Good morning, good afternoon, and good evening. Depending upon what part of the world you're in, we're glad you're here. We've got a great discussion, keyed up today, really focused around how we're bringing data and analytics and machine learning into the world of insurance distribution. And we're going to share with you a number of use cases, and examples of where that's being used, how it's being used, and most importantly, how to start to build that capability within your organization. This is part of our innovation trailblazer webinar series, and you can find more about this at s v I accelerator dot com on our events page. I encourage you to get there. You'll see lots of past webinars and lots of future webinars. And after this is done, you'll also be able to direct your peers, to come find this webinar there as well. So we encourage you to get there. There's a lot of great information there. Today, our job is really to provide you some actionable insight and information that helps you basically move this capability forward within your organization. So we'll talk about some of the trends, that we're seeing out there, what people are doing to improve that agent broker experience, use cases, surrounding that, and then get down into what does it take to implement this. And then finally, we'll wrap up with talking about, you know, how do you get started and how do you present a get a biz a solid business case that helps you move your organization from talking about this to actually doing it. So a lot of great information there. Before we jump in, I wanna thank our sponsors. Exceedence, is our platinum sponsor today, and then also, Coveo, who is our silver sponsor today. There's a lot of great information on both these organizations' websites, that can help you basically dive deeper into this topic. So please do make, a trip there if you can. And, now what I'd like to do is I'll introduce myself very quickly and then have our panelists introduce themselves. So I'm Mike Connor. I'm cofounder and CEO of Silicon Valley Insurance Accelerator. And, my job is to help, the industry make the connections it takes to move itself forward from a digital standpoint and from, an innovation standpoint that really create incremental value for everybody starting with the customer. So, with that, I'm gonna stop sharing my screen. And, Travis, if you could start, with your introduction. Sure. Thanks, Mike. So my name is Travis McMillan. I'm the chief business officer at Exceedence. Just by way of background, I spent about thirty years in the insurance industry starting in the early eighties as an underwriter, and and progressed through that, all the way up to where I actually ran a a couple of different insurance companies. And so today, it exceeds you know, we focus on, you know, all areas of insurance, you know, across the ecosystem as a managed services consultancy and technology company. So, obviously, the the whole topic around, you know, artificial intelligence, natural language processing, data, all of that is is very near and dear to our heart. And and today, hopefully, we'll be able to demonstrate not only, you know, my depth in this area, the depth that Xcedence brings in this area. And I would encourage all of you to, you know, take a look and and learn more about our company, as there's plenty of ways that we can help to enable you, in your efforts, as you look to evolve as the industry evolves. Great. Thank you for asking. Alright. Bonnie. Hello. I'm Bonnie Chase. I'm director of product marketing for Coveo. You know, most of my experience has been in in tech and and fintech. But, you know, at Coveo, we use applied AI to deliver relevant experiences and all digital interactions. And so this is through search and recommendations, so that we can offer those personalized service workplace and commerce experiences. So, you know, we integrate with existing platforms so that you can provide that seamless end to end customer and employee experience. And so that's really the the angle that I'm taking in this conversation is, you know, really focusing on that improving, you know, how we can how we can improve that customer relationship, help employees be more proficient in their roles, and really, you know, take it take that next level of of insurance and make that experience great. So I'm excited about the conversation today. Awesome. Thank you, guys. Looking forward to the conversation. Alright. So I'm gonna start sharing my screen again, and I'll take us through a brief setup, and then we'll dive into a dialogue between us. So, let me get started here. So just as a setup, you know, bottom line is there's a lot of forces that are changing insurance distribution today. We've we've seen a big upsurgence or a a a surge of, digitally powered intermediaries, that are out there. As a result of, COVID, customers' digital expectations have just grown. They really do expect to do most of their business with us, online, and they also expect to be able to tap into expertise when they need it. The other, thing that we're seeing is an increase in personalization. How do we basically work with people not as, you know, a ZIP code or as a, you know, a type of business, but how do we work with the individual and or the business on a very personal level, and how do we engage them and support them that way? And that requires data and analytics to do that. The other thing that the industry has discovered is that, you know, outside of, maybe Europe, here in the US in particular, it's been really hard to scale, direct businesses. And what we're also learning is we're going to continue to need the expertise and guidance and support that broker agent channel, can provide. Not to say that they don't have to up their digital game, and they actually have during the pandemic, but the net is, we really need to bridge that personal and digital gap, and they're kind of a core part of that. The other thing that we've seen is digital acceleration, not only, you know, within insurance, but across all industries. So people becoming more reliant on the ability to work with digital, capabilities. And what we've also seen is, a pretty significant maturation of a lot of the technologies we use as we put them to more use. So then that is, you know, it it's it's a digital world, and it's going to take, you know you know, kind of next generation digital capabilities to stay competitive and relevant within it. To sum it up, you know, what do insurance customers want from agents and brokers? It's pretty simple. They want them to be proactive in taking care of them. They want them to be quick in getting their needs met and their questions answered, And they want those brokers and agents to know a heck of a lot more about what their needs are and how to address them and what the options are to do that than they do. And they in particularly in this digital world we in we're in today, they expect the agent and brokers to offer those capabilities, as well. And that obviously puts weight back on carriers to make sure that that information is available to them, so that they can do that. The bottom line is what we're really focused on, is kind of outcomes. How do we help customers better achieve, the goals that they have and the outcomes they have that are important to them, whether it's on the personal side of things and or the business side of things. And