 All right, welcome everyone. Thank you very much for joining the webinar about boosting user adoption hosted by Logi Analytics. Today we'll be speaking with Seth Hutchison, Product Manager at Logi Analytics. Seth has actually been working in the business intelligence space for about six years. And before Logi, Seth also held different positions in the engineering and product team at Isenda. And moderating this webinar will be myself, Mia Nguyen, Product Marketing Manager here at Logi. In this webinar, we'll talk about, first of all, three steps you can increase part of adoption with data analytics. And then four key capabilities of embedded analytics that can help you drive user adoption. We'll also talk a little bit about Logi Composer, which is an embedded analytics solution that can help you deliver more value to end users and increase your application usage. But before we talk about all that, actually I just want to take a step back and talk a little bit about why you need to increase user adoption and why with embedded analytics. First of all, acquiring a new customer is expensive. You know, it can be anywhere from five to 25 times as expensive as retaining an existing one, depending on your industry. And also as a product manager or developer, you just don't want your hard work to be wasted building an application that your users don't use as much. The second reason is that analytics is an indispensable tool to help people make informed decisions. So what you're looking at right now is some results from the 2021 Hangover Survey. We can see that more than 80% of users across all the survey industry consider analytics to be very or extremely significant to their current roles. And the third key fact here is that if an analytic solution was embedded, up to 77% of knowledge workers would use it. So what does this mean? If your goal is to increase user adoption or even maintain the current user engagement, we need to make your application more sticky by embedding analytics right into your application, which your users already know, and right where their decision is needed so that changes minimize and value is maximized. Next, Seth will actually talk in detail about how you can master user adoption with analytics over to you, Seth. Thank you, Mia. Good morning, good afternoon, and good evening to everyone joining us today. As Mia mentioned, my name is Seth Hutchison, and currently I'm a product manager with Lodgy Analytics. I've been in the business intelligence sector for the past six years, operating as originally an engineering resource working on integrations before switching into a product role. However, during my time in the business intelligence space, I've had a very unique opportunity to be solely focused on the concept of embedded analytics. So while my teams and I worked to build out our platform and feature set, we began to really notice a common trend between many of the customers that we would work with. That problem and trend that we saw was that our customers' teams would often struggle to increase user adoption because they were too focused on the transactional level of their application. Now, what I really mean by this statement is that oftentimes our applications can focus too intensely on our stop and go operations. These scenarios where a user logs in, completes their tasks, moves on to another one, or onto a different application. And as we began working through integrations with those clients, their own users began to truly see value in the expansion upon their workflows that already existed within their end user's day-to-day interactions with their own software. So for today, as we think about ways to increase user adoption in any given platform, let's start with talking about why embedded analytics is an easy path to take. At a high level, the value of any given data entry transaction is inherently low. For example, very few people enjoy or probably derive deep insight from entering data as part of their daily routine. For example, looking at Salesforce, you don't really gain anything from inputting information about your lead. It's only once you begin to review that data that you actually pull insight from what's within the system. So building on that, to truly give our users the ability to drive deeper, more meaningful information from our applications, we need to empower them to leverage their data as often and efficiently as possible. Though I encourage each of you out there to focus on where your application currently struggles in regards to analytics. As you consider where those struggles lie, try to envision how taking these steps towards an embedded analytics solution might address those problems. And while you do, I wanna talk to you about three main steps that you need to take within your applications to create analytic-driven user adoption. Now each of these steps is going to build off of the last and serve to give your users a much deeper experience and increase the day-to-day value of your applications immensely. The first step that any application has to take when providing your users with analytics is, well, provide them with built-in analytics. This is a natural starting point for a modern application when we consider any generic analytics solution. Now, this is typically achieved by providing some form of bolt-on analytics or various UI controls to help facilitate analysis. If you and your teams haven't already thought about this process, you might find yourself asking, why is this necessary? And what benefit does this add to my application or my user base? Well, in today's world, data is being collected at an unprecedented rate. Almost every facet of an application can be logged and reviewed and every transaction within an app can be stored or referenced. Your end users must be able to leverage that amount of data so that they can extract insights and make data-driven decisions. Simply having the data at your disposal isn't enough anymore. You have to allow users to access and analyze it to allow for these strategic data-driven decisions to be made within the organization. As we see this trend continue to grow, many enterprise-level application providers are competing on the level of advanced analytics they provide. In fact, it's more than just base functionality. You can win or lose deals based on your own application's analytic portfolio. But why do we actually need to provide analytics in our application? Well, for starters, this trend isn't new. In fact, for years, consumers have