The secret to SaaS growth in 2026: self-service, monetization & deployment

Thank you everyone for joining. Welcome. Uh you're in the right place here. We're talking about the secret to SAS growth in 2026. What does that mean? We're talking about self-service monetization and the right deployment strategy. And we've got a great panel lined up for us today with me, many different perspectives and years of experience. Uh so let's jump right in. First, a little bit about Qrvey. If this is the first time that you're you're meeting us, um, we are an embedded analytics and AI platform. We're built specifically for SAS companies. And the reason why we're here today is because embedded analytics is growing in importance, uh, especially in the

technology and SAS markets. And the reality is is that it's no longer just a feature, right? It it's a growth strategy. And I think that's why we're seeing the, you know, the increasing of I mean, excuse me, of importance on this technology as it relates to your product, your product's ability to grow and scale and meet your customers needs. But if we are looking at embedded analytics this year, then there's three key decisions you really have to focus on. And so one of those is how to really deliver a true self-service experience for your customers to be able to personalize the way that they interact with their data. Um, and then how do you turn those analytics into a scalable revenue engine? And then the last one

we'll talk about today is really talking about what's the right deployment model. If security is important to you, if you want flexibility and control, what's the right deployment model for your business? and we'll talk about a few of those options um as well as some of the research that we you know we're going to talk about too in that regard. So let's jump into how this is going to work. Um kicking us off now um and for each of those three critical decisions we're going to start with some research. So we're joined today by Howard Dresnner. He's the founder and chief research officer at Dresnner Advisory Services. uh he'll be taking some research from a recent study uh the 2025 wisdom of crowds embedded business intelligence

market study uh that his company turns out every year. Um so we'll be looking at some of the new recent research there and then we'll be talking about it and kind of sharing our own experience um with with that information. On our panel today we have Todd Kinsman. He's product manager at TCP software. We're also joined by Benjamin Hans, chief operating officer at Ingenious Build, and Natan Cohen, head of customer success here at Qrvey. And I'm Carrie Pierce. I'm the head of marketing, and I'm really looking forward to the conversation. So, while we are going along the way, feel free to use the chat to ask a question. Uh, you can also use the Q&A feature. We'll try to answer most questions at

the end because we've left some time to dig into those questions. Um, and if you have any any other comments or questions, you want to talk with any of the panelists, feel free to use the chat feature. So, without further ado, like to pass it over to Howard and he'll kick off and share some interesting information. >> Thanks, Gary. Uh, welcome everybody. So, I'm super excited about embedded. We'll talk more about the slides that you see in front of you in a moment, but we've been covering embedded for 13 years now. And when Carrie said, you know, it's the 2025 embedded report, we actually publish it at the end of each year. So, it's really fresh research. It's not like it's at the beginning of 2025. We collect data on a vast array of topics.

We've been doing it for almost 20 years now. We are a bunch of certified data geeks. We love our data. So I want to sort of set the tone and sort of at a at a high level and then drill down. So one of the things that we ask our community is about what are all the forces that are impacting them right now and their organizations. And if you're if you're a leader of an organization, it's really sort of unprecedented times with all of the external forces that are bearing down on you. Whether it's economic and geopolitical in nature, there are so many things that you have to deal with and have to juggle to make sure that your organization is successful and how organizations coping with that. Well, on the next slide, you can see we asked

organizations about all of the business factors that they're investing in. And this is not just technology, by the way. There's a whole array of things that we've asked them about that they're either investing in or perhaps pulling back investment in. And at the top of the list, you have artificial intelligence. By the way, data and analytics used to be number one for a while. AI is now at the top. And those go handinand glove, right? AI and data analytics. And then digital transformation. All those things are, you know, intrinsically related to each other. We're organizations not investing. And you see some interesting dynamics here. by the way, not investing in business travel. All right, sometimes they have to do some, but not as much as they would have in the past. Discretionary spending. And then look at the dynamic between office space and

work at home. There's some organizations continue to invest at work at home. Other organizations are pulling back and investing more in office space. So I guess it depends on your particular experience. But what's notable here is that data and analytics at the top of the list. That's how organizations are coping with all of those pressures that they're feeling from the external environment. All right, let's um drill down a little bit now and talk about embedded. Now, embedded is a form of self-service. And like I said, we've been talking about embedded for many years. And I love this chart because you got 13 years worth of data. And you can see that embedded is in terms of importance is at its highest point that it's ever been in those 13 years. So it's you know you you would consider it