we need information and insight about those customers to be able to help, address those. We're also seeing that we now have multiple generations out there who have different values, and different needs and want different things out of, basically the carriers and the brokers, that support them in the insurance, offerings that that they use. And so, when we talk about using data and analytics, one of the things we need to do is basically be able to help, make the journey, through this easier for these different, segments, of the marketplace. We're also moving very quickly now from a product centric approach, to a customer centric approach. And what that means is we need to basically be able to understand, where they are in their life or their business and what they need at a particular time as they move another location. I'm expanding my business, or I'm buying a home, or I'm, you know, having a new, baby or now I have to take care of my parents, etcetera, etcetera. You know, for those triggering events, through awareness that they need something, through discovery about how do I get this, addressed, to getting advice, etcetera, etcetera, all the way through this journey. And the bottom line is we now have the tools to be able to understand where the customer is on that and bring them the right content, the right information, and the right connections at the right time. Again, that takes data, and that takes analytics and machine learning. We're also seeing an expansion of distribution options. And what that places on the carriers is basically a requirement to know how to optimize their strategy across these multiple channels today, and to basically make sure that the right information is getting to the right, organization at the right time, and also so that we're basically setting them up with products that they can be successful with. From, you know, bringing intelligence into distribution, there's a whole list of capabilities that we're now seeing, people embrace all the way from how do I, identify, qualify, recruit, train, and manage and optimize, my channel and broker and agent relationships. How do I make sure that, that they have insight into the opportunities I'm looking for, the capacity and appetite that I have for that, and the customers that that I think will match what I'm looking for to helping, you know, Barca, the the products and, help the agent brokers market their capabilities to customer engagement, to, you know, the qualification customization request for quote recode for quote and underwriting side of things and down into policy, management, and renewal, etcetera, and claims. So the bottom line is there's a tremendous opportunity to bring knowledge and insight through data and analytics into all of these areas, and the market is moving on this quite quite aggressively. So when we look at digitally, empowering, distribution channels, we have to think about that entire spectrum of stakeholders. We have to look at the entire, basically, customer journey. And then we have to look at the the capabilities that we need, and the systems that we need to support all of that. And so that's what we'll be talking about more today. There's also another thing that's kind of starting to emerge, which is a concept called open insurance. So think open banking where customers basically permission organizations to use their data. And I think we're seeing some upticks in that trend. What that gives is insight that will help us better understand that customer, better understand their needs, and make it easier for us to connect them to the right capabilities, the right services, the right products, the right, people that they need to basically undertake, the things that they're trying to undertake. So, ultimately, objective is how do we make this easy? How do we make it seamless? How do we make it compliant? How do we make it prescriptive and proactive? And how do we basically leverage all these distribution channels we have all the way from, you know, embedded insurance to the devices in our home to, intelligent, you know, transportation devices, etcetera, etcetera. There's all kinds of ways for us to gather knowledge and deliver knowledge and connect people to the things that they need to do. And today's systems need to basically be able to do that. So that's a lot. Appreciate you you hanging in there with me on that. But now the question is back to our panelists here. Alright. That's all nice, Mike, but how in the heck do we do that? So I'm gonna stop sharing my screen. And, you know, let's just start maybe with you, Travis, and talk about you know, I I put out some trends and other stuff and and some capabilities out there. What are you seeing from the customers that you're working on, and and how do those trends relate to them? Yeah. Great, great question. I think, Mike, when, you know, when we look out of the industry and we deal with with clients all over the globe, you know, clearly, I think that, you know, there's a huge push on right now to harness, you know, what I'd call, you know, the the superhighway here. Right? Data is king, and it always has been within insurers. Right? So whether you're an insurance company, a reinsurer, an MGA, MGU, an agent, or a broker, it's all about the data. And I think the technology has evolved enough, and the industry has started to harness that technology to then be able to utilize it, you know, to drive different outcomes. And and when you couple that by and if we, you know, if you were to think back to the slide you showed about all the different, genders, you know, that are up there, we have the Gen z's, millennials, you know, etcetera, you know, and you overlay that to the personas of the buyers that are evolving. Their demands are different, and I think people are starting to recognize that. So, you know, I take myself, you know, as a baby boomer. And, you know, my expectations on buying insurance is much different than my children who are in their twenties, right, as a millennial or Gen z. What they want and what they demand is completely different. And so, you know, all of this is really driving, I think, the change that the industry is right now experiencing. Great. Thank you. And and, Bonnie, before I ask you to jump in here, I'm remiss in that, I'd really like to encourage the audience to ask us questions throughout this. That way we can kind of tailor the answers that, are you know, tailor the discussion, to the things that are most important to you. So there's a q and a capability here. Please use it. We'd love to hear from you. So, Bonnie, your thoughts? Yeah. Yeah. I mean, you know, like you said, Mike, the the shift to con customer centricity has happened. You know, it used to be about bringing customers into into our experience, but now it's really about how can we be where our customers are. And the more effortless and seamless that we can make that experience, the better. You know, and to Travis's point, the advancements of technology, the focus that that we all have on digital and the more access that we have to data, I think there really is that need to tap into to AI to make use of those data points and and to help optimize those channels. Because the key here and and what I'm seeing as as companies are rolling out, you know, different ways of interacting with customers, whether they're trying to put out a self-service portal so that