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On-Demand LinkedIn Live

Make personalized customer experience a reality with
Genesys AI

Register now for this On-Demand LinkedIn Live session where we walk you through our recent Enterprise Connect Best Application for AI winning solution – Genesys Predictive Engagement. During the live session we discussed how businesses can anticipate individual customer needs building on every interaction across channels and events to improve and personalize future engagements.

Genesys Predictive Engagement delivers AI-powered, personalized and contextual experiences before, during and after critical so customers stay engaged and continue toward the desired business outcome. Working within Genesys Cloud, Genesys Predictive Engagement is part of the AI engine that enables Experience as a Service, the company’s vision for the contact center industry to facilitate end-to-end, personalized cloud-delivered experiences.

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Thank you all for joining. I’m excited to be here

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today to talk about Genesys AI. My name is Elcenora

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Martinez, and I’m the VP of product management for AI

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here at Genesys. I’m Dan Arra. I am the VP

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of sales for AI at Genesys. So let me walk

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you through what we’ll be talking about today, and at

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any point in time if you have any questions, please

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feel free to ask them in the chat. We are

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going to have folks that are going to be monitoring

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live and able to ask your questions, and if you

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do miss anything, this whole recording will be available on

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Genesys. com in a couple of days. So today we’re

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going to be talking about a couple of trends that

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you really can’t afford to ignore and how those dovetail

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really nicely into what we believe is a roadmap for

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success. We’re going to talk about why Genesys AI and

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introduce you to this notion of experience as a service,

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which is a best way to supersize the experience that

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you’re providing your customers today. We’re going to go deep

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into our predictive engagement product, which is one of our

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lead offerings on the AI portfolio. Then we’re going to

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talk about where to go for more information or if

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you would like some additional demonstrations. So first and foremost,

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AI is here to stay. I don’t think I have

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to tell anyone that. IDC estimates that this is a

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market that’s going to grow to $ 97 billion by

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2023, and that over 50% of interactions are going to

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be augmented or enriched by AI. But what we want

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to do is inspire any chief marketing officers or chief

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experience officers to understand how AI can help you in

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the contact center, to make sure that you have the

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ability to lobby for some share of market where your

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company wants to spend their AI dollars. The reason that

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is, is because you want to be able to differentiate

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the experience that you give your customers, and that requires

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being able to personalize every single one of those interactions.

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Now, the need for personalization is going to stretch beyond

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what is available on a customer data platform today. CDPs

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have the ability to store information about customers, but if

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you really want to take that next level on personalization,

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it’s going to require stitching, enriching, and augmenting much of

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that data to be able to personalize the interactions. If

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we think about the fact that 50% of these interactions

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will be augmented by AI, then we need to go

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beyond just a single interaction in a single channel to

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really be able to understand the context across many channels

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over the lifetime of a customer’s relationship with a brand.

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So being able to harmonize and understand all of that

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context is going to be really important. No AI investment

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would be complete without the right level of analytics and

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reporting. So we want to make sure that you have

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the ability to really understand how your AI investment is

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performing, the optimization and really be able to fine tune

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this investment. Now, Forrester estimates in a survey last year

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that 53% of companies already have implemented or are implementing

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some form of artificial intelligence, and this is great because

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what we see is that we’ve passed critical mass. But

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it also means that 47% of companies have not yet

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deployed artificial intelligence. Now, there’s a couple of barriers to

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adoption and that can range from being able to achieve

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it at scale, to security, to adoption, and even some

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of the conversational intelligence. But what I want to leave

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you with today is how Genesys is helping you overcome

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some of those challenges. So I’m going to go through

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these one by one. We have made a significant investment

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in a data science team that is very focused on

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AI ethics and algorithmic integrity, and to be able to

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deliver some of this AI at scale. So we have

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the ability to optimize, orchestrate and really automate many of

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these processes. For those of you that are Genesys customers

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today, that our Genesys Cloud, our Genesys Engage and our PureCloud

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platforms are all 100% secured to the core. We’re also

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driving adoption because the way we think of our AI

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platform is everything is event driven, and what this means

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for you is it gives you greater flexibility to be

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able to think about web events, conversation event, maybe even

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custom events, and so your starting point can different ways

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it can really drive that adoption within the company. We’re

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also very focused on usability. We’ve made great investments in

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a design thinking functionality, so that delivering products that are

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user centered are very much to the core of how

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we think of our roadmap. So we think about ways

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to onboard, to make sure that you can see successes

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early on in ways that it doesn’t require an army

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of data scientist. Then finally, we’re really focused on bringing

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you a higher level of conversational intelligence through all of

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the bot interactions in ways that it feels like you’re

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conversing with a human agent. So why Genesys AI, and

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more specifically why predictive engagement, which I said is one

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of the lead offerings in our AI suite of offerings?