at the level of very important right now for organizations. Now self-service is a way to deliver data-driven insights to the organization broadly which is really important. We talk about a couple of things or I've been talking about it for many years. Something I call information democracy which is how do we get data-driven insights to everybody in the organization and beyond in the organization to help them be more successful in their particular role. Now, the slide that you see here is really important because what it says, it's a little confusing here, the columns and the and the bars, but what's important to take away here is that organizations that use um self-service capabilities tend to be more successful with business intelligence. And what's

important about embedded is it is one of the best forms of self-service capabilities because we're including it in the context of another application. And that's one of the challenges with delivering this notion of information democracy is that how do we get it out to the masses without having to do tremendous amounts of training to make them more sufficient or self-sufficient. So self-service and success with BI those also go hand in hand. All right, next slide. Before we go to the discussion, let's take a look at the feature priorities. And I like to talk about this from a couple of perspectives. Now when we started tracking embedded business intelligence and analytics 13 years ago, the dominant form of embedded was static reports. All right? So you're in an application, you

click on a button, you'll get a report. Uh that's not the case anymore. Uh users and organizations are demanding the full set of functionality that you would expect in any BI or analytical application. So first and foremost, yeah, visualization. They want it to be visual. They wanted to be interactive in nature. And as you go down the list in the top five, you see some really core self-service capabilities. So the ability to actually modify and create and you know do new things that perhaps the you know was was not intended not not that it wasn't intended but had not been thought of by the provider. So a lot of freedom within the application uh that perhaps um you know you wouldn't have seen many years ago. So, let me

turn it back over to you, Carrie, and uh start the discussion. >> Excellent. Thanks, Howard. All right. So, here we are at our first point here. We're going to be talking about how to deliver a true self-service experience. And I'd love to hear from the panel. Now, um for before you tell us a little bit about, you know, your company and and what you do, but I'd love to know what were the main drivers for offering self-service at your company and what are some of the outcomes you're seeing today? Yeah. Um, yeah. So, I'm Todd Kinsman, a product manager at TCP software and, uh, I work on the flagship product template plus. Um, and we've been, uh, focusing a lot on our reporting and analytics, especially as these experiences become more table stakes for our customers. Um,

so that said, uh, one of the, uh, ways that we've been focusing on trying to make this more of a true self-service experience is understanding what information the customers need and when they need that to make those operational decisions. Um, because increasing or decreasing that time to value for them is is critical in helping them keep their costs low and making sure that they're happy with what we're providing them. Um, one example for us specifically on the time clock plus side is overtime trends. Um, you know, the sooner that you're able to identify in a trend that somebody's going overtime or potentially heading in that direction, you're able to curtail that and help save costs for that customer specifically. So, um, making sure you're knowing what data, in this instance,

kind of that time tracking and what data leads to, uh, financial impacts to the customer has been a really helpful way to understand what data helps them self-serve what they need. >> That's great. What about you, Ben? >> Yeah. Hey there. So, I'm our COO here at Ingenious.build, which is a construction project management company. If you want to build a large building, our goal is to get uh different personas throughout that process into one room and to collaborate effectively with each other. Um to speak specifically about why we chose self-service as an option, I think there are two primary reasons that uh were driving us uh towards this. um

three um first reason being um as Howard was talking earlier there's there's this push towards in a way democratization of data um a lot of our um competitors offer this and it's becoming table stakes to be able to offer this especially to large enterprise customers to the ability to have access to your own data and to slice and dice and analyze in the way that you want. Um, a second really big advantage for us with self-service is that it offloads some of the engineering on our side to our customers. If customers have very specific uh reporting needs or analysis needs, then we can say here you go, here's the data. Um, and do do what you want. Um, we're not holding them back and um they

can proceed with what they need and we can spend our time on other things that we have a unique advantage for. uh as software developers. And the third reason that uh we we like self-service is it's just a useful additional offering from a monetization perspective. We can um sell this as an add-on and um uh to to customers that are willing to pay for it. So it's it's great just from a marketability perspective as well. Mhm. Are there any other outcomes that you're seeing today at both of your companies with the ability you said like offload engineering resources? Where have you been able to you know have those resources focus on more innovative