they can help themselves rather than than calling the agent or, you know, establishing a chatbot or whatever that that may be. You know, it's more than just making those channels available. They really need to be connected to create that seamless experience and feed into feedback into the optimization strategy. And so I think that's the phase that we're at now where, you know, before it was getting those systems in place. Now we have a lot of systems. We have a lot of tech. We have a lot of data. Now we just gotta put it to work, and put it to work together. Great. Thank you. If I could just if I could just add on to, you know, something that Bonnie said that just it it kind of resonates really, really interestingly with a use case that that we have as a company. So so we had a client that had one channel of communication with their clients. Right? So it was a it was a b to c relationship, and that was via phone. Right? But over the course of the, you know, fifty years they've been in business, their portfolio of of of, you know, the buyer had changed. And all of a sudden, they recognized that, you know, all of a sudden, they were losing connection with their clients. And the reason they were losing connection with their client is because they only had one channel of connection with them, which is via phone. So they needed to bring in that whole chatbot experience for them because, you know, these younger generations, they want instant gratification. And they wanna do it on their phone. Right? So they're sitting there like this. They wanna send out. And so the engagement of a chatbot and being able to institute that and harness the data that they had, you know, to to leverage that both in a self serve environment and that that staffed with agents, you know, created a whole another avenue for them, which, you know, drove a different customer experience, which, you know, from a satisfaction scale, took them from, you know, where they were middle of the road to all of a sudden their high end. Right? So that's it's a really important dynamic. Yeah. Thank you. And, so I I so, yeah, let's so what I wanted to do is, is step into this, you know, what are you seeing in terms of improving the agent broker experience? I think one of the things that we're, you know, we're that I'm hearing and and seeing is that, agent and brokers are basically starting to come to the same establish the same requirements that end customers are. You know, they're tired of, the paper chase. They're tired of, you know, having to go back and forth umpteen times between the customer, between the underwriter, etcetera, etcetera. And, you know, their customers are are demanding that they, basically work with them in a more effective manner. And so what we're learning is, you know, speed right now counts. Accuracy right now counts. Reducing frustration and friction counts. And so what are you seeing in terms of the work that's being done to bring data and analytics into that process of improving the effectiveness of that broking experience, both for the end customer as as well as for the broker? Yeah. So I think it's a great it's a it's a great question, and I think a great area for us to to talk about. And I'd wanna relate it to this question, Mike, if we can, that Jeffrey just posted, because I think it's very relevant. Right? So when we think about you know, his question is, you know, too often, the latest technologies are being targeted towards enterprise organizations to achieve scale. I would agree with that. The challenge is that, you know, how do you aggregate these technologies to make them available for, you know, small, to medium sized businesses to leverage, and the independent agent market, you know, is the most exposed and endangered from getting left behind. Completely agree with that. Great great question. Yeah. Absolutely. Right? Very relevant for this discussion. Couple things. You know, there's some different carriers out there. You have carriers that are, you know, obviously direct writers, and then you have carriers that clearly support the independent agent and broker channel, and they're not gonna change from that. You know, we deal with, you know, a couple hundred clients, insurers mainly, you know, and and there's you see two paths there. The ones that truly are supporters of agents and brokers, and and there's a lot of them out there, are harnessing tools to make it easier for agents and brokers to attract customers. And and I think that, you know, one of those things, you you can you can think about it, you know, in a couple of different vantage points, but one would be, you you know, one of the most frustrating points for an agents and and broker is that they go through the whole, you know, application and business placement process with a carrier. They get to the end, and it says, oh, now you have to refer to the underwriter. It goes to the underwriter. As soon as that underwriter gets it, you know what they're doing? They're hitting that decline button. Because they knew on the first question that that that a client, you know, that client wasn't eligible. So how do you bring that full facing down to the agent where they know that this client is gonna fit day one. Right? And it's really about harnessing, you know, all of the information that's out there and making sure that you're positioning that through the utilization of these tools, in in the pursuit of that new business for the agent and broker. So when they come with that opportunity, they already know it's gonna fit. Right? And and in fact, the amount of data that they have to put in is is so small. It's one or two pieces of information, and they get the answer because I can gather the rest of it, you know, going out to various sites and collecting it, because, you know, data and whether it's first party, third party, you know, and the use of, you know, all of these different technologies like AI, you know, allows that to happen. And I'm sure, Bonnie, you could probably chime in and and offer some more around that, from a profile perspective. Yeah. I mean, the the the agents have a lot of pressure to get it right. And, you know, as you were sharing your example, it made me think of an example that that I experienced early in my career when I was working with an insurance agency. And we had a we had a client who we had they were an older couple. They were, you know, trying to to get a a big life insurance policy. And it was it was a really, you know, good client. Right? We wanted to make sure that they were protected, and it was a large thumb as well. And part of getting them through that cycle was making sure that, you know, the underwriting stuff happened, that they had all of the the requirements met and needed, you know, whether it was you know, they had to do different tests or they had to provide specific documents. And the process ended up taking a while. And one mistake of missing, you know, one test that they needed to go do, they decided not to go forward with the policy because they they were they had already put all this effort in. And, you know, going back to do another thing was just too much for them. And so they didn't do this policy. They didn't have that protection and coverage. And so it was really kind of a a a learning point for me at that at that time where I was like, wow. Like, we really need to make sure we get it right