Beyond Dashboards: The transformative power of embedded analytics

Broadcasting live from our studio in Boston. Solutions review is proud to showcase Qrvey in the solution spotlight, a unique online event for industry professionals. I'm Doug Atkinson here at solutions review, and welcome to the solution spotlight featuring Qrvey, and focused on the transformative power of embedded analytics. Embedded Analytics is shaking up the business intelligence landscape, going far beyond old school static dashboards. So today, We will explore how embedding customizable analytics tools into your applications will deliver powerful insights into your workflow.

Qrvey is an embedded analytics layer for SaaS companies. It empowers these companies to create more valuable customer experiences by enabling users to build and share custom analytics within their SaaS applications. As an added bonus, you're going to hear from a Qrvey customer on their need for a solution that went above and beyond simple dashboards. And joining us, to walk you through the Qrvey's solution is Brian Dyer head of product marketing. Brian, thanks for being with us. Thanks for having me today.

This is a great topic for us. I have to say that, it's really been the last couple of years that analytics and really data all of our data categories are kind of blowing up. And it's just the it's just a huge topic. Everybody's interested in it. We have a lot of site traffic audience, that has, is gravitating toward this topic in particular. So I'm glad you're here. And you also brought a special which I alluded to in our intro.

Tell us a little bit about who you brought with you. Yeah. I brought, her name's Erin from a company called Resolver. And, you know, a lot of what we wanna talk about is, you know, a lot of why SaaS companies are going down this path of, you know, the old build versus buy on why buying tends to be a better route. So we wanna talk about kinda, you know, what led down this path and how we got to those, some of those decisions. Well, welcome, Erin. It's always a pleasure to have a customer, on these types of programs.

Just to set everybody's expectations, Brian's gonna do a presentation, and you all are going to get a chance to submit questions if you have any. I will take them in here at solutions review, and we'll save them for the end. So by all means, please submit them and we'll and we'll ask Brian, everything in Erin, if you have any for her. Otherwise, Brian, I'm gonna turn it over to you, and let you take it from here. And then I'll connect back up with you after you and Erin if a chance to go through her experience.

Great. See you in a bit. You bet. So we'll get right into this today. So, again, Brian Dryer with Qrvey at here. And Aaron, who's a product manager with Resolver. So we're gonna cover some different topics about, you know, kind of this journey from static dashboards to a more comprehensive analytics product directly within SaaS application. So we'll kinda cover these three topics over the course of the next, forty or so minutes. So one of the most common places to start, right, these dashboards.

Right? We've heard about dashboards for twenty years. Why are we still talking about dashboards? But, you know, when we frame this in the context of SaaS applications, right, we're talking about SaaS applications that have a need for a better analytics product. You typically find yourself a little bit in this sort of a situation. I just think this is always a great meeting to start it off here. Right? Your customers are looking for you to tell them what to do with data. They want your expertise. And you're kinda like, well, we have some charts. Isn't that good enough? And the reality of it is, it's really not.

You know, your dashboards tend to frustrate your users for a number of reasons. You know, sometimes they lack the interactivity that they need. Sometimes they're just not customizable enough. Sometimes they're just lack the integrations. They might be slow. You know, and this comes out in a variety of ways because you're gonna see this in the form of feature requests that you just can't seem to dig out from under, as well as sometimes customer churn. Right? And those are the things we really wanna avoid. One of the things I always like to tell other product managers is that every SaaS product is an analytics product.

Every SaaS product is an analytics product because if your product generates data, you have a responsibility to help your customers not just analyze it, but use it in an actionable way. Analytics is how customers justify their investment in your product. If they don't have reports and dashboards and analytics to really show that your SaaS product is generating value for the company, you're gonna lose that next renewal. These customers will churn. And no matter what your industry is, data and analytics, is really how they're going to show that your product generated an ROI for that company.

And I like this stat from Gartner, Right? So they say eighty percent of enterprise application vendors compete on the sophistication of analytics. Right? So they've gone out and they've done surveys to actually quantify that vendors sorry, the companies will choose vendors on the back of how good their analytics are. So there is actual data out here to support that. Right? So that's all great and, you know, good. So now what? So what is embedded analytics? Let's just do a quick little level step. Right? We're talking about integrating third party business intelligence style analytics into your software.

Why? Cause it enables end users really to visualize the data that they're generating within your user within your application, and it lets them customize it to their individual business needs. So there's typically a journey of where embedded analytics fits in. You know, a lot of times people start with these charting libraries at great for developers. They can kind of plug in a SQL query, and they can kinda get something on the front end. Then you get a more sophisticated data visualization layer, But at the end, it's still just a front end layer, and it just doesn't really handle the data side of things.

Some people go down this path of trying these legacy BI platforms. You know, because a lot of times they'll offer some, you know, iframed embeds. You can put some dashboards in there. And people will try that, but what they really end up doing is spending a ton of time on development to integrate that into a multi tenant application. So this is where embedded analytics really is about SaaS products. And we'll talk about what goes into that, but there is a legit difference, especially because you got a different user base. And some of this comes back to how, you know, how these things are sold. Embedded analytics is for a multi tenant solution.

