Hello, everyone, and welcome to today's webinar. How digital leaders are transforming their customer service with AI? Brought to you by Technology Services Industry Association and sponsored by Coveo. My name is Vanessa Lucero, and I'll be your moderator for today. Before we get started, I'd like to go over a few housekeeping items. Today's webinar will be recorded. A link to the recording of today's presentation will be sent to you within twenty four hours via email. Audio will be delivered via streaming, All attendees will be in a listen only mode. And your webinar controls including volume are found in the toolbar at the bottom of the webinar player. We encourage your on the top left corner of the webinar player, and we will open it up for a verbal Q and A portion at the end of today's session. Lastly, feel free to enlarge the slides to full screen at any time by selecting one of the full screen button options. Which are located on the top right corner of the slide player. I would now like to introduce our presenters today. John Ragsdale, distinguished Vice President of Technology Research for TSIA. Bonnie Chase, Senior product marketing manager for Coveo, and Neil Cassetti, director of product management, also with Kaveo. As with all of our TSAA webinars, we do have a lot of exciting content to cover in the next forty five minutes. So let's jump right in and get started. John, over to you. To today's webinar, I I think it's fair to say there has been a great deal of hype around, the topic of artificial intelligence. And one of the most frequently asked questions from TSIA members. In fact, I just got one this morning, is what are people doing. You know, we've heard the hype. We've heard the potential. But, today, we're gonna focus on some real world case studies of how companies are leveraging AI to improve both the employee experience and the customer experience. So I know you're all getting really heads down on your planning for twenty twenty one, and we're getting a lot of questions on what is realistic for us to expect from AI to plan for next year. And I think you're gonna get some good answers to that question today. So when we're looking, at customer service in particular, there are sort of three areas where I'm seeing people beginning to leverage AI with some very good results. The first, of course, is around intelligence. And this means that the AI engine is able to really understand what you need, what you're looking for, what you're asking regardless of how it's asked or phrased or spelled, and it's able to give real time suggestions to either customers or employees as the next best step or the right answer to something that they're struggling with to help them keep moving, along a process or solving a problem without spending a lot of time researching or talking to their peers or, searching for content. Another thing is becoming predictive. And this is where AI and machine learning come in to begin analyzing customer behavior and customer journeys and understand what sort issues are likely to come up depending on what technology environments they're in, maybe what region they're in, and become a little more predictive So when you start to ask a question, it anticipates what that question is. And, again, regardless of of how you ask it or what wording you use, It knows what you mean. It can proactively serve that content up to you. And personalization is is really huge from, a customer experience standpoint, how can we make sure that the customer feels, unique, and they're getting a very personalized experience, and they don't feel like just cog in the wheel, of a big vendor. And one of the opportunities here is that we are constantly filtering the content that customers are are given, for example, when they're, searching on self-service, to make sure it's it's only appropriate to the products they have or the environment that they're in. So let me, drill into a couple of more pointed examples around these topics And the first place that I think, we're seeing some, some of our more advanced members getting some great leverage from AI is around automating knowledge it. And the truth is, most kilometers programs that I see fail fail because they're not spending enough time maintaining the content. And once that knowledge base starts to get full of a lot of duplicate and outdated, knowledge articles, customers start using stop using your self-service site, employees, stop trusting it, and start googling instead of looking at your internal content, And I've got a a question on my annual kilometers survey about how often kilometers content is updated. And seventeen percent of companies say we have not updated our content for a very long time. But I think if we're really honest with ourselves, I think the majority of companies are not doing a great job of updating their content. So AI can really help here because it can automatically tell you, which content is never used, which content is always showing up in search results, but nobody's clicking on it. What content are they clicking on and immediately dismissing, which tells you it's probably an confusing title, or maybe it's an outdated article. And how can we constantly be adjusting relevancy So whatever it is they're actually looking for is bubbling up to the top of the list and the least useful information is moving down. So that is one example. I think we're gonna hear some examples of that today. Another is on, prioritizing knowledge creation. The reality is none of us have as many dedicated knowledge management resources as we would like. And it's often something that we end up doing in our spare time. And one of the reasons that customers frustrated and stop using self-service is they're searching for content. They can't find what they need. And after a couple of visits to self-service and not finding useful information, they're probably just not even gonna try again. And one of the things that analytics can do is help