upfront and not wait until it's down the line to make those changes. And so from from a, you know, an an experience perspective, I would say, you know, one of the thing is is reducing that waste of time by making sure that they have the most relevant information at their fingertip. You know? It's relevant to the type of policy that it is. It's relevant to the area that they live in, you know, and so that they have those specific requirements, and they don't have to kind of, you know, filter through a bunch of noise to get the information that they need. You know, and and from a policy management perspective, I would say self-service can really play a key role here. You know, clients can call their agents when they have questions. They can go online. I I think the more that we can empower them to self serve, the better, especially as you said, Travis. You know, we've got this this growing, this growing millennial audience. Right? Millennials are turning forty. We're we're we're making up a large portion of the workforce. And so, you know, it's important to make sure that we're taking all of those things into consideration. You know, one example that I can give is, you know, working with with Manulife to improve their employee experience. You know, their their staff wasn't they weren't, you know, adopting the tools that they had to use to, you know, build those relationships with their clients. And, you know, it was making sure that they they had access to all of that information, you know, at their fingertips so that they didn't have to to have that disjointed experience. And the last thing I'll say about this, is that, you know, as we as we think about customer experience, agent, broker experience, you know, in reality, the the the consumer experience, is the expectations are there regardless of personal or business. And so it's it's we can't overlook that anymore. We can't put all of our focus on customer experience or all of our focus on on, you know, the agent broker experience. It's really, you know, how can we take a holistic look at our banking experience in general? Yep. You know, one one more thing that I would add here is that and and thinking about the agents and brokers specifically is that, you know, for as long as I've been in this industry, it's always been a challenge around ease of use. Right? Ease of use with their carrier partners. And and, you know, agents are in a tough spot because, let's say, they they represent five or six different small to mid market carriers. Every one of them has a different underwriting appetite. Every one of them requires different information. And while they all say, quote, I'm a cord standard. Yeah. And that starts off, and then they have a, you know, a three to five page questionnaire that all ask questions, but there's one little nuance that throws it off. So when, you know, you think about, you know, how technology has evolved, how can we harness that to help them? Right? So, really, AI in in both structured and unstructured data can play a huge role in that because you can take, you know, really an unlimited amount of a dataset, all asking the same question using different words, and and you can get technology to understand that. And then take that one answer and place it into all the right places. Right? So what does that do? It makes, you know, a a CSR and account execs role easier so they can do, you know, spend more time actually on meaningful things in the interactions with their clients versus, you know, having to do the swivel chair to five different carriers as they're placing a risk or looking for, you know, a carrier to accept a risk based on, you know, some information that they believe, you know, is relevant. And and I'll just share one other what what I'll call use case slash, you know, pain point, that I've seen, and this was back when, you know, I was I was managing a a fairly large underwriting team. We had introduced predictive analytics. And one of the hardest thing when it first came out in the Barca, and it's it's evolved a little bit, but I think there's still difficulties, is that you take two risks that from an external perspective look the same. You know, let's just take something simple like a restaurant. Right? They're on the same block. They serve the same types of food. They're basically the same size. They have the same sales, the same liquor to, you know, to food ratio, etcetera. But from a predictive analytics standpoint, they may score completely different. One risk may be offered. Here's a price. Here are the coverages for that price. The other one gets declined. From an agent's perspective, they don't understand that. From an underwriter perspective, it's very hard to explain that. Right? Because you, you know, you don't understand what's happening under the hood and how, you know, the algorithms are actually driving that outcome. But I think as technologies evolve, we've gotten better at that so that people can understand it at every level. So is it well, while the models start in the in the predict you know, in in the actuarial space with product development, and they funnel down to an underwriter. Underwriter can truly now understand how, you know, risk a and risk b are different, and they can explain that to the agent. But it needs to be pushed further because I think the agent, and so the question that Jeffrey asked really is how do I know that before it even starts? Right? So so there's still more room here to evolve. Yeah. Thank you, guys. And I I see Jeff's got another question that I wanna dig into in just a second here. But and and I think, you know, I wanna maybe expand his question a little bit. You know, what we have is, basically three different stakeholders in this equation. We have the customer. Right? If it's an individual customer, that's pretty straightforward. If it's a business customer, and particularly a complex business customer, that gets a little more challenging, in terms of understanding them and gathering the information, that they need internally to get, the risk address that they they need to get addressed. Then there's the the broker agent. You know? There's obviously the aggregators out there as well. And then there's the underwriter, carrier, etcetera, etcetera. And I think the net is you know, what we're looking at is how do we basically start to bridge these? You know? And, you know, I do see some carriers out there that are looking at how do I bridge that, how do I basically, make it easier by providing third party data, etcetera, etcetera to understand those customers. But, ultimately, we've got three parties in this equation, And bringing this information together across those is a is a challenge. Who's gonna pay for what? Whose system is gonna do what? And I think, the good news is that we're starting to see, you know, APIs and microservices as platforms emerge that will help us address some of that. But, I just kinda wanted to, ask you maybe, Travis, to start. You know, how are you seeing people approach bridging those gaps? Some of some of it's, you know, kind of, you know, a a technology replacement or a technology skinning. Right? So you so you put a front end onto a legacy system. What Carriers have spent, you know, hundreds of millions of dollars on technology, over time, and it's very hard for them to bite the bullet and say, okay. Well, I'm gonna I'm gonna totally