in real- time and can be propagated into the

660
00:43:54,430 –> 00:43:59,700
various Adobe products, including the Adobe Experience platform so that

661
00:43:59,700 –> 00:44:04,450
we now know if a customer purchased during a chat,

662
00:44:04,640 –> 00:44:08,680
purchased over the phone, we can share that information and

663
00:44:08,680 –> 00:44:12,080
connect those dots so we have the real- time engagement

664
00:44:12,670 –> 00:44:17,000
interactions and we know how many chats or calls it

665
00:44:17,000 –> 00:44:20,870
took to help a customer achieve an outcome. That may

666
00:44:20,870 –> 00:44:26,100
inform future marketing campaigns so that we can tailor them,

667
00:44:26,320 –> 00:44:31,100
tailor the content that is guiding the customer to the

668
00:44:31,100 –> 00:44:34,620
right place so they are not calling when they can

669
00:44:34,620 –> 00:44:38,100
self- serve. They’re not asking questions about information that may

670
00:44:38,100 –> 00:44:42,150
be available at their fingertips on the website. So it’s

671
00:44:42,150 –> 00:44:46,850
a much more efficient way to engage, and it shows

672
00:44:46,850 –> 00:44:50,870
the customer that them, right? So we’re able to take

673
00:44:50,870 –> 00:44:54,970
advantage of the data and information that’s here, pulling together

674
00:44:55,210 –> 00:45:00,470
information from the Adobe platform in real- time to make

675
00:45:00,480 –> 00:45:04,990
the agents or reps smarter, the customers happier, and guide

676
00:45:04,990 –> 00:45:10,990
the customer to a better outcome. So, let’s go back

677
00:45:11,070 –> 00:45:21,910
to our slides and let’s review here what we’ve shown

678
00:45:21,910 –> 00:45:28,540
you. So, we’ve tied together the customer experience from marketing,

679
00:45:28,650 –> 00:45:31,960
sales and care. We leveraged all of the information that

680
00:45:31,960 –> 00:45:35,800
was there, we’re much more knowledgeable about what campaigns may

681
00:45:35,800 –> 00:45:38,880
have brought the customer to the site, what things they

682
00:45:38,880 –> 00:45:42,180
were interested in, not interested in. So we could be

683
00:45:42,180 –> 00:45:45,020
much more effective at upselling and cross- selling at the

684
00:45:45,020 –> 00:45:49,240
end of that support call. We’ve all had these experiences

685
00:45:49,240 –> 00:45:52,740
where we call our cable or internet provider about a

686
00:45:52,740 –> 00:45:56,020
support issue, and at the end of the call they try

687
00:45:56,020 –> 00:45:59,100
to sell you something. The try to sell you the sports

688
00:45:59,100 –> 00:46:03,300
channel, or the movie channel, and you say to yourself, ”

689
00:46:03,910 –> 00:46:06,930
No, I’m not interested in that. Why are you asking

690
00:46:06,930 –> 00:46:10,430
me this? You should know. Aren’t you listening to me?”

691
00:46:10,830 –> 00:46:15,650
So, in this use of our integrated solutions, we’re able

692
00:46:15,650 –> 00:46:18,580
to show that we are listening to the customer and

693
00:46:18,580 –> 00:46:22,100
we know what are their interests, and we can suggest

694
00:46:22,270 –> 00:46:25,830
products that they’re more likely to buy, services that they’re

695
00:46:25,830 –> 00:46:29,110
more likely to buy. And we can again propagate that

696
00:46:29,110 –> 00:46:34,240
information into the marketing platform for subsequent campaigns that are

697
00:46:34,240 –> 00:46:37,840
much more tailored to the interests of the customer. And it

698
00:46:37,900 –> 00:46:42,070
helps us fulfill that brand promise in showing the customers

699
00:46:42,070 –> 00:46:47,320
we value them, and we’re delivering on our commitment to

700
00:46:48,410 –> 00:46:53,420
certain service levels. And we’re using this information to predict

701
00:46:53,570 –> 00:46:56,880
how, when, and with whom to engage in all of

702
00:46:56,880 –> 00:47:01,330
the various channels the customer would like to use. So,