slowly begun to search for ways to apply their data and to learn from it. Even as far back as 2017, we saw at the time roughly 67% of business users already switched to separate analytic tools to gain some form of insight or analysis. And as you can assume, this trend has only continued as more users continue to look for easy to use access forms of analytics. However, having to perform these actions in a separate application creates an inherent disconnect in their workflows and they feel it interrupts their productivity. And as we've seen in the past surveys, about 83% of business professionals expressed a desire to stay in one application and considering an analytic solution. So by adding a truly embedded solution, you're going to not only increase your application's feature set, but you'll help mitigate a growing frustration within your user base. If you ignore this desire, it's only going to further distance yourself from your users. And because this frustration continues to grow, companies can no longer treat the demand for analytics as a standalone project. In order to truly thrive, your analytics needs to be pervasively integrated into the whole of your customer journey, their operations, your products and your services. There's an unstoppable drive from consumers to get real world insight into the information wherever they go and your app has to be able to keep up with that demand. So in summary, placing analytics in your application is going to limit your user's frustration with disjointed workflows. It's also going to increase the time that they spend within your application itself. And as we all can appreciate, it's going to help differentiate yourself from your competitors when it comes to closing a deal. Now, for the second step, we're having a truly embedded analytics solution in your application. Let's assume that you've already begun to implement some form of lower level analytics and your users have begun to respond positively to the canned reports that you've been providing. The second step that you need to take towards driving adoption with analytics is you need to contextualize that information that your users are going to encounter. But how does embedded analytics address this? Well, in order to talk about that, I want to take a step back for just a moment to help outline specifically what I mean when I say embedded analytics. Now, embedded analytics is a term that's often thrown around whenever the concept of data analysis or business intelligence is brought up. But what is it really at its core? Embedded analytics at a high level is the integration of business intelligence tools. Excuse me, specifically the generated analytic content within the business process applications that we're already offering. So basically it's the natural evolution of traditional business intelligence. But why should we as teams and product teams take on extra effort to integrate BI within an application when we already have standalone tools that people are familiar with and exist? Well, if you can already find bolt-on tools, replace charts in your pages, why should we take it a step further? Because it goes beyond just the cohesion of maintaining a user's workflow without having to venture outside of your application, it's also going to add considerable value to their experience. It's going to impact how a user views your brand identity. And as a result, this is one of the key pieces of our solution, placing data directly inside of your application to provide crucial framing context while also preserving your application's unique brand identity. But while we need to add context, what difference does this really make? Well, as we've already seen, Meta Analytics helps users reduce the insight to action gap by delivering highly contextualized insights into the hands of your end users. It makes those users empowered to take immediate action at the point of impact while also performing specific tasks and they don't have to shift context or leave the application. With this implementation, we're both increasing user satisfaction and also increasing the stickiness of our application fully because users no longer have to leverage a separate interface to interpret data. It's this seamless experience that should be one of your biggest priorities to consider when evaluating how to best implement your analytics solution. While both traditional BI and embedded analytics can deliver charts and visualizations, only embedded analytics can truly place these items directly inside of existing workflows. This in turn is going to enable your teams to place a greater degree of analysis within the very fabric of your application. Because of this, most users are never going to know that they're using a multi-product app. In fact, it's the marriage of these two applications that's crucial in avoiding a disjointed user experience and it can prevent them from considering alternatives. Building on that same value, it's also important to preserve the unique look and feel of your application. As product teams, we work tirelessly to deliver a beautiful and recognizable user interface and user experience, and that in turn becomes a core piece of the identity of our application. By integrating analytics inside of these apps, we don't have to throw that identity away. With an integrated solution, you have the power to control the theming and styling of your visualizations and dashboards to result in a much more consistent end user experience. However, as we begin to consider what this experience needs to look like, we also have to consider the actual end users that are going to be engaging with those solutions. But before we talk about those users, I wanna review just what contextualizing the data for them will make as a key differentiator for your product. It will not only improve the inherent understanding of the data through added context, but it's also going to enrich a user's workflow by giving them relevant actionable data points. Additionally, your application's unique brand and styling are going to be preserved and enriched with new flexible analytic content. Speaking of those users, as we move into the third step of this process towards embedded analytics, I want you all to think about the users that interact with your application services today. For most of us, that cross section is going to be incredibly diverse in terms of skill sets and needs. It's this diversity that poses the most unique challenge that traditional BI solutions can't account