>> Yeah. For for um at TimeClock Plus there's often custom reporting requests that takes teams and requirements gathering and back and forth. So by, you know, understanding the data that they're needing and having that in that self-service model, they're able to handle that on their own and free up that team, which often would work on other integration and other data-driven parts of uh the work that we have. So it does definitely free up some time for us. >> It's great. >> Yeah, agreed. Um I think naturally people feel very strongly about um how they interact with their data or how their reports look. Uh so that can be a pretty big time suck uh from an engineering perspective. So

enabling the customer to to build something to the level that they want um is great for the customer. Uh and then yeah that allows us to to focus on on things that are more unique to our company. Um creating a report uh you know putting logos here and there and maybe summarizing data at this level. Um, a lot of different people can do that, but we can focus more on building software related to these construction project uh, workflows and other things that kind of are a unique selling point for our software. I'd say >> that's great. So, Natan, from your perspective, what's the biggest misconception about offering self-service analytics in a SAS product? And what do you think companies tend to

miss when they're evaluating solutions? >> Sure. Well, hey everybody. I'm the head of customer success here at Qrvey. And uh I think the biggest misconception at a high level is that it's going to be easy, that it is easy, and it's just about sharing and publishing dashboards. Um and uh it's it's about a lot more than that. Um and it's not easy, but it doesn't have to be hard if you know what questions you should be thinking about so that you can ask potential vendors um what their solutions are. and the vendors should have already thought about and understand those problems. Um, but I think uh on Todd's answer about you know presenting data sets uh that are meaningful for customers or end

users to be able to find the metrics that they're looking for. Um that's often overlooked uh and you know having end users understand your internal data structures is just not reality. But nor is it a great option to let them navigate the structure um and try to find entities that relate to each other. Sometimes less is more and it's about creating um meaningful views that are properly labeled so that customers or end users can find the data they're looking for. Um so that's the first thing. The second thing is about self-service itself. Once tenant end users start to create content, um if you have hundreds of tenants, you'll have

thousands of end users, right? And if you have thousands of tenants, you have even more end users. Uh how will you manage that content? Um and then how will you manage your own content like uh template dashboards um uh and promote those effectively from lower environments to higher environments, right? Do you have a a vendor or a platform that um integrates well with your existing DevOps processes so that that entire process um is not uh an extra process that doesn't fit in with your organization um and just makes everything the maintenance of the solution um just more of a nightmare. So

those are just a few things uh to think about. That's great. Thanks, Natan. Thanks, Ben and Todd. All right, Howard, we're gonna go back to you. We're gonna talk about the next uh the next item here. >> Yep. Yep. Let's talk about external objectives. So, we ask our community about their internal objectives with embedded as well as their external objectives with embedded. And so, there are two slides here. You know, really what are the goals that organizations have for externally focused embedded business intelligence? And here we slice it by industry. And so what you see here is that you know high-tech so folks like all of you ISVS evaluated resellers mostly are looking to embed those

capabilities within your product so that you can monetize that so that you can you know sell it to your customer either making your package or your offering more appealing or as an aftermarket selling opportunity. And of course it varies by vertical industry but for high-tech that would be the number one answer. And if we go to the next slide, if we look at the expected business outcomes um and there are two here when you once again looking at it by industry. If we look at it for uh technology providers, there are two things and I would say that it looks like increasing customer loyalty is really the top answer closely followed by generating revenue or being able to charge for those business intelligence or analytics capabilities.

And especially in today's market, being able to retain a customer and add more value or more perceived value is really at the top of the list. It's a lot cheaper to keep a customer than to acquire a new customer. >> Back to you, Carrie. >> Absolutely. Thanks, Howard. And you're right. I mean, companies are looking for opportunities to monetize the features in their product, but it's not necessarily that easy to figure out what's the best way to monetize. And so, that introduces, you know, the next panel question. I'd love to hear from Ben and Todd. What does your monetization strategy look like today? why did you make that approach and then can you share a couple of KPIs you're tracking?