703
00:47:01,330 –> 00:47:04,330
in the digital world, if everything is just digital, things

704
00:47:04,330 –> 00:47:06,620
are easy. In the voice world, if everything is just

705
00:47:06,620 –> 00:47:10,350
voice, things are easy. But when you start communicating across

706
00:47:10,430 –> 00:47:15,010
channels, you need to blend that information in intelligent ways,

707
00:47:15,380 –> 00:47:20,270
utilize AI, and partner with vendors such as Adobe in

708
00:47:20,270 –> 00:47:26,470
strategic ways to really deliver this empathy as a service

709
00:47:26,470 –> 00:47:34,440
experience as service. So Josh, we have some time for

710
00:47:34,670 –> 00:47:38,310
Q& A, and Josh is going to moderate that. But

711
00:47:38,320 –> 00:47:40,520
we have some information here that you can take a

712
00:47:40,520 –> 00:47:45,560
look at as well. Thanks Dan. So we do have

713
00:47:45,560 –> 00:47:47,460
a few questions that have come in already. So, as

714
00:47:47,460 –> 00:47:50,570
a reminder to everybody in attendance today, to participate in

715
00:47:50,680 –> 00:47:52,640
our Q& A today just do me a favor and

716
00:47:52,640 –> 00:47:54,830
submit those questions in the Q& A window below the

717
00:47:54,830 –> 00:47:57,180
slides and we’ll get through as many as we can

718
00:47:57,180 –> 00:48:01,080
with what little time we have remaining today. So first

719
00:48:01,080 –> 00:48:05,280
off, I’m going to ask this question. We’ll start with

720
00:48:05,280 –> 00:48:10,190
you, Sridhar. So the question is, ” How do you see

721
00:48:10,190 –> 00:48:14,300
this solution evolving next?” What is the road map, I

722
00:48:14,300 –> 00:48:18,710
suppose. Good. So the first thing we have done so

723
00:48:18,710 –> 00:48:21,950
far is to get the data from Genesys into the platform,

724
00:48:22,000 –> 00:48:25,930
and then expose the information that an agent would need

725
00:48:25,930 –> 00:48:31,080
on agent desktop via lookup services and lookup calls. Next

726
00:48:31,080 –> 00:48:33,660
in our line of work is to get this data

727
00:48:33,660 –> 00:48:36,680
and then provide some sort of analytical view back to

728
00:48:36,680 –> 00:48:41,300
Genesys. So information like how exactly the consumer, who was

729
00:48:41,300 –> 00:48:46,040
dealing with this call center agent, navigating from a page

730
00:48:46,040 –> 00:48:49,710
or from a site based on interaction. Or things like,

731
00:48:49,710 –> 00:48:59,460
what exactly is the redemption rate, and are there other products

732
00:48:59,460 –> 00:49:02,540
that he could recommend which could actually make sense as

733
00:49:02,830 –> 00:49:05,340
a subsequent discussion. Those will be the next part of

734
00:49:05,580 –> 00:49:11,650
the road map. Thank you very much. This next question

735
00:49:12,020 –> 00:49:15,760
is for you, Alan. The question is, ” What can we expect

736
00:49:15,760 –> 00:49:19,870
in the way of change in a CX post- pandemic

737
00:49:19,870 –> 00:49:26,390
world?” I think the biggest thing that we can expect, and that’s

738
00:49:26,390 –> 00:49:30,390
a great question, is that this digital transformation, this accelerated

739
00:49:30,390 –> 00:49:33,960
digital transformation that was forced by the pandemic, is going to

740
00:49:33,960 –> 00:49:37,760
increase. It’s not going to go away. So when we look

741
00:49:37,760 –> 00:49:41,830
at the digitalization of business processes, brands, the cross- channel

742
00:49:41,830 –> 00:49:45,150
experiences, that’s all going to become more critical, and the

743
00:49:45,150 –> 00:49:48,850
ability to move that data across those different channels to

744
00:49:48,850 –> 00:49:51,440
be able to give that integrated experience, which every customer

745
00:49:51,440 –> 00:49:56,830
wants, is going to be the big shift in 2021, 2022.