you really under stand what people are searching for and not finding. And that can prioritize those limited resources you do have to creating the content that's gonna give you the most bang for your buck. And another data point here, only thirteen percent of companies are leveraging analytics to identify and content. I talked to companies that are looking at reports of search strings that didn't return any results they get hundreds of pages of one and two word searches without really understanding what it was the customer was looking for and analytics can definitely solve that problem. And the third example around personalizing the customer experience you know, part of this is, again, looking at the customer journey and patterns of historical behavior, and you can start to become very predictive about what this particular customer needs at this particular point in their journey. But it's also key that we're filtering the content. You know, everybody loves an FAQ list. When a customer goes to the website, it it's just magic. If you've got an FAQ list, and the problem they're experiencing is right there on the list. They don't even have to look for it. But if your FAQ list is filled with a bunch of content, about products. They don't even own or versions they're not on. It's really kind of useless. And again, they stop even paying attention to that list. And according to my data, only twelve percent of companies are filtering FAQ lists in self-service to only show what is relevant to this customer. So I really missed opportunity there, to better personalize that customer experience. If you're interested in more on these topics and the data that TSIA has, I've got two reports I think would be helpful. The state of knowledge management twenty twenty includes, data from this year's knowledge management survey. And boosted boosting knowledge effectiveness through analytics goes into detailed examples on usage analysis, relevancy analysis, content gap analysis. So if you're a TSIA member, you're going to see a panel, of resources on the left hand side of your webinar player, and you can click through and get access to those documents. So enough for me. Let's get to some real world examples of AI. And I'd like to turn things over to our first guest speaker today. Bonnie Chase is the senior product marketing manager for Coveo, Bonnie. Take it away. Thanks so much, John, and thank you everyone. Happy to be here, today with Neil. And yeah, so So as as John said, I mean, there are so many different use cases for leveraging AI. And before we jump into that, I want to just kind of frame up the conversation a little bit, for you all attending. And starting with a couple of couple more stats that really show how much people are interested in AI and leveraging AI for service This came from the third state of service report from Salesforce, a hundred and forty three percent over the next eighteen months at the time that this this was put out. That's huge. Right? And the next stat showing, that agents are actually benefiting from by using AI and and they they spend less time on mundane tasks. And so these two stats are interesting because one, it shows that there is an increase in the interest and use of AI. And the second set says that it is helping. But at the same time, as John mentioned, I still hear a lot of times. What does this actually mean and what are people actually doing with AI. And AI isn't new. It has been around for a long time, and it's a lot more x than just two letters. Right? And so really what we want to do today is kind of help you make sense of AI for for your organization and identify, you know, what that really means for you. And so in in this slide, you know, just talking through the the constant exchange of data. You know, you may need it to predict or recommend or guide decision making. But really AI is necessary to be able to do these things at scale. And so, Sunil, I don't know if you wanna add more to to this slide about AI before we continue? Yeah. I mean, I think one thing to to really touch on is that, you know, Coveo machine learning is really about looking at data and understanding and finding those trends. You know, mass amounts of data that, you know, looking through as a as an analyst trying to understand, you know, what needs to be, updated or, you know, what are people actually looking for. It's something that's really challenging. So machine learning is really great at looking at that data, finding those trends and, and taking action to deliver those kinds experience predictions, recommendations. So yeah, I think sometimes when you think of AI as an entire technology, it can be really overwhelming, you know, you might think about robots and, and, you know, intelligent beings that can can speak and have conversation, but really there's there's these specific tasks that, AI, machine learning, and other technologies within there I can help with. Absolutely. And so, yeah, that's a great point. It's, it's it helps you scale. It helps you provide those individual personalized experiences that people are expecting today, which is really why this matters. Right? And so, you know, when we when we ask the question, why does it matter? It's because we're needing to scale and provide these personalized experiences because that's what people are expecting today. They're they're used to having, these personalized experiences day to day in every interaction that they have whether they're at home watching Netflix, whether they're shopping on Amazon or, you know, talking to Alexa. Right? And so it's in our everyday lives, having that personalized experience. And that's really what's driving the this push to AIs is that desire to have that individuality in your in your service experience. And we when we think about the service experience, This is the experience that most companies are offering, which is really a disjointed experience across multiple channels. You know, we do know