>> Sure. So from our perspective, our core monetization strategy is to sell licenses. uh customer who buys licenses gets access to all of our base features and then in addition to this you can optionally to decide to buy a reporting add-on and that reporting add-on from a pricing perspective scales based off of the number of users that you have basically your your TCV total contract value. Um we decided to um have this specific approach for monetization with reporting because um we knew that not all of our customers will need it. We

service both SMBs and enterprise. Generally this becomes more of an enterprise large-scale customer uh need um finer tuned access to data. Um so we made it optional. um we think that there's a lot of money on the table um to be to uh to get from a sales perspective. So we we definitely don't want to just give it away for free to we we want to monetize it. Um and then um we also want to make sure that uh pricing scales linearly based off of the the use as well. So five people using um this software versus 100. We want we think we can we should be able to charge more if there are more users. Uh so that's how we arrived at this kind of

scaled approach that's optional. Um and that's working well for us. Um as far as specific KPIs uh because we're a company that is um offering uh 50 plus different modules um we we generally uh track KPIs around u adoption and usage of our platform and uh we track how frequently our users engage with particular portfolios of features and what types of actions are they um we generally keep those KPIs um aggregated and grouped at a high level. We don't really drill down too much specifically at specific module

levels uh because again we have a we have a large ecosystem of features right now. Um so that's our one of the big north stars from especially from a product perspective is just general engagement with our product um throughout our platform across different modules and then we'll have some let's say sub KPIs that are tracking time to value for some specific um we call them aha moments key moments that we want new users to get to as quickly as possible to understand the value and purpose of our product. Yeah, I love the aha moment. What about you, Todd? >> Yeah, I'd say it's uh similar as far as pricing structure where, you know, we have our licenses and this would be an add-on for our customers. Um, but when

kind of thinking about and understanding what we would how much we're going to charge and pricing for them, um, we kind of first focused on what the value drivers are for them. Um and so we've we have a verticalization strategy where providing some dashboards that are specific to those verticals to try to show uh you know on the sales side some of that immediate value that you can get if you are in the K12 market for labor management or if you're um in the uh some other government industry where you have a lot of uh budgets that are very tightly managed due to some sort of union contract. um having some very specific KPIs that we can provide for

them out of the box is very helpful um and which was pretty nice but then also we are making sure that we're understanding the number the size of the company because that does have an impact on the infrastructure costs um from our side to make sure that we're we're able to still stay in the positive and and not take on too much cost because of the size of the data sets that we're trying to provide and uh manage for our customers through the embedded analytics. And really getting this solution and getting it to folks sooner um was a a really big win for us just to be able to have something like this that we can offer and continue to build upon especially as additional features from Qrvey have been added. Um like the pixel

perfect reporting and now I know that you guys have some AI features coming up as well. Um, it it's nice to be able to have that built in there and have a pretty clean path to how we would want to monetize or add additional value for our customers. And u maybe not always monetize if it's something that is just a standard that we should be including, but having that path to be able to do that pretty easily since it is an embedded product. >> Yeah. Yeah, that makes a lot of sense. Are there any particular KPIs that that you're tracking as well? Ours is a lot about adoption and retention especially in those mid to enterprise uh size customers. Okay. >> Excellent. Um Natan, you work with, you

know, all of our customers and this is not your first rodeo with embedded analytics. So tell me a little bit what are some of the other monetization models, excuse me, that uh you've seen work well? >> Yeah, sure. I I spend a good amount of my time um sometimes working with sales and and marketing and and even um executives with our our customers thinking about how to monetize embedded analytics. And what's what tends to be common and popular with our SAS organization customers is um a subscription model that ends up being you know three tiers and you can call them what you want like silver, gold, platinum um but uh they want to know you

know how can I gate uh access in embedded analytics? What are the things I should be thinking about um which are not obvious? Um, I think the one that most people think about is gaining access to self-service itself. Um, access at the feature level that can be tied to perhaps a tenant and user role. Um, but there are many more ways to gate access. Um, you can gate access based on um the number quantity of assets that you can create, right? And that way you can make a competitive differentiator and say everybody has access to do pretty much everything. That's our competitive differentiator. But if you subscribe to a higher tier like

platinum, um you're not limited to maybe 10 dashboards, right? You can create unlimited. Um perhaps I think the most valuable way uh uh or or most uh or a great way to gate access um in a subscription model is understanding that as a SAS organization your data is often the most valuable asset you have to give to your customers. Um so perhaps you should gate access to types of data. um or if not types, volume of data, right? We can go back two years, we can go back five or 10, and if you're at the highest subscription tier, um you have access to all of it. Um now,