746
00:49:57,240 –> 00:50:00,170
So that’s really what I’m looking forward to, is the

747
00:50:00,170 –> 00:50:07,300
new digital world that we’ll see exposed coming out. Thank

748
00:50:07,300 –> 00:50:10,600
you very much. Dan, I’m actually going to direct this

749
00:50:10,970 –> 00:50:15,390
next question to you and Sridhar, actually. So, during your

750
00:50:15,390 –> 00:50:17,960
demo, you were showing all of the interactions that you

751
00:50:17,960 –> 00:50:19,820
were putting in from the web page and then receiving

752
00:50:19,820 –> 00:50:23,730
them on the back end. A attendee wants to know, ” Is

753
00:50:23,730 –> 00:50:29,460
this using Adobe Analytics on the web page itself?” Yes.

754
00:50:29,460 –> 00:50:34,640
So, the new partnership that we’ve been discussing is about

755
00:50:34,640 –> 00:50:39,370
the Adobe Experience platform integration, but we also integrate with

756
00:50:39,670 –> 00:50:44,170
several other Adobe products, including Adobe Analytics. So we can

757
00:50:44,320 –> 00:50:47,640
send information to Adobe Analytics and we will be able

758
00:50:47,640 –> 00:50:50,810
to use the information that is in Adobe Analytics to

759
00:50:51,030 –> 00:50:56,460
further inform us about the identity, the sales stage, of

760
00:50:56,460 –> 00:51:02,840
that customer. We integrate with Adobe Tag Manager, Adobe Launch.

761
00:51:03,010 –> 00:51:09,130
We can also integrate with Adobe Experience Cloud, and we

762
00:51:09,130 –> 00:51:14,440
even have integrations through our, what we call data actions,

763
00:51:15,990 –> 00:51:19,200
with products such as Marketo. So there are a number

764
00:51:19,200 –> 00:51:24,440
of integrations that we have with Adobe products through this platform.

765
00:51:28,780 –> 00:51:32,230
Thank you very much. The next question is, ” How do

766
00:51:32,230 –> 00:51:36,320
you carry context from web to voice without forcing customers

767
00:51:36,320 –> 00:51:41,770
to log in on the web?” There are a couple of different options, and we

768
00:51:41,770 –> 00:51:46,750
work together with Adobe to do this. So, we utilize

769
00:51:47,710 –> 00:51:52,160
the cookie, the phone number, and the email to do

770
00:51:52,160 –> 00:51:57,700
what we call identity stitching. Additionally, Adobe has something called

771
00:51:57,970 –> 00:52:02,260
an identity service. So, as you saw when we were

772
00:52:02,260 –> 00:52:06,020
doing the demonstration, we started with an unknown visitor. So

773
00:52:06,020 –> 00:52:10,700
there still are insights you can glean from observing, analyzing

774
00:52:10,790 –> 00:52:14,790
the behavior of people who are unknown. We can see

775
00:52:14,790 –> 00:52:16,320
what part of the world they’re from if they’re on

776
00:52:16,320 –> 00:52:19,590
the website, we can see what may have brought them

777
00:52:19,590 –> 00:52:24,380
to the digital property, we can observe what they’re putting

778
00:52:24,380 –> 00:52:29,440
in their shopping cart or what form fill elements they’re

779
00:52:29,480 –> 00:52:34,730
entering into an application online. And then once the customer

780
00:52:34,730 –> 00:52:38,090
starts to share some identity information, we can begin to

781
00:52:38,090 –> 00:52:40,610
connect those dots. We can say, ” What do we know

782
00:52:40,610 –> 00:52:44,470
about them,” in the marketing automation platform once they’ve shared

783
00:52:44,470 –> 00:52:49,170
their email, maybe after initiating a chat. We can then

784
00:52:49,250 –> 00:52:52,990
use that to see what other campaigns appeal to them.