Right? So you have different security controls. As opposed to a BI system, which is selling to internal groups. Right? There's a fundamental difference with how they work. And you've got a lot of requirements for embedded analytics here. So self-service, personalization, collaboration — cannot leave security. Right? Security is how, you know, it's not just multi tenancy. It's also user personalization. Row level security. And the last one really is around automation. Right? How do you put that data into action?

So there's a lot of components to what makes a good embedded analytics layer. Right. So if I take these sort of five components down. So self-service. Right? What people want is to edit dashboards. They wanna build some new ones. They wanna use some templates. And, you know, You can't just have every tenant get the same dashboard and expect people to get what they want. Personalization. What are we talking about? Right? Which data do you wanna report on?

Not every customer has the same data model, and not every customer is gonna wanna choose the same data in every report. Right? So you've got some tenant specific templates. You know, don't forget about the mobile side of things. I mean, you probably invest a lot in making sure your SaaS application is mobile ready. Right? And then things like scheduling, you know, those are how you deliver it to people at different points in their workflow. So consistent data models across tenants really break down, right, when your app analytics application forces you into that. Kind of the next one. Right? People wanna collaborate.

They wanna edit things as teams. You know, sometimes they have internal and external sharing. So how do we manage controls with what people wanna do there. You know, scheduling in workflows is really how teams can collaborate, you know, looking at data at a consistent schedule. Right? So and that doesn't really kinda work too well with, you know, a lot of the user based licenses where everyone needs a paid account to log in — that really doesn't scale in a SaaS environment. When we talk about security, right? It's not just who has a login and who doesn't. You know, this is customizing the data that people can see.

Maybe it's a full data set. Maybe it's a partial data set. Right? Maybe it's just certain data within a certain table that only certain users can see. Maybe it's access to dashboards. Right? Maybe you have some administrative level dashboards versus some lower level dashboards. But that native multi tenancy is what really ties us together. So when you have to create individual data warehouses for your different groups or different tables or really do a lot of extra work to segregate it, it really starts to break down. And then lastly, on that automation side, right?

You know, how do you put data into action? If all you're doing is emailing PDFs, it's not really automation. Right? We're looking at, you know, more complicated threshold based alerts. If you can write data back to your data set, you can then, you know, trigger new actions. But sometimes the action doesn't always happen in your system, which is why APIs and webhooks are really important there. Because, you know, sometimes a data trigger will actually trigger a workflow in a third party system. So you wanna be able to tie systems together without writing all that middleware yourself. So where you get into trouble, right?

Some of your common points of failure: data quality. Right? When you're doing this all yourself, you're gonna be responsible for that. Data security — you don't really wanna have to build your own security model. This is really where companies get into trouble. When they try to build this whole multi tenant layer themselves, that just leaves you, you know, really exposed to gaps. Data silos, right, when you can't tie data together, that becomes a bit of a bottleneck and it leads to a lot of people asking you for better download features because if they can't do it in your SaaS application, they're gonna do it somewhere else.

And then relevance. Right? You're also then responsible for keeping it up to date and relevant. Right? I mean, there should always be some kind of data cleansing processes and procedures, but again, do you wanna build this all yourself? That's really one of the biggest challenges that SaaS companies have to face when they go down this road. So kinda with that backdrop, right, of okay, SaaS applications are different — they're not just internal user groups. How does Qrvey actually help address that? So who are we? You know, our goal is to be that embedded analytics layer for SaaS companies.

We wanna take a lot of the components that you would build yourself — ETL, warehouse, a preparation layer, and then front end software — this is years of development. Right? And a lot of SaaS companies, they think they can shortchange this, but the reality of it is, you know, this is a complicated set of software, tools, processes, and Qrvey aims to simplify that for you by providing it in a unique solution. So the three ways we tend to do this differently. Right? So, one, we're all in on the cloud here.

So we built our solution on serverless technology, which is, you know, really about the only way to really give you both scalability and cost efficiency in a cloud environment. Right? So that was, you know, kind of choice number one. Number two, deployable to unlimited environments. You're a SaaS company. You have a software development process, and it's important for your solution to fit inside that software development process. So that means you have staging environments, you have QA environments, you have testing, pre production environments that all need to have the same exact software throughout so that your QA team can really do their jobs.

And then, you know, it gives you that on demand scaling. So that's where that serverless technology really gives you a leg up on making sure that you're only using cloud infrastructure that is required of your application. So number two is freedom to design experiences. So with native multi tenancy that really lets you tailor different pages to different tenants. So sometimes that can be useful for rolling this out, you know, in stages over time. Sometimes this can be, you know, when you add this to user tiers or it's a premium add on.