marketing and sales, they're going to spend a good amount of time thinking about what tiers you should have and the pricing and what are the gating factors. It's a shame to then take that back to engineering and product and they say, "Well, that's lovely, but we can't support it." Right? There's a there's a um a process here. Um how am I going to uh on board a customer who's at the platinum tier? What if I on board them at gold and they later upgrade to platinum? How do I push those assets into their workspace? Um and what if they downgrade? Right? So, these are processes um that engineering and product will have to support. Um and it shouldn't be a headache for them to do so. Um so that those are the things that

uh I work with our customers on. Yeah, absolutely. And you made a really great point about bringing more more functions together to set up a a monetization model that's not only going to work but also scale because what SAS company doesn't want to grow, right? I mean, this is why we're in business. Um so very interesting, great perspectives. Uh we're going to hand it back over to Howard. and we're going to start talking about deployment models. >> Great. Well, thank you. So, a couple of slides about deployment models for embedded business intelligence. And this is asking the entire community what their opinion are, what their preferences are regarding deployment. And you can see the top answer is actually being able to deploy this u in the vendor's environment. So, the vendor

that they're actually acquiring the embedded capabilities to actually leverage it within their environment. The second answer is being able to deploy it to their virtual private cloud provider AWS etc. or the last least preferable option is be able to deploy it uh at the customer's data center. So that's the entire community. If we drill down and look at this by vertical on the next slide, you can see some uh some variations here and you can see that in fact high-tech organizations would prefer to be able to deploy it to uh the organization's virtual private cloud. The second uh option which is you know which is quite a bit lower than the first option is being able to support it in the vendors environment. And of course the last option which is really

really small is uh deploying it uh in the organization's data center. >> Yeah. So let's kick it over to the panel here. Uh so Ben and Todd, what requirements drove your decision around the right deployment model for you? Was it uh you know hosted by the analytics vendor or hosted by yourself as the SAS provider? And why was that important? Uh for us it was uh one of the big concerns was the security aspect of it. Making sure that this really highly sensitive data is managed in a secure way and that meant that we would be managing it ourselves and keeping it within the rest of the data that we're managing. Um and I should also say too

that having a really strong data foundation as well is a really important part of this. So again having that crossf functional um decision making especially with like engineering leadership or u having the data architectures involved just to make sure that you understand not only how to have the right data and how to continue to uh deploy and keep things up to date in a efficient way. Um and although that might be something that might be an initial uh uh a lot of investment from some of the engineering resources to understand having that proper foundation and great data modeling within the organization can have multiple benefits outside of just having a way to plug

into the embedded analytics especially in the world of AI that's just driven by your data. So having a a wonderful foundation, it has many benefits and something that I really highly recommend folks investing into right from the start. Um because you don't want to have to retroactively make a lot of adjustments afterwards, you know, like adding plumbing to your house after you already built it, >> right? >> Yeah, on our perspective, I think it's something similar. um security. I don't I won't add too much there. Um we have customers uh who care a lot about their data and where data is persisted. Um so being able to have control there helps

when it's managed within our ecosystem. And then also from just a functional perspective, if it's hosted within our technical infrastructure, um we can set up our own automated tests. We can deploy updates when we want. We can um make sure that updates are shipped with our analytics software at the same time as when our main production website is updating. So, we just generally have more control. We have more insight into our own costs um and let's say spend on a monthly basis from our own uh cloud infrastructure. So it it it's more flexibility and control and ability. Um it definitely comes with

let's say some more more work. Uh but uh we we think it's worth it to be able to have those uh those benefits. >> Yeah. Cost predictability becomes even more important, right? And now that you're monetizing your solution and kind of protect those margins. Mhm. >> So, Natan, what are some of the limitations that a SAS company might run into, right, when they're choosing either uh, you know, the analytics vendors hosting my solution or, you know, we should self-host. >> Yeah, this is one of the areas where uh it's really not obvious why the deployment model matters at all, right? because if a vendor you know hosts your solution I mean that that can work and