that most people start in Google, but at the same time once they actually get into your digital properties, they end up jumping around and what they actually want and what we we need to provide for them is this more streamlined experiences that AI, AI can provide. And so by understanding who you are, what you need and what you need next, being able to provide that streamlined support experience. And so when we think about AI and where you should start. I think the first step is understanding what you're trying to optimize or what you're trying to automate or whatever that goal is in your cost to support journey. And so we like to use this cost to support to kind of show how, you know, depending on whether you are, helping customers through agent assistance, or customer self-service, the more you're able to push them to self-service, the more customers you're able to to serve and, the lower the cost. And so AI can really be applied to any of these areas. And so I think the first step is understanding how it can be applied to these areas. And so as we go into this presentation, we're going to kind of walk you through, this perspective. And so we have the the customer perspective the agent perspective and the content manager perspective. And so content manager here isn't necessarily the knowledge manager from a KCS perspective, but it can be. It could be the admin. But wanted to have that consideration in there as well. And then the foundation for this all is really being able to act access the content and so search permeates across all of these. So let's start taking a look at how AI is applied in each of these areas starting with an intelligent search. And we know that this is important because over half of agents have to toggle between screens right now in order to get the information they need to do their jobs. And you all know what a poor search experience feels like. But what do we mean when we say adding intelligence to search? I think there are a couple of things, that that that actually means. So Neil, if you could kind of explain, you know, what having a unified intelligent search really means. Yeah. I mean, there's a lot of different components to it, and it it really is the basis of the experience. Right? Being able to unify your content in a single location where you have access to all that rich information that's unstructured or structured metadata And in order to do that, you need connectors that are able to pull that information in. And in some cases, actually use what we call early binding security, inspect the permissions of those source systems so that as you're searching against the content, if I'm searching for sharepoint or confluence of Jira or whatever kind of, content I'm able to access specifically the content that I have access. Says to. And that's, you know, reducing that, that swivel chair that we always hear about. Being able to deliver that then in a rich UI that's able to be completely customized and have control over every pixel able to deliver the kind of personal experience that fits your brand. Again, using machine learning to, to adapt that experience both through things like query suggestion, reautomatically ordering of the results, and, you know, many different ways to be able to actually dynamically change experience. And then learning from that, having the analytics to understand what was done, and, you know, in some cases, actually take some search optimization tactics to you know, feature a specific result when you, you know, you have, you know, something that's a timely, piece of content that you want to surface in front of users immediately, you know, change the type of content that is being surfaced based on a query. So this, these are all the kind of attributes of, you know, what we consider an intelligent unified search. Like, again, that's really the basis and what we'll expand on a bit more here. Yeah. And, you know, we often start with search because it's really about getting information in the in the hands of the people who need it in that moment, whether they're an agent or a customer. But if you're able to unify that content that gives them access to everything and allows you to put that layer of machine learning on top of all of your content, not just on separate silos. So it really brings everything together to really streamline, how, the understanding of how your customers are using your content. And an example of this on the next screen, you know, this is what a relevant search experience feels like So as Neil mentioned, you have dynamic navigation that can adjust depending on what somebody is searching for. Recommended results that adjust in real time based on user behavior. And so these are things that that, can make your search experience more powerful, more so than a federated search or a your typical search. The next area, you know, going into customer self-service, this is where, you know, you wanna detect issues before they arise. Guide them to the right channel that suits their profile, answer questions in any of the channels that they are using, and make that support experience seamless, and effortless for them. And so in talking about the customer self-service use case, you know, it's not just about making your content online or providing that digital experience. We like to say that that we're no longer in a digital economy. That was ten years ago. We're in an experience economy now. And so just because your your support site is online and your and is online doesn't mean it's going to be useful and effective. And so, you know, over half of customers still find self-service portal is difficult to use. And so that's why adding AI, into the self-service experience can really help improve that and make it easier for them to use. And, you know, one of the one of the best ways of doing this is creating that personalized experience. And so, you know, why don't you tell us a little bit about how that personalization kind of gets