785
00:52:53,980 –> 00:52:56,070
And then, if they do share a phone number, when

786
00:52:57,370 –> 00:53:00,810
that call comes in we can, in real- time, find

787
00:53:00,810 –> 00:53:03,060
out if the owner of that phone number is on

788
00:53:03,060 –> 00:53:07,840
our website right now, yesterday, last week, and again pull

789
00:53:07,840 –> 00:53:14,530
it all together. So, it is a solution we call

790
00:53:14,530 –> 00:53:19,450
progressively profiling. So we don’t have to have the identity

791
00:53:19,450 –> 00:53:24,860
of all people known. This can evolve as a customer

792
00:53:24,860 –> 00:53:29,280
begins to share information by their behavior and by entering

793
00:53:29,320 –> 00:53:36,430
some information online. Sridhar, anything else to add to that? Yeah, I mean

794
00:53:36,740 –> 00:53:38,650
you got pretty much everything. One thing I want to

795
00:53:38,650 –> 00:53:42,840
add is what we are looking at in terms of reacting

796
00:53:42,890 –> 00:53:47,470
or responding to a particular customer. Predictive engagement, as well

797
00:53:47,470 –> 00:53:51,260
as how we want to include all the marketing that is

798
00:53:51,260 –> 00:53:54,970
being done so far, and how we can actually transition into

799
00:53:55,740 –> 00:53:58,530
an action, whether it is engaging with a call center

800
00:53:58,530 –> 00:54:01,580
agent or engaging with a brand via other means. So

801
00:54:01,580 –> 00:54:03,500
putting them all together, we just want to make sure

802
00:54:03,500 –> 00:54:12,320
we are conveying the right message at the right time. Thanks guys. So

803
00:54:12,320 –> 00:54:15,210
we have time for one more question, so I’m going

804
00:54:15,210 –> 00:54:18,280
to ask the last question and then I’ll wrap up

805
00:54:18,300 –> 00:54:20,950
here today. So, the last question is, “Do you foresee any abrasion

806
00:54:20,950 –> 00:54:23,210
with members who do not want to interact with bots

807
00:54:23,550 –> 00:54:27,270
and proceed directly to a live person without giving a

808
00:54:27,270 –> 00:54:34,570
way upfront?” I think we can utilize our platform to, number

809
00:54:34,570 –> 00:54:39,210
one, learn about the attributes and behaviors of all those

810
00:54:39,210 –> 00:54:48,100
people who may dismiss the bot immediately. Maybe, over time,

811
00:54:48,100 –> 00:54:53,350
you might identify people who are from a certain geography

812
00:54:53,390 –> 00:54:59,170
or looking at certain areas of your website, and those

813
00:54:59,170 –> 00:55:03,620
people are much more likely to dismiss the bot and

814
00:55:03,620 –> 00:55:07,310
ask for agent, agent, agent. And then, in the future, you

815
00:55:07,310 –> 00:55:11,370
can start to put them into a segment for different

816
00:55:11,430 –> 00:55:15,020
personalized treatment. So, when we see people who share these

817
00:55:15,020 –> 00:55:19,470
attributes, connect them right to a live agent. And others

818
00:55:19,600 –> 00:55:23,340
may prefer to engage with bots, for example, and in

819
00:55:23,340 –> 00:55:27,170
those cases you may not want to, even though you

820
00:55:27,170 –> 00:55:29,860
would prefer as a business to connect them to a

821
00:55:29,860 –> 00:55:33,240
live agent, maybe you discover that the customer is more

822
00:55:33,240 –> 00:55:37,700
comfortable engaging first with a bot to see if they

823
00:55:37,700 –> 00:55:40,780
can get that information that they need much more quickly.

824
00:55:42,080 –> 00:55:45,880
Any other ideas there, Sridhar or Alan, you’d like to

825
00:55:45,880 –> 00:55:52,830
add to that? I, personally, have seen things whenever I

826
00:55:52,830 –> 00:55:56,920
need to get a, especially when businesses are having different

827
00:55:57,100 –> 00:56:00,420
mandates based on geographical locations, I would personally just would

828
00:56:00,420 –> 00:56:03,370
like to reach out and then get the information as

829
00:56:03,370 –> 00:56:07,790
quick as possible, so bots work in most cases. And

830
00:56:07,790 –> 00:56:12,680
again, what we’re trying to do here is utilize the

831
00:56:12,950 –> 00:56:17,590
behaviors and attributes, the data that’s available in our combined,

832
00:56:17,590 –> 00:56:23,730
collaborative platforms, to predict how, when, and with whom engage,

833
00:56:24,320 –> 00:56:30,180
so how to engage could be offering a callback, or

834
00:56:31,200 –> 00:56:36,390
a email, or again, suggesting a bot when you think

835
00:56:36,520 –> 00:56:40,000
that’s the best approach. So the AI has the ability

836
00:56:40,000 –> 00:56:45,990
to discover and learn those customer preferences over time. Right.

