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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 CX, our Genesys Multicloud CX 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.