created behind the scenes? Absolutely. So this, what you're seeing here is, kind of illustration of something to call our dynamic user profile service, which is The way that, you know, users interact with, and, I mentioned earlier, usage analytics as people are searching as they're clicking on content interacting with recommendations. All of that information is that data. It's that underlying information that the machine learning needs to be able to explore stand the trends. What we're gonna get with this user profile is things like explicit attributes that we know what our user through, you know, through CRM or through whatever tool, we're able to share that information, the products they own, the entitlement of their service, who they are, the developer, the administrator, the business owner, what that sort of thing, but also behavioral data is that know, what did they do? What were they clicking on? What were they searching for? How did they navigate through the site? And even beyond that, then starting to look at that and being able to identify and tell to be categorized things like, you know, this user is, based on their behavior and their their, you know, overall experience, These are the kind of things that they're looking for. This is the kind of information they're interested in kind of categorizing them. And so what you still see illustrated here is an example of two separate users where they're both typing the same set of, terms. Right? They're starting to type the word account. But completely different, recommendations for for queries. And this applies not only to queries, but also to the way that we boost the content automatically in in the results that they'll get. And across other aspects of of the machine learning. So it's it's really kind of a basis of understanding the user better and being able to deliver that kind of personal and I'll I'll show this, in a bit more detail. And I think, maybe on the next slide as well, one thing that we can pull from this user profile is something we call user actions. And what this is is as a user goes from one device to a other or one session to another. So a session would be like Coveo to your website now, and I go to your website two hours from now. We can start to stitch that information together. So number one, we know that I visited your site now and two hours later, but Maybe I visited on my phone, and I've authenticated into that community through my phone. And then on a completely separate device, I've done the same thing. Well, we know that that's me on both of those devices now. We can start to stitch that information together and look at it holistically. So this allows us to then surface information to the agent. Again, I'll show this in the demo so that they can have that insight they can understand the journey. They can speak to a customer with that kind of personalized, approach to say, and I see that you view this content. I I understand that, you know, you may be considering this document. And did you have any questions about it or was it helpful? I think that's that's really important and it's a great aspect of, our profile service. Yeah. And so, you know, through that profile, these are the types of experiences that you can create. So this one, for example, understands what you and and others like you. Are interested in or may want to learn more about. And then the next one really shows another example of, you know, assigned in person and, you know, because of what they've recently purchased or because they love running or pick up where you left off, this Netflix type recommendations for content can be provided to the customer as well. Do you wanna add anything to that, Neil? Ex exactly. Yeah. It's really like we are able to do an anonymous level of personalization based on that behavior, right, based on those, and I can show that in the demo, but those anonymous types of behaviors and attributes that are maybe a little less implicit, but, are a little explicit, but there, there's still information that help us to understand who this person could be. And against all the other sessions and the other users, we can then start to say, you know, you fit in this cluster people like this. So that's where you get that people like you type of recommendations. And here, what we're seeing is, you know, Susan Susan is a as an individual. She is a person who we know some attributes about. We also know all that you know, that anonymous browsing type experience, but when we know what device she has and we know she likes running, we can do much more personalized recommendations for her. And it's it's key, you know, when you're looking into AI technology, it's something that a lot of companies saying right now. So it's important to understand what type of information they're collecting about the user and how they're able to use that to improve the experience. So we'll take you through a few more examples of AI and customer self-service. This is another one intelligent cases. This is something that we actually recently just came out with, in September. And this idea is really around the case submission process And what we found is that not everybody needs to deflect cases. We need really need to be thinking about that new versus known And if you're KCS, you you're very familiar with the new versus known ratio. And and really the idea is that if it's a known case, that has content that can resolve it that should be deflected, and solved with content. If it's a new case that maybe needs a little bit more high touch, doesn't have content available, then we definitely want that to, be streamlined to the support agent so that it can be resolved quickly. And so this, this process helps both improve the customer side and the agent side with AI. So Neil, can you talk a little bit about what what's actually happening, from an AI perspective that can help streamline cases as they come through? Yeah. So what we're doing, again, you mentioned