Whether you have a desktop application competing with SaaS newcomers or you’re working to SaaSify or hybridize your on-prem offerings, software usage data is critical to making informed roadmap and customer lifecycle decisions.

In this Industry webinar on product analytics for desktop applications, hosted by Mike Belsito of Product Collective, Dan Barrett and Michael Goff from Revenera discuss the importance of understanding product usage analytics for both on-premise and SaaS applications. They emphasize the need for product analytics to inform pricing and packaging decisions. The speakers also discuss the technical aspects of deploying a solution for product analytics in desktop applications and the pros and cons of buying a turnkey analytics solution versus building one's own. Overall, the webinar emphasizes the importance of collecting data to gain insight into how products are being used and to make decisions that improve the customer experience and deliver value.

Transcript

Mike B. (00:00:04) - Hey, everybody, this is Mike Belsito, co-founder of Product Collective, and we're back for another industry webinar, I hope wherever you're tuning in from. Uh, I hope you're having a great day. I am actually in Cleveland, Ohio right now, and not gonna lie, it's like 80 degrees and sunny. It's a beautiful day outside. So, um, that's all nice, kind of puts me in a better mood when, when the weather's nice. And so I think we're gonna have a great session today. And, uh, so what I wanted to do before we actually get the session started is just give a little bit of a, a lay of the land of what these industry webinars are like and, and what you can expect and, and sort of get out of it. Um, we will officially get started in just a couple minutes, but, um, if this is your first time attending, you know, these are really opportunities for you to not just learn, um, you know, about more in your role in product, but also get to interact a little bit.

Mike B. (00:00:57) - And so you'll see at the bottom there are, there's a chat feature, there's also a q and a section. Um, those are tools for you to sort of add to the conversation. So in the chat, for instance, um, maybe you heard, you know, you're seeing something in the presentation, maybe you heard a comment you wanna add to it, put it in the chat, you know, add to it so you could share with your fellow attendees. And in fact, right now, if you wanted to put maybe where you're tuning in from in the chat, um, go ahead and do that. I'd love to see where, where people are kind of coming in from. What I will say is, when you do that, make sure you write to all panelists and attendees. It kind of gives you an option of all panelists or all panelists and attendees.

Mike B. (00:01:35) - If it's all panelists and attendees, everybody can see, um, what you're kind of adding to there in the chat. So definitely do that. Um, you'll also notice there's a section for q and a. Now, this is a chance for you to ask questions throughout the entire session. We'll, we'll take most of the questions towards the end, but when you have a question that pops up on your mind, by all means put it right there in the q and a section. That'll be a great place for you to, um, kind of load it up and, and I'll make sure that we, we take your questions at the end as well. Um, so the only other thing I wanted to add here is that I will be emailing everybody afterwards with a recording of this session. I'll try to get that out by the end of the day, um, so that, you know, if you, if you miss something or you know you want to go back and see it, you, you'll have access to that.

Mike B. (00:02:22) - And so, um, know that that will be coming. Um, by the way, in the chat, I'm seeing that there are people from all over the world here, all the way from Estonia, London and the UK. Uh, so thank you very much for being here. I hope we're, you know, London and Estonia. I hope, hope the weather's nice there too for you today, um, as it is in Cleveland, Ohio. So, with all that, I wanna get right into the session. Now, the session, again, we're focusing on product analytics for desktop applications, and we have two folks from the team at Revenera, Daniel Barrett and Michael Goff, and they're here to help us walk through this. They're here to help us learn. Uh, really at this point, I'd love to turn it over to Daniel, Michael and, and they'll take us through this.

Dan B. (00:03:04) - Great, thanks, Mike. Hello everyone. And welcome. My name is Dan Barrett and I'm a solution engineer here with Revenera. And I've spent the last five years supporting our products that are focused on usage analytics. So I've helped customers big and small and all sorts of various different industries, build and grow out their usage analytics programs. So I've worked with individuals in, in all sorts of different roles as well, from product management, uh, all the way to software compliance as well. Uh, and today I'm joined here by Michael Goff.