for, which brings us to the third and final step that you need to consider for your own solution, user diversity and self-service. Our applications reach an incredibly diverse range of users every day, and it's crucial that we enable each and every one of these to leverage our analytic solutions effectively and efficiently. In order to achieve this, we need to integrate a degree of self-service so that our users can truly explore the data, interact with that data, and engage with your application in a new way that makes sense to them. You need to plan for an entire spectrum of user skill sets from senior leadership all the way down to individually contributing consultants. Some users may only want a simple dashboard that displays sales figures, while others may want to personally interact with the data to create their own charts and graphs. The important that each one of these users gets what they need without ever having to leave your application. For our power users, we want them to be able to interact with their data freely without having to wait on any of our application or development resource, as their ability to work longer within the application without any roadblocks increases, though does their overall satisfaction with the tool. These end users, like for example, a business analyst or a program manager, need the freedom to create dashboards and minutes, perform an analysis on that data, and then share that content entirely within our application. In a perfect world, your users should be able to access everything they need without ever engaging your support or training resource. With true self-service capabilities, you're empowering your users to complete their analytics and reporting tasks within familiar workflows. They'll also be able to be provided with better insights and also find the answers that they need entirely on their own. Now, certain embedded analytics solutions can even allow you to customize available functionality for your users, trimming out potentially confusing capabilities for those who don't need them. By increasing our end user's autonomy, we also increase our end user satisfaction and fulfillment, which subsequently helps drive their adoption and opinion of our software and tools. The more we give our users to leverage, the more time and effort they'll put back into that tool. But with an intuitive embedded process, we can not only drastically cut down the need for training, we can also free up our internal resources to focus on more critical tasks. By leveraging an easy to use analytics tool, your application's usability and adoption rates are going to increase along a simply enriched feature set. Now, this process is going to give your users an increased value and satisfaction that no other solution can provide. But before we move on, I want to recap the key takeaways from this third step in the process. First, embedded analytics allows you to perfectly match the skill sets and competency of your already diverse user base. Second, really having a truly and intuitive experience means that your users require minimal training and documentation to become fluent. And then finally, as requests for training and assistance decrease, you're going to free up vital resources within your own teams to focus on critical work. Each of these three steps are incredibly important in integrating any analytics solution within your software. And each one is also an effort that's going to further improve the customer's use of your platform and help to differentiate yourself from your competitors. And so to see just how much values can add in the real world, I want to pass things off to Mia for a bit to take a look at an example of the tangible impact that this can have for your organization. Thank you, Seth. So as Seth mentioned, I'll talk about how self-service and customizable analytics has basically revolutionized how Amplify delivers deeper customer experience insights. Just a quick company background introduction here. Amplify is a unique platform that provides brands with valuable customer experience inside, to meet the fast-changing customer expectations across marketing, care, and commerce platform. They've actually been in this field for about 25 years, reaching over a billion of monthly interactions for over 7,000 brand identities. And as if those monthly interactions were not staggering already, they analyze an average of 138 million for the customer's data point every single day. And that volume of data and feedback could be paralyzing without the right approach to handling it. But as we can see here, Julie and Baron, their software development lead, selected Logi Composer and then spoke to the result by stating, now our end users can build their own dashboards and visualizations, get better access to their data and get more detailed insights that they had before. But just collective problems that we address, we can actually see some other results that they have seen with the right embedded analytics solution. So alongside Logi, Amplify actually worked to highlight and address some of the issues that they were facing with their current solution. So first, their homegrown analytics solution was not customizable enough to meet their user needs. It would actively detract from the development of other features just to focus more on this phase. And second, their customers were only seeing reports that were provided to them and could only derive insights that team created on the behalf, leading to the potentials for, you know, missed insights and actions. Also at hop requests increase at their customer base and demand grew alongside their point, eating away more IT resources. But ultimately it was decided that they would use Logi Composer, a Logi embedded analytics solution to help address these concerns. And after the implementation went live, Amplify began to see an almost immediate return on investment. You can see that their customer adoption grew by 25% within the first 30 days. Can you imagine how, you know, increasing how engaged your customer were in your application that quickly? They could also seamlessly integrate Logi embedded analytics solution within their existing framework and security infrastructure. So basically that means that their development teams didn't have to juggle unnecessary risk and technical debt. And they were also afraid to, you know, continue their valuable work elsewhere in their solution. They could also customize our service to reach a wider range of users with different skill