Right? So some tenants will have it and some won't. So that end user personalization means you can tailor everything on single pages — from which data that they can see, different filters that they could see, all the way down to individual charts and dashboards. But two, everything is embeddable. So what we mean by that is it's not just, you know, whole page dashboards; you can tailor this so that you can mix and match, you know, widgets from our system and widgets from yours. And I'll demonstrate that as well. So you can see what that really actually looks like in practice. And then you need control where it matters most. Right?

So deploying to your cloud means you get the ultimate control and security. So this is gonna inherit your security policies that you already have in place. So this is a really important part of why we want it to be part of your cloud and not a third party cloud where you would send data to. So this lets you keep it in place. It's the best way of doing data security. You know, and then, you know, with those granular multi tenant controls, you can really tailor that as well, to exactly what it needs to be. So we're not forcing any specific models; your system always remains the system of record, and you simply pass in, you know, security when you need it.

So we really took a holistic approach and looked at where SaaS companies need a lot of controls there. So how does this look in practice? I'm gonna flip over to a demo here, and we'll actually kind of go through that with that backdrop. And I will, and we'll talk about this as we're going along. So a demonstration I've prepared here. So this is a SaaS application. And right off the bat, this application along with many applications, you tend to have a dashboard when people log in. So this is what I was talking about, designing experiences.

So this page is a mix of Qrvey widgets, and widgets that belong to the application. We didn't wanna have to tie you into just, you know, you just do a dashboard, and that's it. You know, if you wanted to offer different text blocks or different ways of showing data, you've got a lot of choices here by giving you different options. So here I'm clicking on an individual chart filter. Right? So if you wanna offer drill-ins right there from these individual widgets, it's a very simple and easy thing to add to any chart. You know, as I'm scrolling through here, looking at the different tooltips, I'm giving people data right when they log in.

That might be the most relevant to everyone. But then, you know, next screens will kind of show how we tailor some of that through there as well. But you can see a mixed and matched widgets on this particular screen. So going over to another dashboard. So this dashboard, I'll spend a little more time on this one. So this dashboard here has a fully embedded dashboard. So these are not individual widgets. This is actually a full dashboard, but I've tailored the look and feel to fit inside my application. So we're gonna walk through some of the features that, you know, we offer with our out of the box solution that you can then offer to some of your SaaS users as well.

So as we're scrolling down, you can see, I've kinda mixed and matched layouts. I've got a responsive grid. So this will, you know, kind of shift and move around depending on the screen size here. Looking at this first widget, obviously, I'm scrolling over. You can see I'm mixing both a cumulative sum, which is the line chart, as well as, you know, individual day data. So you've got different ways of presenting the same data on the same chart. You see the dual y-axis there as well. A lot of companies like to do that. So you can kinda show relative performance between two different metrics.

We've got things like reference lines. If you have annual goals that you're working towards, you can highlight that pretty easily. And that stuff can be customized on the fly. You know, I put this into my dashboards, but this might be different for different tenants, and we've got different ways of customizing that as well. So you can see a lot of the different charts. That's a cumulative number when you're working towards a goal. Even though you're getting monthly numbers, it's still a running total. So the user doesn't have to do the math in their head on the fly.

Doing things like small multiples right here when you wanna break down categories of data, using the same type of visualization, which is why you see four bar charts. They look very similar. But it's actually one widget using a small multiples method to break down — in this case, I'm looking at payment methods over different treatment types. And then we can't forget tables. Tables, you know, they may be one of those little things that people sometimes overlook, but I tell you when we talk to customers, tables are oftentimes one of the most important visualizations that we walk through with them.

So you got full control over the look and feel. We've got multi column sorting natively built in that you can turn on or turn off depending on what you like. But again, I've tailored that look and feel to my application there as well. So that's kind of like an overview of, you know, one dashboard here. So we've got a couple filter options in here as well. So the first thing I'll show you is if you wanna see the data behind any given chart, you can very quickly click on that link that says go see the data table. This will give you the raw data behind any given chart that you're looking at, which is always a great way to surface a little bit more behind individual widgets.

You can select the columns. You can do some filtering from in there as well. So you got a few more actions. What I'll also show you is you can also do different types of filtering. So that first one, I've only filtered that one individual widget. So the only thing I've done there is change that one monthly visits just to isolate that September number. So nothing else has changed at this point. You notice all the metrics have stayed the same. All the breakdowns are the same. So I'm gonna go ahead and remove that filter from this individual panel. So we have panel, global, and dashboard filters.

And then I'll go over to another chart and we'll kind of demonstrate how in the same dashboard I can then configure that particular widget to filter the entire chart. So now you'll see all of my data has changed based on that one segment I've selected from that pie chart. So these are the types of customizations you can start to offer. These are the features that a lot of our customers like to see because we know that people tend to look at data differently. People a lot of times just have different flavors. They have different processes.

They think about things differently. So that is not a panel filter this time. So that becomes more of a dashboard filter. So I'll go ahead and remove that next. And we'll get that off there. Yep. So now you'll see I've got a dashboard filter up top. We'll get that. We'll get that in there. And then we'll take that off and that will go back and reset the whole dashboard. So mixed a bunch of visualization types on here, whether it's your typical bars, pies, but we have things like heat maps and maps as well.