it certainly would work um I would think that if you are a SAS organization um who has built uh a web application and that's your core intellectual property right and that's that's the user experience that you've uh spent hours countless hours designing and curating um you really can't sacrifice device that and it's it goes beyond embeddability, customization, colors, logos. It's it goes beyond that. It's really about um is the the analytics vendor that I've chosen that says that they're integrating with my application um is it still separate, right? Is it is it really under my control? Are they going to upgrade it? And then my customers are presented with features

that I didn't know were coming and I haven't prepped my support teams and my internal CS teams to support. Um so you know as a a SAS organization you know how can you not have that level of control? And when you self-host the platform in your cloud account um it's really not a separate platform. It's part of your platform and you control it. And this really comes into play when it when we talk about DevOps processes promoting content from lower environments to higher environments. Do you have a vendor who even acknowledges that you're going to have more than one instance of their platform? You might have a couple test environments. And even production, it's not a monolithic thing in the cloud. You might it might

be one production account, but there are many regions of the world that you might want to deploy to. And there might be a pesky customer who comes along and says,"I can't have any of my data even sitting in the same repository. So I need my own instance, right?" Well, is that going to be a problem for your vendor? In other words, as I scale my own business and become successful, is my choice of analytics vendor going to become an asset or a liability long term? Um, and then speaking about security, it's nice to know that your data isn't leaving your cloud, right? >> And that's certainly another advantage. >> Yeah, absolutely. Especially if you're an industry that is looking at

compliance regulations. Um, I think that we are we're actually up on time or we're up on the the panel, but we do have some questions that came in. So, let's um let's dive into them. So, the first question, no surprise, but no one has talked about AI yet. Where does AI fit into uh into this um I'm going to assume mostly into self-service? Um potentially monetization, but at least self-service. So, I'll toss that one up to the group. >> Well, I'll I'll take it first if that's okay. So, uh it's interesting. I was just at a a large conference and trade show focused on data and analytics and everyone was talking about AI and agentic AI and context. That was really

big. It was of course it was really hard to differentiate one vendor from the next because everyone is is talking about that now. But I think it's um it is important to note that agentic AI and generative AI and business intelligence really go hand in hand and increasingly organizations do expect that to be one of the paradigms that you offer them and so I would expect and and when you're looking at embedded because everyone's talking about context well with embedded you've got so much context for your for your agents or for generative AI to guide them in answering the question for the end user. So for those users and it comes down to use case as well some users don't want to use generative or agentic AI others will use it aggressively and so it really does come

down to the use case but embedded and embedded business intelligence and artificial intelligence specifically generative agentic AI are natural fits and I would expect that users would find that at least some number of users would Yeah, Qrvey, you know, we we're not really afraid of um AI. We're we're leaning into it. I I think when we look at the areas of our platform um that AI really um would have an impact on, uh we're we're always trying to make the process of building dashboards and analytics easier, right? We're not married to a pointand-click interface. If I can describe what I want to see and the answer is a chart or a

mashup or an entire dashboard, that's great. Um, you know, the value of the platform is not like point-and-click bar chart, right? It's really um uh the things that I've I've talked about um in terms of content management and and data presentation, things that AI just can't do immediately for you. Um, and it's more about making things easier that are just kind of um things that naturally need to evolve in terms of how we create uh content. >> Right. Right. And if Genai is going to allow the user to interact with that content, that is a form of self-service altogether. >> So the next question that came in were what were critical discussion points during the internal evaluation of

For us, it was mostly time to value, trying to make sure that we were able to find something and find something fast. Um, especially since we were at that point a rapidly growing company and had acquired other um businesses and were trying to make sure that we could find some value, especially when the engineering teams were working on so many different things. um making sure that we're able to get the an analytic solution implemented and as fast as we could. >> Thanks Todd. >> Uh to add there I think cost is a big one. Um it's crazy how much of a spread there is in terms of uh cost by different

analytic providers. Uh and then also um ability to customize. Uh one of the big things that drew us specifically to Qrvey is an immense amount of customization um from both a data architecture perspective and deployment perspective uh as well as just enabling customers to use the software that we provide to them and allow them to uh make their own adjustments. So I think for us it's kind of how much can someone how much control do we have and ability to customize as well as price uh as well. And I think another one is just you know how how much of a resource is that the company available um to support you in setting