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Simply put, the average consumer receives up to 10,000 brand

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messages every day, and what that means for you as

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a brand is you have to rely on information that

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exists within your own customer data platform, enrich that with

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CRM information or some other custom applications to really reach

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that target customer. But there’s definitely a better way. If

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you think about your customer at the center of how

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you think about these experiences, we want to introduce you

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to the notion of experience as a service as a

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better way of delivering exceptional experiences, and what this entails

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is to really think about all of these interactions we’ve

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talked about over the course of a lifetime of a

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customer and a brand, and all of the data that

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is collected both on the customer side as well as

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all of the information that’s collected from all of the

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employee and agent interactions, and enrich that in augment it

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with artificial intelligence, so that you have a higher level

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of conversational intelligence and so that you can really optimize.

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So what that does is it gives you the ability

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to really provide experience as a service to every single

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one of these customers. Now, this is a big idea,

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and so what I want to do is I want

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to talk about what some of these imperatives for experience

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as a service are. As I go through each one

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of these, what I want to challenge you is to

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think about how well each one of your companies is

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doing on delivering some of these imperatives. So as a

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customer, I want you to show me that me before

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you’ve ever met me, and that means I’ve been a

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customer for a long time. I haven’t necessarily had to

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interact with your brand, but when I do, you already

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know who I am and how long I have been

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your customer. In some cases, I want you to help

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me before I even know there’s a problem. Let’s say

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my bill has increased, my telephone bill has increased by

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25%. Wouldn’t it be nice if I got notified in

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advance? Maybe I want to look at a different data

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plan. Maybe I want to look at different options. So

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reach out to me before I even know there’s a

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problem. Know the road I traveled to get here, and

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this can be one of two things. It can be

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understanding that your conversation started over the phone and ended

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with an agent, and throughout that entire experience, you’ve retained

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the context, or it can also be again over the

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course of a customer’s lifetime with a brand, understanding what

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they’ve been through. This one is so important to experience

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as a service, present me with the answers I want

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and the ones I didn’t know I needed. Often time,

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our agent assist tools allow agents to provide customers with

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the right information in realtime, but we feel that it’s

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important to take that a step beyond, and that means

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really look at what the customer may want, what are

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some of the other products and services that they’re using and

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how you can really help them in ways that they

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didn’t even know they needed, but it will be a

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better experience for them. Don’t ask me to repeat myself,

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I think every single person has had some form of

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experience with this. We want to make sure that we

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explain things to an agent once and that context is

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available throughout the entire interaction. Empathize with my situation, and

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what that really means is that in that moment of

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truth, we want the agents to be able to respond

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with the appropriate emotion. So there are certain hearing aids,

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as we call them that have been built into the

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entire platform to help the agent respond appropriately. Fix it

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and stay with me until it’s fixed, and oftentimes, check

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in with me to make sure that it all worked

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out, and this goes above and beyond just a survey.

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This goes with having the right level of empathy, where

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you are reaching out to these critical customers and make

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sure that we’ve been able to resolve their problem or

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their challenge or their concern. Then above all, keep my

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data safe. So what this does is it brings the

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concept of experience as a service to life in ways

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that you can really understand how to deliver that exceptional

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service. So before I turn it over to Dan, let

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me spend a couple of minutes recapping some of the

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things that we’ve talked about. An event- driven orchestration platform

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that allows you to be proactive. Reach out to your

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customers in advance. Engage with them in that moment of

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truth. When there’s a break in the workflow, or when

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there’s a possibility that they might churn. Personalize every interaction,

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show that them, strengthen the relationship, and above all be

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able to retain history and the context throughout all of

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the interactions because empathy builds trust and trust builds loyalty.

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So let me turn it over to Dan to run

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through a demonstration of predictive engagement. Thank you very much,

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Elcenora. I’m going to provide a demonstration of Genesys predictive

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engagement, and before I jump into the demonstration, I’m going

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to go through a single slide to connect the dots

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a bit between predictive engagement and experience as a service.