You can always do more complex filtering on an individual widget. So these are like the different time frames. If I wanted to, you know, take anything off from there, I could. I just wanted to do that on the individual widgets. We can do that there as well. So you'll see a handful of options down there too. So when we talk about customizations, so that was you know, one way of doing customization. I kinda show it this way too — this is roughly the same dashboard, but this is a dark mode. If you have an application that has a fully kind of dark background and you've embraced this dark theme.

This is just another way of looking at it. So to show you that you can customize this with both some CSS overrides, you know, on the embed. We do support theming as well. So when you really do wanna take this, tailor it, make it look like your application as much as possible. You have very easy tools to do that. And you'll notice nowhere on there does it say powered by Qrvey. This is a fully white labeled solution. So you won't see our brand anywhere on any of these embeds. So this is meant to be your application and to look like your application.

Now you see I've got three buttons in the top right corner: view, edit, and a create button. So when we talked about, you know, you wanna be able to offer more to people, not just a static dashboard. So you can do again all of this with embedded widgets. So this is all still within a SaaS application. If this user who's logged in has permission to edit, you can offer them an edit button of your own. Those buttons belong to the SaaS application, not ours. But from here, you get our dashboard builder.

So you can add charts and images, text, and buttons on this. So it doesn't just have to be charts and widgets. You can actually add some context — if you wanna support that more of a story type format, you can do that too. From there, you get the full editor. And you can do this with individual charts as well as full dashboard editing too. So from here, you know, this is where you can, you know, do something simple. Like, I'll just change the sorting on this one so you can see something is visibly different. But you get all the full chart controls there as well, with an asynchronous save in the background.

So I hit view, kinda went back, went back to that first step. The next phase, what we'll click on is the create button. So that was an edit of something that existed. Now you have a full create if you wanna give a blank canvas and start from scratch. So I've got over here on the right hand side, I actually have charts that I've already pre built. So users with the right permission set could actually use something that has been pre built and plenty of people do it that way.

Right? Or they wanna build from scratch, and that can go both ways. So the same chart editor that you saw as part of the edit function is also the same one here for building from brand new. So you've got different, all my different categories of data, right there. It's a simple drag and drop interface where people can drag to the either the x or the y axis or they can drag right off to the categories and values procedures there too. I've got full editors over there. If I wanna change, you know, the orientation of my chart, maybe I don't want those data labels in there.

And you can see I've got a few ways of customizing it. I mean, you can take a lot of this stuff out of the box, but for some more advanced users who like things in a certain way, you know, you've got some choices of what you can offer people there. So all the way from looking at different chart types as well, switching over to a multi series chart. You know, if I've got two different metrics, I can put that second one on another y-axis, change the line, and that very quickly gets you to, you know, being able to display two different metrics on one chart. Give it a little contrast with a darker color, and you're on your way.

So from here, you get a lot of, again, we have a lot of customization options built into this, but a lot of times, you know, the out of the box is ready to go. You can filter this one chart so it always uses a filter right off the bat — it's just that I wanna only look at data from this year. You know, that's a very simple way of doing it right here. At the time of build, so this won't get changed later on. So if you really wanna lock it into that, you can do it. We also support custom formulas as well as custom groups and custom buckets.

I'm not gonna demo those today, but we do have support for that as well. Formulas are obviously something more for advanced users. But now I've got a second chart. I can line it up, you know, moving around my responsive grid. And, you know, you've started to build a new dashboard. And again, you might have publishing workflows. Maybe this has to go through somebody before it actually is a live dashboard. You can get people their own kind of 'my dashboards' areas too. So you got a lot of choices is how you publish it. Flipping over to a full page table — we talked about tables as part of a dashboard, but this is also another area where a lot of times companies will stop building their own.

Tables and offer some more complex ones. So what I've done in that top right hand corner is I've built a filter into the SaaS application. So that's not a filter that's native to Qrvey. But what it demonstrates is you can have your own kinda look and feel if you wanna do complex filtering. You can do that within your application. You also get the multi column sort, so you can offer that as well in there, at the same time. So you can really start to, you know, modify, change these depending on how you wanna do that. So if you notice the visit ID has a unique number.

So what I'm gonna do here is actually jump to a transaction that's linked to it. So we can link charts together. We can also do like a hyperlink type chart. So what I did was pass that visit ID as a URL parameter, and it took me to a screen where I was able to jump right to, you know, some kind of a transaction value. So the last part I'll show you is automation. So automation is, you know, another embeddable widget here, and I kind of embedded the whole experience just to do more of a demonstration on kind of the whole widget.

A lot of times people do their own list with our APIs. But for the sake of the demo, we embedded the whole thing. So to show you that you can have multiple automations for any given user and account. But, again, these are more nested automations where you can do if-then statements where if you do need to have, you know, custom conditions, nested conditions — you've got your, different ways of sending it out, email, SMS, directly to Slack. And this has been a little more of what we kind of talked about earlier. So you've got different data actions around searching, updating, inserting records.