up your analytic uh software as well. >> Yeah, that's great. And that actually uh is a nice lead-in to the next question. Uh the next question is what's the level of responsibility for educating your end users for self-service analytics as everyone's looking at you know high adoption. >> I'd say that's something that's an ongoing process especially depending on the the size of the company company you're looking at. I've noticed the trend for if you're at a more enterprise or that mid-size company level, you have more folks who are just focused on data and analytics and reporting. And so on ramping them into a solution with

analytics is not hard. In fact, they'll ask for certain things that we might not already have or that we should be considering for enhancements. So they're a really great resource for understanding how we can um make some additional customizations which has been really helpful um like Ben is saying because it is much easier to do those customizations with Qrvey. Um so I I'd say that that would >> Yeah. Qrvey um you know we recognize that there isn't like a monolithic end-user persona um there are different types of end users and you know you're certainly going to have the folks who are just the

viewers but um there are also folks who uh want to interact and make simple changes but they just need a very simplified interface that really focuses on the key features that are meaningful to them to customize a dashboard. Um, and then you certainly have the end users who are pretty savvy report developers. They've used BI tools for two plus decades. Um, they even understand the structure of data and may even be pretty fluent in SQL, right? So then, you know, do you have a way to offer them the power to create buckets and formulas, um, conditional formatting and things that they would otherwise expect to see, um, from their from their experience?

>> Ben, did you have anything to add there? >> Yeah, this this one's interesting. I think at the end of the day if if the users don't know what the value proposition is of the feature then um they won't get the value. So what is the fastest way and lowest touch way to get that education and awareness. Um, one of the things that we've started to do is to give some out of the box reports for example um to new customers so they can see uh what's possible um and then they can modify that and um that's just a very quick way of showing them what the value is. Um I I think uh it's tough because there there's a very large um

there's a lot of things you need to know to have uh like a a good approach to building reports. You need to not only understand the business and like what the data means uh but you also need to have some background in business intelligence. So I think it's it's tough to be able to expect um to train people on everything. um there should be some base expectation of u knowledge on the other side. Um and with that assumption I think with um some basic reports, access to help articles um uh hopefully people can kind of uh get there and then you can offer the last last mile in terms of answering questions or um trying to provide some insight on how to

do specific things. >> Yeah. And I'd even add to that too. Um, one important part for us has also been partnering with our sales team and the enablement teams to make sure that they're setting expectations right from the start because exactly what Ben said, it it it's this entire separate kind of functional uh technical area that you know engineers might not even be very familiar with. You know, it's the difference between building a car and the highway that the car is working on. And so if we're if if we're not setting that expectation correctly from the beginning, it can lead to much more difficult conversations later on in the process. >> Yeah, that makes a lot of sense. Um, one thing I heard someone talk about is

personas. And so, you know, understanding that your approach to self-service is not a one-size-fits-all for your customers. So, just curious, you know, Ben and Todd, how many different personas do you do you focus We focus on two the like an admin type persona and then a viewer persona who's more operational. >> Yeah, makes sense. >> Uh yeah, specifically within within reporting we have two um and then our generally within our product we have like 10 10 different personas of specifically within construction. >> Great. All right. Well, that was a wrap on the questions. I'm just going to jump

to the last slide. Uh, what do you do next? Right. Um, so we'd love for everyone who's attending today. If you've got an interest in u embedded analytics and rolling that out this year, it's best to start as soon as possible so you can increase that time to value. Um, we've got some interactive resources on our website. Please check it out. Uh you'll see things like an interactive demo center, a a developer playground where you can actually try different aspects of our solution for yourself. You can match it to your own UI and get a feel for how that works. We've also got a fun ROI calculator and Avenger scorecard that's that's interactive as well. Um, but as a thank you for everyone who do did attend today, we have a copy of the 2025 wisdom of crowds

embedded BI report by Dresnner Advisory Services that everyone will get a copy of uh to really dig into the data and uh hey if you have questions right about self-service, how to monetize, how to choose the right deployment model, the things we talked about today um but also just multi-tenant data architecture, right? and and whatever solution you're working with now and trying to retrofit, we're we're willing and ready to to have those conversations. Uh whether it's at the business level or digging in um you know under the under the hood there. So uh on behalf of everyone on the team, I want to thank Todd and Ben for sharing your insights today and Natan as well. And Howard, thank you for going through the

research. We really do appreciate that you could really ground this conversation in data. It's important. And um so once again, thank you all for attending today and we'll see you soon.