scrap what I have and go with something cutting edge because cutting edge technology has really built what I would call very modularly. Right? So it's a plug and play model through, you know, a lot of the things that that you're referencing, Mike. Right? So it's, you know, it's about using those APIs to to plug in, you know, third party, you know, partners to, you know, accentuate, you know, the process. Right? Now while many will struggle with with that total replacement, there's a way to create, you know, a conduit between, you know, cutting edge and and legacy to drive a new process and and a different outcome, you know, for your either your agent broker partners or or for your, you know, specific policyholders. And and I think that, you know, we we've got, you know, quite a few use cases where we've been able to to come in and do that to evolve the process on behalf of, you know, various insurers that are either in a b to b or b to c, type in environment. And if I tie that into, you know, kinda Jeff's question around, you know, third party data to streamline the process, You know, again, it gets at, you know, how can you harness a front end, through the use of of AI to be able to go out and grab information that, you know, somebody doesn't need to, provide. That's one aspect of it. And and, clearly, it exists, and it's relatively easy to do, and then streamline. You know? So over the course of, you know, six months, you could implement, you know, a different front end to a system for a different, you know, experience, whether that's with the customer or whether that's with your agent as the customer. So that that's one. And and I think, you know, the second is that, when when you think about, you know, the whole the whole experience, how can you how can you harness, you know, less is more, right, where where that one or two pieces of data then drives the whole process. And and in addition to making sure that, you know, your your appetite is exposed in the sense of they know exactly what it is that you're looking for. And I think some carriers have taken that to the extreme where, you know, they go out and they profile business. And then even through the agent and broker channel, they then push those leads to the agents and brokers to then drive them back through reverse engineer process, you know, into into their camp because they support that that channel. Gotcha. Thank you. Bonnie, any thoughts from you on this? Yeah. Yeah. And and, you know, to to that point, I was just going to mention that, you know, the more data that we have access to, the more machine learning can learn. Right? Because because that's that's what feeds machine learning. In my world, you know, I'm really looking at that at the interactions from a content perspective. So that means, you know, where are they searching for information, whether that's a chatbot, a portal, whatever that is. So tapping into those existing systems, and then seeing how how people are interacting with that content. What are they searching for? What are they clicking on? What are they reading? And then, you know, based on that information, you know, and their user profile, comparing them to to people like them and seeing, you know well, based on your behavior, there's another person who has the same user profile as you, similar behavior. They found success, you know, with this piece of content, and so we think this would we will recommend this to you as well. And so, you know, from that perspective on the content perspective, I think we're a little bit further there when it comes to, you know, connecting into systems and capturing those data points, and then providing that information. But but, again, you know, that that's that's that's the infer that's the the general information piece. So I would say that's that's my wedge of the conversation. But it's really about, you know, for us, it's, you know, how can we capture that search and and content interaction from across the entire ecosystem so that we can create that, you know, the the necessary changes to the experience. Gotcha. Thank you. And, we've got a a a Michael, I see Michael Clark, I see you put your hand up. Would you mind, putting your question, or your thoughts into the q and a channel so we can address it there? Thanks. Alright. So, while we're waiting for Michael, I think, the next thing I wanted to dig into, and I think we've talked a little bit about this, is just improving the customer experience. You know? So, you know, do you have some specific examples of where we're basically able to gather that customer data, get that, distributed along their journey as they're working through whatever they're doing internally, plus what they're doing with the agent broker and then ultimately how that information gets passed to the carrier. What are we doing to make that that experience you know, what examples can we share about making that experience more seamless? And with the understanding that, you know, there's kind of regulatory oversight about how we share information, etcetera, etcetera. But, do you have some examples of of that? Maybe we'll start with you, Travis. Oh, Travis, you're on mute. I need to get one of those signs. Right? I'm on mute. Yeah. I was just testing the mute button, actually. It does work. Yeah. Absolutely. No. So so I'll give a London based example on that. I I think so we had an insurer in a b to c model, that was a a new Barca up. They came to us, and they wanted us to build the technology platform for them. Right? And what they wanted to do, it was high, you know, high value homeowners. They wanted a different experience, for that clientele. And and what they wanted to do was have one, two, three data points. And and, obviously, I can't get into specifics of what those data points were, but that would honestly kick off the entire underwriting process where then we would go out to various third party data sources, pull that information into the context of the risk, and then have an automated underwriting process to go through, you know, whether the risk met the underwriting guidelines or did not. And if it did, then here were the various offers for coverage and, you know, have it turned back to them in a matter of a minute. And it was you know, it actually was. It is a very successful model. And I think we're starting to see more and more trends like that in the marketplace. You think about, you know, some of these bigger, new evolving Insurtechs like Lemonade, etcetera. You know, they're all kinda cut on that. You know, how can I leverage technology and data to drive a different experience, you know, for the customer, whether that customer is, you know, the policyholder, whether that's the agent and broker that's placing it? And we're seeing this, you know, continuous evolution based on that. Gotcha. Thank you. Bonnie? Yeah. And and I would add to that, you know well, customer experience, obviously, we know is is really important. And, you know, as as the use case example, we were working with a company to really, you know, their goal was to maximize the customer's time and attention so they weren't wasting their time on irrelevant things. And there are a few different areas, you know, where where where we can we can get ahead of that curve. Right? So if they're researching information about, you know, a specific type of policy, for example, and we can provide