So automation can be pretty complex from there. So kind of just tying it all back here. So a lot of what we do at Qrvey really is about that experience. So that's kind of a quick walk through. It's about twelve minutes of what we offer here. There's a whole data management layer to this that I'm not gonna show today that we can show at another time. This hopefully gives you a sense on the types of user experiences that you can build within a SaaS application using Qrvey's embedded analytics. Enough about Qrvey.

Let's talk to Aaron. So we're gonna bring Aaron in here now that I've kinda set the stage and walk through a little bit about Qrvey. And, you know, it's always sometimes better when you hear from somebody else, so as much as I'm sure you love hearing vendors talk. Sometimes it's better here. Other people talk here. So we've got five questions with Aaron. I'll let Aaron kind of introduce herself and her company. And then we'll get right into the first one. Hi. I'm Erin. I'm a product manager here at Resolver.

Resolver is a highly configurable risk management and corporate security software, and we're on a mission to empower our users with risk intelligence to help them drive proactive strategies and deliver value to their companies. A recently launched embedded dashboard feature is critical to that plan. As a product marketing lead, I loved that you were on point with that company message. But let's let's kinda talk about this. Right?

So you went down a path. Right? You had to make a decision at some point that it was either gonna be buy something or maybe you continue building — what were some of the factors that kinda played into this decision? The most important driving factor was focusing on what we do best. We have a lot of deep expertise in the corporate security compliance and risk management space. And we really wanted to partner with a vendor who could help us deliver sophisticated business intelligence at scale.

With the added benefit of decreasing time to market, you know, we were able to partner with you to develop, like, a far more sophisticated embedded dashboard offering than we could have built ourselves in the time that we spent embedding it. Yeah. That time to market is a common one. We hear a lot of people think it's shorter to build than to integrate, but it usually doesn't pay off in the long run. So, yeah, we definitely heard that one a lot. So question number two here. So looking at some of the use cases, you know, where did you find that offering that embedded analytics — that more advanced experience — had some higher impact.

Well, the important thing to recognize about our customers is they're on teams typically viewed as call centers for their business — internal audit, security, loss prevention, threat detection. Right? But we know that they bring a lot of value to the table. And we wanted them to help convey those powerful messages to their own stakeholders. And instead of just managing the risk, how can we proactively spot emerging trends and develop strategies to mitigate them?

And that's something we're really excited about dashboards for. That's great. Yeah. Trend spotting is always an interesting one because that means something usually means something different to different customers within an application. So, yeah, I definitely agree with you on that one for sure. Now these are obviously never easy decisions here. So the technical considerations are always a big part of any evaluation. What were you guys kinda looking for to really make sure that, you know, you were offering that better user experience.

That's a good question. Going into this journey, one of our biggest challenges is our data model. We have a highly flexible data model that can change on a dime depending on what our customers need. So we needed to partner with a vendor who could support changing data models and custom data models per tenant. Another consideration was security — working in this corporate security space.

It's highly regulated. And working with globally distributed teams, we need to be compliant with the most stringent requirements. Because we were able to deploy Qrvey within our AWS environment, we were able to deliver on those promises and guarantees we have to our customers. And then just building on that a little bit, our early research indicated that customers would lose trust if they felt they were leaving our platform or engaging with a third party vendor, especially with this kind of highly sensitive data. Because we were able to offer that really frictionless embedded experience, the dashboard builder, and dashboards themselves.

They look and feel like our product, and that's because of a lot of the white labeling features Qrvey offers. Yeah. You really hit on an important theme there, and I'll highlight it is that trust theme. Right? I mean, I know you guys really have that, but I mean, we see this in a lot of industries, especially when you get into like health care, financial, cyber and risk for sure. But we play a lot of those industries that really — trust is an important one. That data model consideration is a unique one for sure.

And I know you and I have talked about what goes into making that work on your side there too. But, you know, it's one of those little things that don't often come up in trials. You know, when we work with customers, they kind of overlook how important the data side is. They think their data is ready, but a lot of times they're not. So it's always interesting to hear that — especially it's good to know going into it. It's a lot cleaner. Which you'd be surprised at how many people start trials and evaluations not understanding the state their data is actually in and what they're gonna need it to do in future.

So I'm highlighting that one further. And anybody else that's thinking of going down this road, that's a really important consideration. It's not just the charts and dashboards, but, you know, where your data lives and how it sits and what you're gonna need it to do is a pretty important side of that too. So here we go. Number four. This is where you get to, you know, put on your expert badge. So what kind of advice would you give to other companies that are maybe thinking about embedded analytics? Thinking that, okay, it's time to stop building this ourselves.

You know, any pitfalls that maybe you guys learned along the way, but just, you know, you could put your expert badge on and what kind of advice would you give to other people? My first piece of advice would be to take a look at your data warehouse, make sure that you're storing your data in a way that's highly usable for your dashboards, easy to maintain and in the right format. Second tip — when you're entering proof of concept with any vendor, make sure you have a really strong rubric documented that all your stakeholders can agree on.