we, we historically were focused on deflection, and that's, you know, that's been around for quite a long time. And that's still obviously very important to do, but more and more when you think about, providing a great customer service experience. It's not about putting more and more roadblocks in front of your customer. And, you know, making them jump through hoops in order to get a case submitted. It's it's really about letting them submit their case with the most correct information as quickly as possible so that the experience feels good. You know, they're trying to get help you as an organization. So you don't want it to be a, a friction based experience. So giving them the ability to do that in a streamlined way, but collecting all the right information and and getting the case, into the right agent to help him. Yep. Exactly. Couple more examples. So, an in app experience is another one. So that's really part of that unified content experience. If you're able to unify all of your content, then you're then you can actually pull that content into into your own experience and provide contextual information there. Another example is chat bots. So chat bots are something where we know, you know, when they first came out, they really weren't that intelligent. And there's a lot of manual work that happens in, building out that workflow. But if you add intelligence to it, something like Coveo where we do have that AI powered search, then, you know, you have a way to always provide an answer even if there isn't a match. And then similarly to that question answering, and Neil, this is something that we haven't quite come out with yet, but this is another example of, using machine learning. Can you tell us a little bit about this one? Yeah. It's, like you said, it's currently a pilot, targeted for q four. And this is the the type of experience that pulls, you know, the snippet of information that is most relevant out of the content. So this is a machine machine learning model that looks at the content well structured HTML type, let's say, knowledge articles, web based content, and pulls that snippet out. So instead of you know, instead of giving you a list of results, we'll actually give you the snippet of information from that first result that answers the, the question. And it, it kind of has a natural flow into a chatbot experience. So you can see how, you know, this is in a search page, but also in a chatbot, you might wanna with a snippet of information rather than a set of links. So I'm really excited to see this Coveo. We do have some information out on on our blog as well. How to kind of think about this and how to get prepared for it even, to make sure that your content is consumable by this type of model. No. Yeah. And this is kind of where things are going because this is, you know, this is what Google does with their results. They get they provide featured snippets, which actually rank above you know, the zero results. But, yeah, it's just moving toward that experience that people are are used to. I'll quickly go through some of the agent assisted examples that you'll you'll also see in the demo. But, really, again, there are specific use case for agent assisted support. And so we'll walk through some screens there. So I think one of them the the ones that we're all probably used to seeing is something like intelligent term detection. So here, you know, looking at the subject and description and being able to pull relevant keywords and use that to provide recommendations And then another example is automatic relevance tuning, which this goes beyond, just looking at keywords. Right, Neil? Yes. Absolutely. Yeah. So it's really, again, like you said, pulling the turns out of the subject description of those learn blocks of text, learning from that, and then, adopting or sort of adapting, the results. So maybe what is more relevant the second time around, after agents have addressed these types of cases. So it's it's, again, it's kind of dynamic and updating as it's used. Mhmm. Yeah. And so something just to think about is, you know, when you're looking at a tool that provides recommendations, how are they providing recommendations? Is it manual? Is it based off of keywords? Is it based off of keywords and behavior? So there are a lot of things that, differing ways that tools can provide this type of capability. And then smart context again, just looking at more, more keywords and more categories to help add more context to the case. So, understanding what product is being used and, things of that nature to just give more context to that user profile. And then finally, you know, this is this really is goes to Neil's example of the user profiling service, just showing how we can actually stitch that information together about what they view clicked search, you know, where they did those searches, things like that. And then finally, from an admin and content manager perspective, I did wanna show couple of screenshots, before we move into the demo just to show what, what a unified service journey can look like from a dashboard perspective. There's a lot here, but I'm gonna break it down quickly. Being able to actually calculate your cost of support and your savings is huge. And so having something that can unify your data that can actually, look at look at data from all of the content sources, all the repositories, and being able to plug in numbers and do that calculation for you. Is really helpful in proving that ROI. The ability to see how your machine learning is performing where it's being used and and what you need to adjust is key as well. And then, of course, just having your general overview information around, you know, your users and visits and what people are doing on your site. So these are all really useful things that can happen if you have a unified experience, and that can really add value to when you trying to optimize your site and make that experience better where