Michael G. (00:03:32) - Thanks, Dan. So, uh, I am a principal of product marketing here at Revenera. Uh, been here a little bit longer than Dan. Um, again, with a strong background in usage analytics, but, uh, now as part of Revenera a focus on software monetization as well. And, uh, definitely looking forward to having a nice conversation and talking about product analytics or desktop apps today. And Dan, I think we were gonna start with a poll, right?

Dan B. (00:03:58) - Great. Yeah, so let's do it. So, you know, we've learned a lot as our business has evolved over these past few years, and certainly we're gonna be talking about some information that we've learned on product analytics for desktop applications. Uh, so you can see there's a poll question here. Um, how are your applications deployed today? Are you mostly on premise or mostly SaaS or kind of, uh, you know, an even distribution between each? Um, so again, the question is how are your applications being deployed today? And I know that, you know, certainly the title of, uh, of today's presentation is product analytics for desktop applications. So, you know, maybe we'll, we'll see some answers skewed towards, uh, the, um, the first option there, mostly on-premise or desktop applications. But, um, you know, perhaps we have quite a few folks that are an even mix of on-premise and SaaS as well.

Michael G. (00:04:47) - Yeah, we're definitely seeing a lot more folks, uh, serve with hybrid deployment solutions. Uh, we'll be talking about that a little bit as, uh, we move on to the next slide after we see the poll results.

Michael G. (00:05:01) - Okay, so we've, we've actually got a pretty good mix. So we've got 33 OnPrem mostly on-premise, 39% mostly SaaS, and about 28%, uh, roughly even mix of on-prem and SaaS. So with that, you know, let's talk a little bit about why we're here today, right? So we're, we're talking about desktop applications, we're talking about product usage analytics. Why? um, well, obviously, you know, SaaSification, is not a new concept to most folks. Uh, IDC has predicted that by 2024, 51% of total software revenue will be from SaaS delivery models. That makes sense. Um, and then, but we're also seeing that on-prem software really is only shrinking at about minus 2.4%. So it's really not going away anytime fast. Um, and I think one of the challenges for on-prem software vendors is making that decision. Are we gonna compete with SaaS offerings or are we gonna migrate to some form, some form of SaaS solution?

Michael G. (00:06:06) - And we actually did our own survey, uh, not so long ago. This came out last fall. Uh, we're wrapping up, uh, collection of data for our next one, uh, this week actually. Um, but we did find that not surprisingly, uh, a lot of vendors are going to be increasing their, uh, SaaS deployment models, uh, over the next 12 to 18 months. But more importantly, when we look at the on-prem, um, 41% no change, 24% are actually increasing. So I think the takeaway here is on-prem definitely still matters, and it's gonna be really important to understand how your products are being used, uh, so you can make those decisions in a much more informed way. Um, now of course, it depends on what type of application you have, right? So if we look at this also from IDC, uh, you see that on the left side of the spectrum, we have collaborative applications on the right side, we have engineering applications.

Michael G. (00:07:03) - Um, it makes sense that a collaborative application is gonna be a lot better suited for a SaaS, uh, model. Um, whereas engineering software probably requires a lot more, uh, hardware requirements, um, may not make the most sense right now to move to SaaS, but the bottom line here is your journey may be faster or slower, uh, depending on where you sit in this spectrum. And then one other piece I wanted to bring up, uh, is a subscription model. So obviously subscription isn't necessarily the same as the SaaS model, um, but sometimes they get used interchangeably. Um, and one of the interesting things here is that we do see, uh, again, from our monetization monitor survey, that over the next 12 to 18 months, people are going to be growing their subscription model, also gonna be growing their usage based models, which is another reason why product usage analytics are gonna be helpful.

Michael G. (00:07:59) - Um, but the other thing I wanted to point out about this is, is when you move to a subscription model, when you move to SaaS, uh, you're changing the expectation of your customers. Um, and that's gonna have an impact on, on-prem too. So, you know, people who are in a SaaS environment are expecting regular updates, you know, continuous improvement, um, perhaps even some insights into their own usage of the product. So even if you're sticking with on-prem, you're gonna wanna keep an eye on, on some of those things and those expectations that customers have. Um, and you're gonna wanna be able to meet those. Um, and again, you know, if, if you are considering a subscription model, uh, even if you're not SaaS, some of those expectations go along with that.