This came in really handy for me when we had to kind of not move forward with the vendor. I had some really great documentation to provide them and kind of be that great customer. And then third consideration and a challenging one: how are you going to maintain your data? How are you going to keep it in sync? And how are you gonna delete objects that shouldn't be included anymore, and how are you gonna make sure this is performant?

So a good plan going into the evaluation is my overall goal. Yeah. You know, we say this to other people. It's not like, how good is your bar chart? How good is your pie chart? No. The data side is really important. And this is to be fair — you know, one of the reasons that Qrvey from the very beginning has always included a data layer. You know, our founders have been in the analytics industry for a while, and I think they have no shortage of direct experience.

Like, when data is not ready and doesn't perform, it doesn't really matter how good the front end is. Right? If there's no data, the user experience just falls down. So I think that's great to hear. And hopefully somebody took some notes on that one. And then lastly, and then, you know, this is kind of we get to look into our crystal ball here. Any trends in the space that really gets you excited. Things you're looking forward to. Yeah, look into your crystal ball, put on your wizard hat. You're a Harry Potter camp.

Would it even be a conversation about SaaS products if I didn't say artificial intelligence? Not in 2023. So definitely AI. But like, one challenge we've come across is our customers are not necessarily data scientists. So business intelligence has quite the learning curve and that's one place I think AI could really boost us — how can you get the perfect data set on your first try instead of this trial and error process that they go through today?

I also wanna say cloud infrastructure. There's some really exciting emerging technologies that make data storage and maintenance way more efficient and way more powerful and scalable. So, those are two areas that I'll be looking into in the coming months. Yeah. AI is a big one. I think we're getting ready to launch a beta, you know, kind of along the ChatGPT / OpenAI lines there too, to really help with some of that synthesis. Right?

I mean, they can just spot those trends a little bit faster. It's an exciting time. I mean, I know the security side of things has gotta be first and foremost — making sure that data always stays secure. But you're right. I mean, I think AI really is the biggest opportunity in the space that the analytics industry has seen in a while. I mean, machine learning's been around for a while, but I think, you know, democratizing AI — it's a pretty exciting one. So I know we're looking forward to it as well on the Qrvey side of things.

So cool. That kind of brings me to the end of that Q and A. That was a great little segment there, Aaron. I appreciate you joining me on that one. My pleasure. I think we've got Doug who's jumping back in here. I am. There we go. I'm here. And and, I actually wanna, while we have Aaron here handy. I I love the fact that you brought a customer to the show. And I'm a simple man, and not technical. I'm the host.

But I host a lot of these. And I see a lot of solutions. And I've seen a lot of trends, but I'm curious — with regard to Brian's presentation, which I thought was extremely well done, and also extremely good looking. The whole presentation layer of it — I wanna talk a little bit about that because in your profile on LinkedIn, Erin, you talk about how you are a fan of great design. I'm curious how much, when you were picking a solution, it mattered how it was going to look to the end user.

It was essential. Our dashboards are such an important part of the customer's journey and helping them really influence strategy at their companies that we knew that we needed to deliver a highly polished, professional looking, boardroom ready dashboard. So it was one of our number one criteria. Well, and along those lines, I'm curious to get both of your opinions.

Do you see that affecting the way the end user interacts with a solution, where they're not only can they trust the data, but they can kind of interpret it a little more easily because it's graphically presented. Yeah. So I think you do a couple of ways to kinda answer that question there. Right? And, you know, when you're dealing with multi tenant SaaS applications, it's never one size fits all. And I think that really is kinda highlighting what Aaron was getting at with a number of her users there. You know, she really highlighted the fact that they've got a different data model for different customers and different tenants.

Right? That has to translate into a front end user experience. So, you know, the one size fits all static dashboards really have a challenge with that requirement because the way you filter and slice your data could be different than your colleague, which can be different than colleague number three, that might all be in the same tenant. So the front end really has to have the different options to either, you know, support it out of the box. Right? So that means they can map to user permissions and controls.

So when the page just loads it, you know, it loads with what that user is eligible to see, as well as taking it a step further and saying, okay, this is a good start, but I wanna go take it down a different lane. And that lane's only gonna be for me. I don't know if Aaron you wanted to add anything to that one there, but, you know, that customization, we see it in multi tenant applications. Yeah. It's very important. The only thing I would add on is the design itself of the dashboard is just as important as the UI.

So we've invested a lot of time in training and enablement on our end. Can you talk a little bit about the elements of that team that you kind of joined with, and kind of how the product came together both between companies. I mean, what was the account management experience, that sort of thing. Oh my gosh. Qrvey's been amazing. We have a full team on their end that we have almost daily conversations with — at least during the development phase.

And a lot of you know, our development teams are very closely in touch, and working together. So it truly is a partnership. And it needs to be. Right? Because while we're offering a solution that has plenty of out of the box, every SaaS application is different. I mean, how Aaron and Resolver architected their system is gonna look different than our other customers — from databases to middleware, to even API structure.