recommendations based on what others have actually, you know, gone and purchased. They already have a policy, and and, you know, we wanna provide a a recommendation for what they they need to add next. So there's all kinds of ways that that we can improve that experience. But I think the key thing there is to make sure that, you know, we're we're capturing that that data from across the entire ecosystem. And I and I I'll say this again because this is where I'm seeing a lot of the challenge. The systems aren't talking to each other. Departments aren't talking to each other. And all of that can really impact the customer experience. And so, you know, from from the the use case example, you know, we really, implemented, you know, a a strategy to capture that search and and content data across, you know, their community, their website, their chatbot, and use that to, and use the the analytics from that to optimize the experience. Gotcha. Thank you. And then, we've got a question from from Rodrigo. I'm gonna try to parse this down, but, I'll I'll start with his last sentence, which is, you know, does a Shopify for the insurance industry exist? Right? And what he's what he's pointing to is, you know, how do we basically make this end to end experience from gathering information and insight about what the customer needs to basically getting them the recommendation to the options that best meet their needs. You know, is is is there a a platform out there that we could recommend that does that? And so my my answer would be this, which is, I think every key core platform vendor out there is basically trying to bring that capability to life. That said, there are a bunch of different systems and capabilities, that need to be brought together even if you have the most advanced platform in the world to bring this about. Right? You you need, you know, your your your your data environment. You basically need your analytic AI machine learning environment. You need the personalization capabilities and so on. So I don't know of any one platform that does all of that. I don't know if you guys do. I'd love to find out. I would I would I what I I would challenge you a little bit there Okay. In in the sense of while there while there wouldn't be one platform that exists that can do that. Right? Yeah. But but think don't think about it in in just the context of technology because it's more than just technology. You're always gonna need people processing technology no matter how far we evolve, you know, as an industry. And and I think that, you know, if you think about where technology has come from and where it is today, there there are definitely companies that are on the cutting edge of that, where it used to take, let's say, you know, eighteen months to, you know, implement a product. And then all of a sudden, you know, everything's hard coded and, oh, I need to change my rates. I've gotta open up the hood and do all this coding. And then by the time you actually get your rates to market, guess what? The market's already set sail. It's already gone. Now you need to change them again. Right? And so there was always this tail to chase. With with, you know, the admin advent of, you know, configurability rather than customization, and a lot of newer technologies are based on that. And I can, you know, reference our, you know, our sister company, ChainNet, who's our product company. You know, it's it's all drag and drop real time. Right? So rather than take, you know, months to stand up a new product, we're talking about weeks. So there so there are elements of that that exist today. But when you think about, you know, I think to to Rodrigo's point about the Shopify for insurance industry. Right? To me, what that means is, is there a company out there that I can go to that can help me from end to end across everything? Right? And there are what I'll I'll reference is this. So so we have at Exceeds, we have something we call our MGA agility suite. Now while that's focused on MGAs and MGUs and whether you're a start up or an existing, It it's broken into kinda two main buckets. One is technology. The second is services. Right? And we combine those in a module way to say, okay. Well, what does somebody need? They need everything end to end from setting up, you know, their data structure, their workflow, you know, how they're gonna deal with their distribution all the way through to, you know, what their product set is and then what technology they need to enable that, and then what middle and back office support do they need to drive that. Right? So so so there is, to some extent, I think during the evolution, the ability to have that. Yeah. You just you gotta know the right people to ask. And I and I think the the other thing you wanna make sure is that the people have deep, deep insurance acumen because they have to understand at every level what it is that you do and how you do it. Because if you don't have that, you know, basically, you're training a technology company about insurance. People don't have time for that. You know? Fair enough. So that that's what we're seeing. Alright. So, we've got several more questions I wanna get to, and I reckon you see we've got about fifteen minutes left. I wanna talk a little bit about, channel optimization. And so, you know, one of my slides showed all the different channel options that are out there today. And what's really clear is, you know, most insurers are looking for alternative channels to bring, product capability, and knowledge and expertise to market. You know? So whether it's MGA's, MGUs, whether it's the aggregators, whether it's an embedded insurance, partner, etcetera, etcetera. So the question now is, you know, how, you know, what are we seeing out there in terms of how do we optimize this? You know, I was a a channel man. My my my first, job at when I was working at Apple Computer, was basically their being their channel management guy. And what I realized very quickly is I had to be very careful about how I set my channels up, where I set them up, etcetera, etcetera. And in many cases, you know, as we deal with more sophisticated products, we get down to what channels do we distribute, what kind of product through, what kind of channels do you know, and what kind of, knowledge, expertise, and support and training do those channels need so they can be successful helping customers. So what are you seeing in terms of, you know, use cases where we're actually working to understand how to optimize that channel based on knowledge and then making sure that we're getting that kind of information out to them, to support that. So can you share any thoughts about that? Jeff in here. So when I when I think about, you know, analytics being used to optimize that strategy, there are really two questions that come to mind, which is, you know, what do customers want and where are they struggling? So those are two key insights that we can get from analytics is, you know, what are they searching for? What are they reading? What are they trying to find out? What what do they want? Right? And where are they struggling is, you know, they're looking for things and they're not finding it, you know, or they're they're looking for things and we don't have an answer for that. So those are two key areas where I see, you know, we can definitely use analytics to optimize, that strategy. And