you need to make those changes. And then finally, content gap analysis this is something that, that John mentioned, which is this need for, understanding where you have gaps in your content and being able to do so in more than just looking at keywords. So, with that meal, do you wanna add anything to this report before we jump into the demo? I'm gonna I'll probably show it live. I think so. Alright. Let's do it. Wanna pass it to me. Let's do it. Let me know when you see my screen here. Please see it, Neil. Perfect. Alright. So I'm gonna take you through both a custom customer and agent experience, in a show kind of a bunch of different things, and I go through it fairly quickly. So First of all, this year, what we've got is a speed bit community or, sorry, a speed bit company, and what they do is they they, activity chapters. So this is their web based application. And what I'm gonna actually start with here is our end product experience. So this is how actually bringing that AI and recommendations directly into a web based application. So here I am trying to track my goals, but I have access to help both content that's contextual to where I am that I'm getting started with my device, but Also, I'm able to go ahead and search in the, yeah, search box here and get those free suggestions. I can then choose one of those, which is gonna give me recommended content based on previous users that have searched within this this type of experience. So I can, I can preview this content directly here, inside the, in product experience, and I can also view it directly at the source for the advantage here is, you know, keeping the user in their workflow and have to leave or go to another site or go to Google? I can find all the information directly here. I'm gonna go over to the community and show you, you know, once we land on the community, what here. So this is a Lightning community, a support community for that stupid company. And I'm looking at the healthcare, Nextiva Blaze, so here you'll notice that people like you type, recommendations. So queries that might help me explore a bit better, community posts, And even at the top here, you know, videos, PDF manual for the device, speaker Blaze, So lots of good information. And what I wanna show you here is we have this kind of, demo widget to show. This is the activity that we're talking about. That data that clicks and the information that helps the machine learning learn. But we also know some basic stuff, you know, the the type of browsing that along here location, language. And these are those attributes that help us to say, you know, there's a segment of users that end you know, similar behaviors by that segment of users helps us understand and provide, better personalization. If I go ahead back to the homepage, let's see here, same sort of thing we've got that kind of personalized experience. I would want to search for, in, like, how I tracked my heart rate. And this is gonna take us to that, full personalized, full dynamic search page facets on the left that'll let me drill down tabs for different areas. If I wanna drill into the shop or to the community, content, so it allows me to see all the different types of content that's been unified here. And even, for example, preview content directly in the page. So I can watch this video while again staying on the community engaged by hopping up into the YouTube. So from here, let's say, you know, that, first of all, I'm gonna say, you know, we'll we'll switch into the shoes of Susan here. He's a he's a speeder, easier. And go beyond. So Susan's gonna log in to the community, and that's true. The difference between that kind of people like you versus explicit attributes. So Susan, who we know has had this browsing history and has been searching for things like pairing and the bid and all the different topics that she's interested in, plus the CRM information. So the fact that she is interested in running She owns the speed of delays, and she's a fairly new user. That's where we can start to go Susan that kind of Netflix life experience where we can give her banners of recommendations because of the device, because of the interesting, as you will see, it's interesting running a long post here which you may want to do. So this is that with personalized experience that you can provide for your user, and by understanding their journey and using that data to your advantage. I'm gonna hop over into the case submission process. And this is what we've done historically I mentioned around, around case deflection. And so I'm not gonna show this. What I wanna do is hop over into that case assist experience that Von was talking about. So here I've got, our case assist type new case submission process. And I'm gonna say I have an issue issue. Let's submit release. So I'm gonna be kind of up to there. And not continue there. And here, I'm gonna just go ahead and paste in a huge wall of text with the scarify issue. And so here's where we're actually looking at that content. So it's huge, while we're text, and we're sending that to machine learning. And it's coming back. And instead of me as a user having to select from a drop down of products, it's actually suggested that skew it lays as the product. And then I can choose the devices not responding or any of these other highly competent categorizations. So that allows me to, to get that keeps categorized properly while still making sure that I've entered all the correct information. And again, you know, going beyond this, you can continue to provide that kind of deflection experience, to be able to serve content. But what we're really hearing more and more is just let me sit with my case as quickly as possible, and don't make a huge jump through three weeks. Quickly cover the agent experience, and then I'll hop into the analytics for a moment because I know John had some really interesting topics around content and analysis. I'm buying a pretty good, example of