So, you know, it's important for us to invest in that because that's what it takes to make our customers successful. Yeah. So Brian, can you talk a little bit more about that initial process as you're talking to a brand new prospect and trying to understand their needs and and how you're going to deliver that sort of integration for them. Yeah. I mean, a lot of the evaluation criteria.

A lot of things we're looking at here. One, so obviously is on the data side first. So we have our own data layer which can support the SQL and the NoSQL of the world. Right? But it kinda starts with where is it coming from. Then a lot of times you're looking at what's been done already. So I alluded to this earlier — not every data warehouse or not every data lake is created equal. So how ready is it to kind of ingest into our system or into what we provide into our system.

There's a lot of discussion on that point to be frank. And, you know, that's usually where we keep technical experts on hand to really help with that mapping. We come across customers that are doing some very sophisticated and complicated manipulations of data on their side. Sometimes it's really good. Sometimes it's just what they had to do. So there's kind of this best practices approach. We try to be, you know, a partner, a consultant, a mentor to them too because some are really ready. And some just need more help than others.

So we do things like — every trial prospect gets a dedicated Slack channel with us. So we give people direct access. It's not support tickets. We give people direct access to Slack. We're with them the whole step of the way on the trial process. And we make sure that they do a legit trial — it's not the right word — it needs to mimic what it would look like in their environment. You know, we don't wanna just rush people through this kind of quick. Here's our board builder, you know, that looks good. No.

We want them to hook it to their data. We want them to hook it to their user permissions. We want them to do it right because we want people to be successful. I mean, if we're not the right fit, that's fine. We're not the right fit. But if they rush through that stage, everyone will look like the right fit, and chances are the one they choose will not be. So we're pretty hands on throughout that whole process. We get dedicated Slack channels, success managers, access to our development team if that's what it requires. I'm curious about definitions, because we do see a lot of things here, and we do have a fairly broad audience.

And I wanna make sure that folks understand the difference. Can you just dig into the difference a little bit between embedded analytics and embedded BI. Sure. You know, we've written some blog posts about this very topic over the years. You know, embedded BI, the way we've kinda defined it really is taking your traditional BI players — without naming names, I'm sure everybody can come up with three or four. And really taking a solution that was built for internal usage, business groups, data analysts, and trying to somehow force that in.

So it kinda uses the BI term 'embedded' more often than not — it takes the form of just a static iframe. I mean, everyone uses the term embedded analytics, but you'd be amazed at the differences. So embedded BI really is like kind of these legacy systems that are trying to shove it in there. So sometimes it's only iframes, right, which means you're not getting into that granular widget. The white labeling is sometimes really limited and very inconsistent. So in this whole part of building trust — like, it's very obvious that it is not your software.

And that really kind of flies in the face of, you know, embedding something in your solution that's supposed to be a seamless part of your solution. And then it breaks down with multi tenancy. The legacy BI systems just weren't built for multi tenant out of the box. So a lot of times we get customers that went down that path and they really struggled with the development cost of making it ready. So embedded BI was really kind of taking your traditional business intelligence solutions and trying to offer them an embedded layer where, you know, some people will make it work.

Don't get me wrong. But for the most part, we find anybody that's kind of gone down that path and really struggled with it — it usually falls into one of those buckets. It's either the multi tenancy gets them into trouble, the security model gets them into trouble, sending data to a third party cloud — sometimes that becomes a show stopper. So that kind of class of solutions, which we call embedded BI, typically just falls down in an embedded analytics for SaaS application scenario.

And so we do want everybody to understand and we would certainly encourage everybody watching this to go to the qrvey.com site and see everything that they have to offer there. Lots of resources available. And, Erin, I'm curious — with regard to that, what did you so you clearly went through a consideration cycle. You considered other solutions, and it sounds like you may even have been fairly far down with some of those solutions. How did that work for you?

I mean, how did you, when you decided as a team, you were going to look for a partner for your embedded analytics — can you talk a little bit about that process and what, if anything, you needed to kind of recognize internally prior to being able to really engage in that ultimate buying decision? Yes. Well, it was a big commitment for us. We were in the evaluation stage for six months.

I had a full dev team dedicated to evaluating business intelligence vendors, which is expensive. But we really wanted to make sure that we are picking the right partner and a technology that would scale with us for the years to come. And the actual evaluation itself ended up looking like building some very experimental code and prototypes to make sure that this technology would fulfill all of our user requirements.

So it was quite intense. And, Brian, is that what you see typically in these engagements that you're entering into? It is because the ones that go down that path, you know, it's not only that they take it seriously. They do have a better handle on, you know, what the commitment is to to really take this next leap forward. So, yeah. We do see people doing that. I mean, when we go down that path, customers, you know, we can really help them be successful. But that's where the rubber meets the road. Yeah.