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I’m going to highlight some of the elements that Elcenora

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touched on prior to doing the demonstration. So you’ve heard

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quite a bit about the show me that me, and

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the way that we do that with predictive engagement is

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we observe what is happening in all of the interactions

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between the customer and your business, what’s happening on your

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website, what’s happening in the contact center, and we analyze

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that information in realtime for the purpose of segmenting those

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customers into different groups and personalizing the experience and driving

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realtime interactions that demonstrate that we’re showing the customer that

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we know them. At its core, empathetic communication is about

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listening, is about letting people know that you really understand

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who they are. We’re answering the questions, are you really

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listening to me? Do you care about me enough to

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remember what I’ve told you before? That’s what we’re doing

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here as we go from left to right. We’re leveraging

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all of those interactions and observations, analyzing and then predicting

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an outcome, predicting how to engage, that could be with

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a bot, and it could be with some information, more

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context that makes the agent smarter, more effective. Maybe we’re

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using AI to predict the right agent to route that

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interaction to. What we’re doing here is we’re driving people

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toward outcomes, outcomes that are your business’s desired outcomes, as

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well as the customer’s desired outcomes. I may be a

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customer visiting a website, interested in making a purchase, or

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I may be looking to get support for a product.

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Those are my outcomes as a end customer. The business

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wants to deliver the right information at the right time efficiently.

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So everybody’s outcome is being considered here in an effort

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to connect the dots between the analysis, the outcome prediction,

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the first touch interaction and so on. As I go

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into the demonstration here in a moment, I want you

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to think about the real life experience of walking into

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a retail store, and somebody walking up to you saying,

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a clerk at the store, ” Can I help you? Can

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I help you now? How about now?” An experienced a

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store clerk, salesperson or support person is going to observe

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what you’re doing, what are you putting in your cart,

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where are you stopping in the store, which aisles and

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so on, and they’re going to take that into context intelligently

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about how, when, with whom to engage to personalize that

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experience. Okay, now I’m going to switch and share my

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browser and show you the demonstration. All right. So I

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am going to show you a visitor on the left,

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visiting a website, the GSOL website, and on the right,

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I’m going to give you a peek under the hood

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of the predictive engagement platform. I’m going to show you

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what the system can see, how we’re analyzing and predicting,

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when and with whom to engage. Then after I do

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that, I’m going to show you an agent experience. So

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keep in mind, initially, I’m showing you what is happening

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under the hood of the platform. What we’re doing here

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is looking at all of the people who are on

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the site, some are known and some are unknown, and

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we’re observing their interactions. This is also looking at what’s

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happening in the contact center and connecting through our event-

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driven orchestration to other systems where we’re leveraging CRM data,

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marketing automation data, and even other systems that have web-

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based APIs. So I’m going to highlight this one individual

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that happens to be me, and you’ll notice something here,

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as the visitor on the left starts to navigate around

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and look at different pages, we’re going that behavior being

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observed and analyzed, and we’re even making predictions about this

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unknown visitor, now unknown. We can see what part of

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the world they’re located, some technographic information about the devices

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that they’re using, and we can see historical information here

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as well, if there were previous visits. So again, I’m

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unknown at the moment. But now I start to put

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something in my shopping cart. I add these batteries to

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the shopping cart, and you’ll see here that now that

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I’ve added something to my shopping cart, the system can

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see what is in the cart, what’s the value, which

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product category. We can see that were identified as a

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particular segment, and there are other events that occurred like

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our cart value changed from zero to $ 216. So

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before the visitor on the left proceeds to check out,

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they may want to log in because they have some

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stored payment information in the system, and you’ll see a

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few things happening. Now that we’ve logged in, we know

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the identity of that visitor, and we can see that

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maybe they’re eligible for other communication options. Now that we’re

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logged in, maybe our purchase history or the value of

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our shopping cart gives us a few more options to

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communicate. That just appeared after I logged in. When I

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proceed back to check out, I go back here and

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you’ll see that the prediction of checking out is higher,

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and just to highlight here, we’re making two predictions that

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make sense in our configured for the GSOL customer. Your

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business and any other business would have different configured outcomes,

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making a purchase or being identified as a lead, those

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are sales and marketing outcomes. But you could have care

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outcomes, like make an inbound call for support or open

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a trouble ticket. So here, the visitor in their purchase

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motion doesn’t know what the discount code is, and before

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they check out, they start searching for it. Maybe they

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go over here and they search for discount codes, and

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we’re going to pick up that search information and what

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just happened here on the left is we triggered, the

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platform triggered an automated chat offer that is leveraging all

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of the context, all of the analysis and predictions that

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we’re observing under the hood in the platform on the

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right. As the visitor accepts this chat, they’re going to

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initially be prompted with an intelligent chat bot who knows

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this is Dan, and that he’s likely to need help

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with his purchase today. So first, we’re leveraging the intelligence

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bots, and here we may want to say, ” Yes, I’ve

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got a question. Wondering about,” oops, type, ” about shipping availability.”