kinda highlighting the the important parts. This is our insight panel on the right here. And again, case details are here. So we're just pulling that information in. We're able to see not only the recommended content, as well as the continent and viewed by the customers. So I'm I'm not gonna tell students about this document. I might even just say, or, you know, I see you viewed this this, web help about tracking your heart rate on your device. Did you have any questions? Did you did you wanna go through together? But it allows me to speak to her as as I understand the journey that she's been through. And, of course, I can easily send content to her through email, post it into chatter, at least have those quick actions available to me here as well. And that full user actions history is a quick click away, or I can see all the clicks, all the queries, the full timeline of activity. So really powerful stuff. We keep doubling down on that, and we'll see more and more innovation coming there. The last thing I wanted to cover really quickly is analytics. So Bonnie touched on this a bit. Really, again, this is where we're able to look and under stand isn't machine learning learning? Is it is it giving the right outcomes? And how can I track that? Can I see that, you know, over time machine learning is starting to recommend more and more relevant content I can see the activity across all our different search pages and the cost of support and deflection but also drill into things like understanding deflection and and sort of like where you know, where deflection is is performing well and where it's not? So deflection by this specific country or bias specific product. I can drill in and see that, you know, that that specific product actually has, you know, a spike in, in, cases and suddenly we need to put some effort on that. So this this is the kind of ways that you can use custom dimensions to be able to better understand those metrics and drill in versus just saying deflection or not deflection And maybe one last thing also, is just around, keywords. I know John mentioned around, you know, looking at huge lists of the queries and try to say, okay. You know, what, what are people searching for? And it's not returning results. Well, you've got an example here. We've got three of those keywords, and you can see visually you know, where the the bulk of queries are with results. So I can actually click on, let's say, track, which is gonna allow me to drill down this report just a little bit more to be able to see not just the queries, and not just the keywords, but the specific types of queries where track it's consistently across. This allows me to look at a group of queries that are around a specific topic, and that'll allow me to do a better job of looking at my knowledge base and trying to address those those numbers. I know that was a super quick going through, I'll pass Zach to Bonnie and see if there's any questions. Yeah. Thanks, Neil. And and I think that basically sums up what we wanted to share today to kind of just give you ready to take questions. Okay. Thanks so much, guys. I'm going to just jump right in since we're getting close on time. And we had quite a few questions about integration So what are the typical service platforms that Coveo integrates with and then more specifically does the agent dashboard plug in into Salesforce or ServiceNow? So I guess I'm gonna take that one. Yeah. Absolutely. Yeah. For sure. So, yes, we have integrations into Salesforce into service now with Zendesk, you have integration, for formal, actually be agnostic. So we kind of have all the bases covered. And plus if you think of something like cases is, it's APIs that you can plug into really anything anywhere. So, absolutely. Yes. Okay. Let's ask one more question. They said great use case examples. Can you share what kind of outcome results you have been able to achieve or observe? For example, contact deflection rates of, you know, percentage over a year period? Yeah. Absolutely. Actually, I have a few stats to share from a couple of TSIA members. We had informatica who did a hundred and twenty percent case deflection I can't remember the specific time range for that one. But I know we we do have numbers around So Palo Alto shared that they saved one point six million in two years. Tableau is another customer of ours, and they they that they were saving one point five million per month. And so it really depends on the organization, and we are getting various numbers. But We see some great results when it comes to to case deflection and NPS and things like that. And I think what's important too is to remember that deflection is, is not just on the deflection, you know, the case submission page. That it really self-service success. You need to look at that whole journey and understand that Yep. You're deflecting with all of the content on your your support community. Yeah. Exactly. Okay. Well, thank you all so much for, the webinar today. We have come to the end today's event, but don't worry. I know we have a few questions out there. We weren't able to answer live. We will make sure to follow-up with you, privately. Now just a few reminders before we sign off for today, there will be an exit survey at the end of today's webinar. Please take a few minutes to provide your feedback on the content and your experience by filling out that brief survey, and a link to the recorded version of today's webinar will be sent out within the next twenty four hours. I'd now like to take this time to thank our presenters, John Bonnie and Neil for delivering an outstanding session, and thank you to everyone for taking the time out of your busy schedules to join us for today's live webinar how digital leaders are transforming their customer service with AA. Brought to you by Technology and Services Industry Association and sponsored by Coveo. We look forward to seeing you at our next TSIA webinar. Take care, everyone.