levels. And that means that the customer support team were able to focus on, you know, truly critical issue because the interaction with analytics was easier to understand and accomplish for their entire user base. While Amplify is just one example in the sea of companies leveraging embedded analytics, Seth will actually shift gears to talk about the key functions that embedded analytics need to provide to help you like Amplify, you know, improve user experience and adoption rate. Over to you again, Seth. Thank you. So now that we've seen and talked about the steps that you all should take towards implementing embedded analytics into your application and also the value that can provide, I wanna take a step back to talk to all of you about what an embedded analytics solution needs to provide and needs to offer for you and your organization. Within this section of today, we're gonna highlight key functionality that any comprehensive solution should provide and also argue with some questions that you need to discuss internally within your teams as you consider your own approach. Now on this page, you can see that there are four things here listed and as teams, I implore you all to focus on some of these areas as you look to determine the best path towards embedded analytics for your solution. Now it's my belief personally that any solution worth its salt is going to exhibit some form of excellence in at least these four areas, which as you can see are the integrated user interface, the integrated user experience, the secure access and multi-tenancy. In my opinion, it's these four areas that will help you and your teams deliver a much more refined user experience and increase the stickiness of your application. So let me explain why each of these areas should matter to you as it relates to your end user experience and the efficacy of your development team. With the integrated user experience, when we consider this, we need to consider the ease and flexibility of the components because it's crucial to how users feel about our application. Now there are a sea of applications out there today that all feature drastically different looks and your brand identity has to stand out among your competitors. And it's here that we see a few examples of what really helps qualify integrated user interfaces as an embedded analytics solution and how we determine that they are flexible and desirable. When it comes to marrying two distinct applications together, it's important that they can achieve a congruous look to all of their components. By selecting a tool that champions their ability to white label these components, you wanna make sure that any UI elements from that solution can be modified to fit your existing applications look and feel. Consistent styling across your workflows is going to help improve the end user belief that they're using one centralized piece of software and that they're not using several things put together behind the scenes. As you consider your own styling needs, be sure to focus on how extensible these components can be and how much they need to be. For example, ask some of the following questions. Do they feature extensible APIs for styling? Can we adjust any of these front end APIs to meet our needs within our existing pages and workflows? After all, you don't wanna implement a solution that's selling point is to fit inside of your application, only have to change your workflows to fit that tool. Lastly, consider the aesthetics of the tool that you're going to implement. Just how in depth can we customize those visualization? Can they leverage unique coloring, positioning and theming and what can we actually embed on a page? Additionally, think about how limited those visualizations are. Can you only access a basic library of them or is it something that we can extend to additional libraries and potential video and text resources? When we think about the user experience, it's almost important as the look of our application. After all, it is the feel and the flow of the user experience. Your embedded analytics solution should allow you to embed and configure your activity into one cohesive experience. As you evaluate analytics solutions and providers, I want you to think about a few questions and ideas here. First, how flexible are your visualizations actually going to be? Can you place them anywhere within your application or are you limited to a certain page or content structure? Does your solution leverage outdated, potentially risky iframes or is the integration more tightly coupled? When I look at any tool in the market today, I immediately wanna know how flexible their experience is for true self-service analytics. How easy is it for someone to author their own visualizations and dashboards within the tool? Do I feel like my end users will be able to grasp this without any hands-on training or what selfishly can I get away with just improving my documentation to support this new feature set? These are important considerations you all need to ask yourselves and any potential providers to ensure that you're finding the best fit for you. Now, while training and understanding is a big concern for basic use cases, also consider the opposite side of that simple experience. How advanced are these interactions going to get? What level of interactivity can we get out of these visualizations? For a couple of examples, can we filter and limit data on multiple visualizations easily and consistently? Because while a simple user experience does help address the layman user, we also wanna help ensure that that solution enables power users to truly shine. Now, we've talked a great deal about the user side of things when considering embedded analytics. After all, we're all here to discuss how this really drives user adoption. But for our teams, what risks are we taking on whenever we integrate another tool inside of our application? So as you consider an analytic solution, particularly an embedded analytic solution, differing what information a user has access to can often be as crucial as the experience itself. After all, we all know that data security is important, so how do we really control what data users can and cannot see? Does the tool that we're evaluating allow you to control the authoring life cycle of reports and dashboard? When you look at any potential solution, I want you to ask some of these harder and more technical questions to them or potentially just within your teams. For example, what control do we want