why this is important, I have a couple of examples of this. You know, we think about technology and and channel strategy and things like that. Chatbots come up a lot. Chatbots have been, you know, kind of the the shiny objects, and people are starting to realize that maybe chat chat bots aren't as great as we thought they might be. Maybe they're not as effective as as we thought they might be. So should we invest in a chatbot? Should we invest in optimizing this chatbot? Should we get rid of it altogether? Well, that's where analytics can really help you understand. Are customers using it? Are they being successful with this channel? And and we've seen customers who who have had success with a chatbot and who haven't. And either way, the right answer is, you know, you're you're providing the information in the channel that the customer prefers. Another example of this is, you know, I was having a conversation with with someone at AARP where they were talking about their their strategy. And and this one the reason I'm bringing this one up is because it's it's so interesting. You know, they still have a lot of mail in, inquiries. People aren't not everyone is going online to get support. People are handwriting letters and sending that into them. They still get a large volume of handwritten letters to their to their comp their organization, so they still need to be able to support that as a channel. So that's just an example of, you know, why analytics is so important in in, you know, in how you're optimizing so that you make the right decisions on on where you spend the energy, effort, and money. Thank you. Travis? Yeah. I I think I think the other thing is is when you think about, multi channel insurers as an example, you know, one of the one of the main pain points that they've had is visibility across their multiple silos. Many of them are structured that way, you know, and it it's been that way for a long time. So, you know, creating a bridge so that you get visibility across a client that, you know, maybe in, let's say, middle market, maybe in personal lines, may also be in your life and health division, things like that, allows you then to harness that information and then create, you know, upsell opportunities for rounding. It also helps with your retention because if you know that let's say you've got a a multimillion dollar client in, you know, your middle market segment and you're gonna nonrenew the owner in your personal line segment, that could become problematic because you may wanna move move his business from you. Right? And so, you know, what we're seeing a lot of is, you know, being a Salesforce shop is that, you know, Salesforce as a CRM can actually can bridge those gaps. Right? And then you can bolt on to that, you know, different AIs to create that experience and and drive the necessary outcomes. So we're seeing that, and that and that's really creating a lot of efficiencies, for, you know, revenue growth and also for expense savings because, you know, it saves time through the process that you would normally have to go through to validate, well, is this person over here? Are they over there? You know, things like that. Gotcha. Alright. So, we're getting down to the top or up to the top of the hour here. I wanna dive into, another question and then kinda we'll end with, like, how do we present this the business case. So I I wanna talk have you guys share a little bit about, you know, what does it take to actually do this from a data and a system standpoint? Where do you start? You know? Obviously, we we most companies have got information siloed all over the place. You know? We know people are bringing that information together. We know people are trying to API stuff together. But, and, Travis, you know, where do you start when you go into a customer organization that's trying to solve some of these problems? How do you help, them put together a picture of where they're at versus what they need? Yeah. No. So, I mean, it starts really at at a fundamental level, and and this may seem really simple, but it's, you know, it's understanding, you know, what does their data look like? How is it structured? Is it unstructured? Is it structured? In a lot of cases, you find just a hodgepodge of information. Right? And so it starts there. In order to harness information, you know, first, it has to be in a structured way because then it becomes actionable. So so, really, the first step is understanding, you know, where is the data? Can you grab the data? And then can you take it from both the structured environment and the unstructured environment and put it into a structure, which then can start to drive how you wanna use that data through the various tools. Right? So that's one. The second part is is understanding, you know, what are their pain points, and and and what are they looking to change as an outcome. Right? And when you understand that, then you can overlay, you know, the availability of information that's there to try, you know, to then start to, you know, determine how you can optimize their process through the leveraging of that data to change and overcome those obstacles that are in front of them, and the you know, and their pain points and ultimately reach, you know, that optimization level. The other thing and and this will probably tie into to kinda your last point about, you know, how do we get this done? In in my experience and the experience of, you know, of the company that that, you know, that I work for at Xcedence, you know, one of the things that we always try to instill in our clients is, look, what you don't wanna do is go in and say, look. I'm gonna change from a to z, and I'm gonna do it all with one big bang. If you do that, you're looking to fail, honestly. Right? You'll you'll end up with a runaway project. You'll spend millions, and you'll never get to the endpoint. Most important thing is is to put together, you know, a mini use case. And and if you don't prove it out, if you fail, and you may, then, you know, you adjust and you go back at it. And you keep doing that until you get it right. And once you get it right, then you build upon it. Right? Because you wanna have that success factor. But it also allows you to measure what's gonna be the impact so that, you know, if you start off with a small pilot and you spend a little bit of money and a little bit of time, and I'm talking about, you know, a month to to get to a use case that then validates, you know, your hypothesis, then you can build out a substantial business case with a runway that says, look. Here's how we're gonna do it over the course of the next twelve months, eighteen months, and very modularly. So that that way, if you hit an inflection where it wasn't successful, you can then retune and then move it forward. And that's critical. Yeah. Thank you. Yeah. And I, you know, the other thing I wanted to bring into this discussion is that bottom line is our tools and technologies and methodologies change dramatically. Right? And so the net is you can and you just said it. You know, we can do things in days and weeks that probably would take us months and years before, to do. And so the net is you reduce the risk, you reduce the time it takes to validate and or to pivot if you need to. And and so the investment is far less. And I I love what you said about, you know, I'll kinda capitalize. Small