837
00:56:45,990 –> 00:56:47,570
The only thing that I would add to that is

838
00:56:47,570 –> 00:56:52,290
that we call that the contextualization of the experience. And

839
00:56:52,290 –> 00:56:57,120
so, that could be anything from you’re calling in from

840
00:56:57,120 –> 00:56:59,710
a mobile number so you don’t want to necessarily go

841
00:56:59,710 –> 00:57:02,540
to a bot, to there’s a back- end system that

842
00:57:02,540 –> 00:57:05,030
may have data, say it’s you’re calling in about a

843
00:57:05,030 –> 00:57:07,820
credit card breach and it already knows that your number’s

844
00:57:07,820 –> 00:57:11,820
been breached so it immediately sends you to a live

845
00:57:11,820 –> 00:57:14,730
agent. So, there’s some really interesting new technologies that are

846
00:57:14,730 –> 00:57:17,360
coming out out there, and the way Adobe and Genesys

847
00:57:17,360 –> 00:57:19,830
are putting this together, I think, is definitely near the

848
00:57:19,830 –> 00:57:26,660
forefront. All right guys, thank you very much. I think

849
00:57:26,660 –> 00:57:29,570
that’s a good way to wrap it up. So, first

850
00:57:29,570 –> 00:57:32,250
I want to thank our speakers for answering those awesome questions.

851
00:57:32,720 –> 00:57:34,870
Unfortunately, there were a couple that we did not answer

852
00:57:34,870 –> 00:57:37,160
aloud, but don’t fret. We’re going to follow up with

853
00:57:37,160 –> 00:57:40,270
you via email within the next few business days. However,

854
00:57:40,270 –> 00:57:42,940
if you’re looking for more of a prompt response, do

855
00:57:42,940 –> 00:57:45,860
me a favor and just email us at [email protected] genesys.

856
00:57:46,170 –> 00:57:48,680
com, no caps or spaces, with the title of today’s

857
00:57:48,680 –> 00:57:51,090
webcast in the subject line and we’ll be sure to

858
00:57:51,090 –> 00:57:54,870
get back to you as promptly as we can. All

859
00:57:54,880 –> 00:57:56,940
right, so to wrap up, don’t forget to take advantage

860
00:57:56,940 –> 00:58:00,290
of the additional resources within the resource list below the

861
00:58:00,290 –> 00:58:02,910
slides today. So, clicking on these will open up in

862
00:58:02,910 –> 00:58:04,790
a new tab, and make sure you do so before

863
00:58:04,790 –> 00:58:07,520
today’s session ends. It’ll give you additional information on the

864
00:58:07,520 –> 00:58:11,330
partnership between Adobe and Genesys. Also, as a friendly reminder,

865
00:58:11,330 –> 00:58:13,480
if you could please fill out that survey that’s going to show up

866
00:58:13,480 –> 00:58:16,690
at the end of today’s webcast, we’d greatly appreciate your

867
00:58:16,690 –> 00:58:20,980
feedback. And with that, on behalf of Alan, Sridhar, Dan

868
00:58:20,980 –> 00:58:23,500
and the entire Genesys team, we thank you again for

869
00:58:23,500 –> 00:58:27,170
joining today’s webcast, ” Connect the Dots with Genesys and Adobe.”

870
00:58:27,490 –> 00:58:29,470
Until next time, have a good one everyone.

Meet the Speakers

Allan webber webinar image

Alan Webber
Guest Speaker
Program VP, Customer Experience
IDC

Sridhar jayakumar webinar image

Sridhar Jayakumar
Guest Speaker
Principal Product Manager
Adobe

Dan arra

Dan Arra
VP, Sales
Genesys AI
Genesys

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