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We are going to get a response back telling us

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a bit about that topic, and then, ” Yes, I’ve got

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questions about my discount code and I want to talk

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to an agent please.” So when that initial step connecting

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us to an agent occurs, we’re doing a few different

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things here. We’re going to answer this interaction, and now

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I’m going to show you the agent view, okay? Again,

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what we were looking at was what was happening under

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the hood. But now as an agent, I have access

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to what was in the shopping cart, maybe previous visits,

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the likelihood, hey, this visitor got very likely to making

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a purchase and then abandoned, and then we could continue

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to watch while we’re thinking about what we’re going to

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say, watch what that customer is doing. We’re going to

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leverage these segments and the behaviors, and as I hover

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over as an agent some of these elements on the

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right, I decide I want to respond with a canned

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response about discount codes. So I search for my available

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canned responses. I find the appropriate one here and now

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I can guide that customer to the right information so

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that they have the context and information that they need

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to improve and streamline the process. Now they know that

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the discount code could be entered here. They have that

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discount code available to them. I apply it and proceed

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to check out. So when I check out, you’ll see

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here that the outcome is achieved. We see the payment

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was made, what was the size of the order. This

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goes from 99% to 100% and we’re ready to wrap

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up. We ask the customer, ” Do you need anything else at

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the moment?” Maybe they say, ” Well, yes, I actually had

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some questions about your panels.” So the agent has an

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opportunity to take advantage of that additional conversation, create a

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new lead in the CRM with our integration here to

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extend the reach beyond what’s just happening in predictive engagement.

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We could create a lead here, but now I’m going

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to show you a separate example where we do that

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in an automated fashion. That’s going to end up creating

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a lead and adding an individual to a campaign all

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under the hood automatically. So we wrap up this interaction

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and we now know exactly which wrap- up code to

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use because we saw that order placed, and now we’re

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done here with this particular interaction and we’re waiting for

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another. So let’s say this visitor later that night or

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the next day decides they’re going to go back onto

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the site and put some items in their shopping cart.

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In this second case, so you have to fast forward

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a little bit and this is a later that that

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night. They put some items in their shopping cart, and

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we’re going to, again, observe what that visitor is doing

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so we can see under the hood what’s happening. This

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visitor decided they would like, Dan over here would like

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10 of these items and he proceeds to check out.

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So now he’s got a pretty high cart value and

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he’s eligible for another discount, and this is a solar

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panel so it’s a different discount code. He doesn’t have

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it available to him at the moment, and he starts

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to again search around for it. He abandons this shopping

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cart, and here, what we’re doing is taking advantage of

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all that information and creating a new lead in our

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CRM system that is going to inform the enterprise account

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rep who is working on deals larger than $ 2,

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000 to follow up with this customer the next day

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or first thing in the morning. Now, if I take

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a look here in our CRM system, and we want

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to observe what happened under the hood, we’re going to

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look up that lead and we’re going to find this

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lead, Dan was created. You can look at those details.

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In here we can see we’re leveraging the journey, the

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intelligence of predictive engagement can see that the shopping cart

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value was $ 2, 100. We can see what the

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items were that were in the cart, and then we

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also created a new member to our GSOL campaign. It

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is going to inform the enterprise account rep that he

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needs to follow up, either initiate a call or send

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an email to follow up with Dan the next day.

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So that concludes my demo. Thank you, Dan. That was

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great. Thank you for joining us for this edition of

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LinkedIn Live and what I hope is the first of

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many that do a deep dive into the Genesys AI

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portfolio. Thank you all for joining us. If any of

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you would like more information, there is a self guided

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tour available at Genesys. com, along with a hyperlink at

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the bottom that allows you to reach out in case

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you want to contact any of our account executives, your

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Genesys support team, or generally any more information on anything

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that you saw today. So again, thank you for your

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time. We appreciate your partnership and we look forward to

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seeing you at another edition of LinkedIn Live soon.

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Meet the Speakers

Elcenora martinez webinar image

Elcenora Martinez
Global VP, Product Management, AI
Genesys

Dan arra webinar image

Dan Arra
Vice President, Sales
Genesys

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