is beautiful. Right? That's what actually gets things done, in an organization, not the big well, you know, we've learned that so many times. The big bang things just don't work well today. So, Bonnie, quickly on your part, any thoughts about where you start in terms of personalizing, gathering information, etcetera? What do they need to have in place to do that? Yeah. I mean, you know, it what I would say would really align with what Travis said, but, but kind of taking it at a higher level, you know, really understanding what your goals are and making that very clear, and having that tied to a business objective can really make you know, as you as you plan your your short term, you know, checkpoints, that and the hope is that those checkpoints would lead up to that bigger goal of that, you know, better customer experience. I would say, you know, making sure that that you're thinking about how you will manage change. There's a lot of communication that needs to happen. There's a lot of different, you know, cross functional teams that need to get involved, so that's something that's important to consider. And then, you know, making sure that you think about it in a way that you're iterating. So that's the you know, we're not going after that big bang. We're going after small improvements that each create that bigger, that bigger, better experience. Great. Thank you. So, then let's kind of, drive into the last question here, which is how do you put together a business case for this? Right? What you know, in in today's world, we're moving really fast. What we know is speed to value is incredibly important, and speed to proof point, I think, is incredibly important for executives. I think the good news is that most carriers, most brokers have realized that the investments they've been making in, technology have really paid off particularly during the pandemic. So I think the audience is a little more receptive to this. But still, we're talking about bringing a lot of technology together, talking about pulling data together. We're talking about basically making sure that we're capturing information, at the right point, that we've got attributed that in a way that we can do something with it. And we're also talking about bringing in analytics, artificial intelligence, etcetera, etcetera. So there's a lot that needs to get done. How do we make the business case, to get started on this? So, Bonnie, maybe we'll start with you. I mean Yeah. What are you seeing in terms of return? Like, what actually happens when we do this? Yeah. So I would say, you know, this goes back to that, that high level business objective. Right? It has to tie to value from the top. And, you know, what what we've seen is is, you know, if if if you're making an investment in technology and you have that executive sponsor backing you and you have, you know, that high level business objective that you're trying to achieve. So let's say, you know, if we're trying to improve the customer experience, what are some ways that that we measure that? You know? Are we looking at CSAT? Are we looking at MPS? Are we looking at, you know, is it just, you know, engagement? You know, what are those things that are that are important? So identifying, you know, that that high level value that you're that you're trying to make an impact on, and then tying it to those specific KPIs that feed into that value. I think that's that's one way to to take a first step at developing that business case. Great. Thanks. Travis? I I mean, I think it's similar, and I think I kind of already spoke to it a little bit. I I think, you know, what you wanna do is understand, you know, kind of the the challenges that that they're facing, what their strategic direction is. You wanna relate the two of those. Right? And and then you wanna frame it out with a hypothesis that says, look, you know, here's here's what I'm gonna drive. I think one of the important things is we're talking about technology and all these different, you know, digital, you know, tools to bring them together. What's also important is that, you know, companies that are gonna embark on this that haven't already, or maybe they have and they failed, you know, those those create two different challenges. When you haven't done it before, you don't have the insights to what's out there, what's the best to harness, what's the best to mash together. So you need a partner that's what I would call agnostic to technology. Right? They work with everybody, and they have a view about that. So that would be critical to the to to kind of building out what that business case is. If if you've already gone down the path or you've tried something and you failed, you know, then then you have to approach it differently within your company. Because to get more traction, to be able to get more funding, to be able to step off and and get, you know, an executive committee to agree to, you know, trying to overcome these challenges again, you know, is gonna be more difficult. And so then I would go back to saying, make sure that you start off small. Start off with a a very small use case and pilot that can prove that, yes, something like this can be done. Now I think about, like, for example, when, you know, robotics was the was the big buzz. Right? Many people try to implement robotics in in the insurance process, and the reason why they failed is because the robotics companies, unfortunately, didn't understand the process of insurance. Right? And and the insurance people didn't understand robotics, and that's why it was such a long time to adopt use cases that could truly drive change. You know, if you take a company like Xcedents where we're agnostic to robotic technology, we're agnostic to all technologies. We work with, you know, many, if not all. You know? And because of, you know, a deep acumen around insurance and we understand process from end to end, we know where you can apply robotics and how you can optimize that, you know, for the benefit, you know? And so, you know, it it's just it's it's it's a mix up of a bunch of things, but I think that the best thing, honestly, at the end of the day is to start small and have a contract concrete vision on, you know, if we do this, this should be the outcome, and then make sure you drive it to that. Yeah. Thank you. So, basically, we're pretty much out of time. So I wanna thank you guys both. Great great insights, great discussion. Appreciate, the support, for the work we're doing, and I also wanna say thank you, to the audience for being here. You know? We really appreciate the work that you're doing to move the industry forward in this. So let me, share my screen real quickly, and we'll do a wrap up. So, this is part of our innovation trailblazer series, as we said originally. You know, please check it out on our website. Lots of great stuff up there. Thank you again, to our sponsors, Exceedence and Coveo. Please do get to their website. There's a lot of great information and insight there. Thank you to our panelists, and, also thank you to the people from the audience that were asking questions. We really appreciate that. And then, you know, if you're interested in connecting with anybody, please reach out to LinkedIn. Very easy to get us there. And with that, I'd like to say thank you very much. Appreciate your being here, and that's a wrap. Cheers. Thanks. Thank you.