So I know you asked Aaron, but I'm gonna ask you the same question with regard to the build versus buy decision — when you're talking to somebody, and they say, oh, we can just do that ourselves or they're not necessarily convinced. I mean, what are the triggers that you're kinda leveraging there? Yeah. So usually the signs that you're kind of ready for this. So, one, if you just have feature requests that are piling up, like customers are constantly asking for something custom, or they're really pushing on you for more downloads.

Downloads are a good sign that, you know, they're asking for something custom — they're just not doing it in your system. So those are two things that product managers really can see. Right? Those are good signs that your product is falling down more than anything. Sometimes the cost of maintenance, you know, you're spending just too much roadmap time. You know, some development shops, they just spend too much time on the analytics and, you know, where you build it upfront, it may not look like super intensive to build static dashboards.

But the cost over time of incremental improvements, it becomes a regular part of your roadmap. And when you're constantly dedicating time to this, at some point, hopefully you ask the question of there's gotta be a better way. You know, that's what we see a lot. And it's common for startups — they get something up there really fast. It works for a little while, but at some point, they're gonna outgrow that. Sometimes, you know, hopefully customers don't leave them, but once in a while we do see churn issues.

I mean, churn can show up. You know, the quality of your dashboards can be a return reason. And we've a hundred percent seen it. So let's take that one step further and talk a little bit about ROI. So how would somebody think about measuring ROI? What are maybe some of the common ways that people can think about measuring ROI? Yep. So people will look at, you know, using this in a couple of ways. But some customers will say, this is not a cost center.

This is a revenue center. So in that particular case, ROI looks at, depending on how they wanna monetize it. So some companies monetize this with either premium add-ons on enterprise licenses. Right? So kind of annual add-ons to a more expensive product — and that's a matter of how often are you selling them. Sometimes they will offer them as part of premium user tiers. So if you sell seats and maybe you have multiple tiers, sometimes your upper tiers will get access to some of those more customization features or the build-your-own.

Build your own dashboard features. So that's an easy way of monetizing it that way. If you're not monetizing it, it tends to show up in CSAT scores. So customer satisfaction. You know, a lot of times you do legitimately get higher NPS scores if that's how you're measuring it. We've seen that show up there plenty of times. And those are more of the customer facing sides. Internally, you're looking at, you know, development performance. Right? Are you dedicating less roadmap to it? Does that mean you're freeing it up? Which is a little harder to measure because it's a bit of an opportunity cost.

Type question there. Right? Because if you're spending all this time on analytics, well, what aren't you doing? So if you're spending less time on this after you get first implementation done and get it rolled out, you know, what is the benefit and what's it freeing up for you to really focus on there as well. So typically ROI comes in one of those three buckets. Hopefully, you see them, you know, maybe all three. But, those that monetize it, you know, they're definitely able to go right to it. And we see this, you know, pretty regularly. We have customers that done it in the two ways I've mentioned.

We have customers that have just used it as a justification for an annual price increase, you know, just across the board like everyone's getting it, and it's part of the justification. And, you know, the SaaS industry is in an interesting time right now. You know, we went from that kind of interesting COVID boom to everyone's, you know, looking for a little bit of extra revenue. So we definitely see companies looking to monetize this a little bit more than we had in previous years. Yeah. Erin, are there any thoughts on the ROI question on your end?

Definitely a lot of what Brian said. There's some product metrics we're looking at, looking for decreased data exports — indicating that folks are able to achieve their goals in our system. And also looking to move up market with more executive personas using our product. Yeah. Great. The last question I'm gonna have to ask because you guys touched on it a little bit, but I mean, let's let's talk about AI, at least a little bit.

I know there's — and this is, you know, probably a little outside the box because, you know, I'm sure you're both having lots of internal discussions about how to present this to clients and customers and how you're going to integrate it as well. But can you talk a little bit about what you see as the opportunity — I don't consider it a threat, but I'm curious what what you guys see as opportunity around it. My main goal is to make insights highly accessible to our users.

So if there's a way for us to leverage artificial intelligence to make the insights more impactful or easier to find, I would be excited to explore those options. I agree with you. I think the ease of finding the insights, trends, outliers that sometimes took a few extra steps — I think those are some of the biggest opportunities for your average user. I mean, your advanced user may have been dabbling in machine learning probably for a while, but it's the average user that's gonna get the biggest benefit from this.

So whether that's asking questions in natural language queries or simply feeding in some information and it just can find the trends, you know, in a matter of a few seconds. So it's an exciting time for sure, and you're right. It's not a threat — it's an opportunity. Absolutely. And insights, by the way, is one of our favorite words. So we'll end with that. This has been tremendously informative. Appreciate the time. Appreciate you, Aaron, coming along. Brian, thanks very much. And obviously, best of luck with Qrvey and everything you have coming up in the next year.

Alright. Thanks for having us. Thank you. Well, there you have it. Another solution in our spotlight. We want to thank Brian and Erin Peck for that fine presentation, and we appreciate your participation as well. Until next time, I'm Doug Atkinson here in Solutions Review. Thanks for watching.