over self-service functionality? What functionality do we need to present to users and how do we best regulate it? While these conversations can be in a larger internal discussion, it ultimately leads you towards selecting a tool that truly fits your vision for the application's analytic solution. Outside of the user consideration, I want you to also consider the security of the data itself. Anytime you introduce a tool that accesses sensitive data, it's important to know that the solution you're choosing isn't introducing any potential headaches or concerns in the long run. Challenge your teams to investigate how they want to achieve security models, things like row and column-level security, and also how do they want to log user activity. Wherever possible, select a tool that not only addresses these concerns, but actively removes them by providing even more flexibility behind the control of your data and how users are accessing it. Lastly, I want to mention a topic that most of our customers want to discuss here at Logi, and it's a topic that I've also spent a great deal of time discussing during our integrations, which is multi-tenancy. Now, your analytic solution isn't just for you while you also have to support a wide range of tenets from potentially one singular tenant to maybe an unlimited number of them. Not only that, but it needs to achieve this range of scenarios while also requiring the least amount of code and components necessary to really maximize how reusable the solution really is. As you can see here, many of these concepts probably aren't foreign to you, things like single versus multi-tenant or single sign-on. But what might not be familiar is why these actually matter for embedded analytics. We've spoken a lot about creating a cohesive product vision and brand, but what's about what's happening under the hood? Selecting a tool that allows a seamless integration of not only your branding, but also your security models will further reduce the number of resources you have to dedicate to its implementation and ongoing management. Make sure that whatever solution you choose meshes well with your existing security models and also enables your teams to lock down and control who's accessing the platform. Ensure that your solution also provides some support for your current multi-tenant structure while also allowing a fluid authorization process. Again, I wanna stress that creating the illusion that your users are accessing one singular tool is one of the largest victories you can achieve when creating a better user opinion of your software. Additionally, I wanna note that your product teams should really ensure that your Tennessee deployments allow for curated and effective levels of self-service. What I mean by this is that once your tenant structure is reflected inside of your tool, you need to make sure that you can still maintain a fine control over the self-service features and content interactivity, things like report and dashboard creation, deletion, or sharing of that content. To wrap things up before we take your questions, I wanna take a moment to really evangelize Logi Composer. After all, I'd be pretty remiss if I didn't take an opportunity to speak about how all of these product considerations are concerns that we hold dear internally here at Logi and that we often strive to reflect within our mindfulness and the offerings. With Logi Composer, we've designed all of our functionality to deliver the first truly out-of-the-box development experience for embedded analytics. We understand that your software teams are responsible for the work that's going into building and sustaining your application. It has a testament to that. We wanna provide something that's easy to use while providing total control over the end-user's analytic experience. Across our entire product team, we work to ensure that we provide not only the best embedded analytics for your users, but also the best analytic experience for your own internal team. With Logi Composer, we focus on a few key areas to really help drive that integration. For example, content authoring. We wanna empower developers to create and leverage visualizations with our effortless authoring paradigm, but not only that, we wanna give them complete control over how end-users will interact with that content. And we're not just trying to help with the creation itself, we're also trying to help with how people are actively going to use it and interact with what's being created today. Speaking of your end-users, I've mentioned that embedding self-service into your product is a key part of driving adoption rates. Well, Logi Composer can be controlled to match the skill sets of your users, while also enabling to modify and share their own content. You can provide or limit the functionality that's enabled for them to truly make sure they're not accessing things that are beyond their skill sets. For any of your users that are going to be authoring something, we're exceptionally proud of our query engine and smart data connectors. Not only do we provide a wide library of connectivity, but we also provide you with exceptional performance when accessing that data. You wanna be as responsive as possible, give your users as little time waiting within their workflow. Additionally, we aim all this functionality to live within your existing tech stack without additional setup from your development team, further freeing up those resources to focus on other things. Now, speaking of those resources, let's consider DevOps. We've built out Logi Composer with a cloud-ready microservices architecture in mind. We wanted to provide something that scales effectively, both for performance and your cost. So that way you have to have less resources spent towards maintaining it as things move forward and it's able to scale with you as your user base moves into more adoption and it rolls out to more of them. I just wanted to say thank you all again for joining us today. It's really been wonderful getting to speak to something that we're so passionate about here at Logi and we hope you've been able to take away some key points to discuss internally with your teams. Because we have covered so much ground today, I wanted you to take these four points to heart as you continue looking into your paths forward. I hope that you all continue to look into embedded analytics as a means to truly drive your product into a constantly evolving space. We look forward to hearing from each and every one of you.