Michael G. (00:08:45) - So we're here today to talk about product usage analytics for desktop applications. Um, but let's ask the question. So why is usage data so important, and arguably, why is it even more important for desktop applications? Um, as, as we see here, people use products in a lot of ways that you might not expect, um, in a lot of product measures, you'd talk to the, the team and they're like, oh, yeah, we know how people use our products. Yeah, not always. Uh, I think the people that made this, uh, coffee pot did not expect that people would be boiling eggs in the coffee pot. Um, which sort of brings us to our next slide. Uh, a quick search of the internet yielded this result. 17 surprising foods you can cook with the a coffee maker. Um, the question is, should you, um, and then from, from a product person perspective and a software monetization per perspective, should you monetize that, right?

Michael G. (00:09:38) - Is there a use case that you could monetize? Obviously, uh, if you have a coffee maker, you probably don't have that type of insight, but you have an on-prem software product, um, you can still collect some of that product usage data and decide whether or not one of these use cases is actually really interesting. Also, whether or not there are enough people doing it that makes it monetizable. Um, and, and whether or not you should look into that. Um, and if you folks are familiar with April Dunford, she's a product positioning expert. She has a book called, obviously Awesome. Uh, she shares in that book a story about how she was working for a company that sold a database and it was really a hard sell. And, and it was really hard getting people to buy it, and they were struggling. And they actually talked to a customer, and the customer said, you know what?

Michael G. (00:10:25) - I don't even think this product is a database. I think it's a data warehouse. And the, the person explained why he thought it was a data warehouse and how they're using it, that was more like a data warehouse than it was a database. Um, and they ended up revising their whole product roadmap. They, they figured out how to evolve the product into an even better product warehouse, um, and sort of got a best fit for their customers. Um, that was much, much more successful. You know, they, they were able to, to sell that a lot more easily because they were focused on the value that they were delivering for their customers, which is a good segue into why are product analytics important? Um, none of this should be shocking to you. But you know, if you are coming from an on-prem only environment, um, maybe you've talked to some colleagues who have SaaS products, but you know, there's a much more focus these days on agile development, even a across deployment models.

Michael G. (00:11:22) - Um, and when it comes to agile development, customer satisfaction is the highest priority. Um, you know, changing requirements at any time during development is, is important. Uh, delivering working software frequently at shorter time scales very important in an agile environment. And collaboration between business users and developers is really important. So product analytics informs all of that, uh, agile development, uh, principles. Um, you know, another thing to consider is changes in pricing and packaging strategy. You know, if you don't know what part of your product, you know, what 20% is driving 80% of the value or the revenue, that's a problem. So you need to be able to identify what that functionality is, how to price it properly, and perhaps how to even package it, right? You may find, you know, you have different user segmentation that will pay differently. Um, and we'll talk about this a little bit more, but you know, when you get into the SaaS world, you know, product teams in the SaaS world are using different metrics to measure success than, um, on-prem typically does.

Michael G. (00:12:33) - Um, you may have heard, and we'll talk about this pirate metrics, AARRR, AARRR, which is, uh, how they got that acquisition, activation, retention, referral, and revenue. We'll, we'll dig into that more, but that's a very data driven way of, of looking at metrics for your, uh, product success.

Um, and again, you know, you need to continuously identify what the customer's needs are. Um, and again, subscription model customer attention is key. It's, it's not the same as selling a perpetual license where, you know, if they're shelfware, there's shelfware, you know, you made your sale, you don't necessarily need to improve it. Uh, they have it, they can use it or not. In a subscription model, in a SaaS environment, those expectations change and customer attention is key. And you really do need to see what engagement looks like over time, um, and make sure that your key accounts are engaged with your application.

Michael G. (00:13:25) - And then finally, you know, we talked about this a little bit when we were talking about pricing and packaging, but just identifying which elements of your product are driving value, um, that's gonna inform your roadmap decisions. Um, it's gonna inform your pricing decisions. Um, and as we'll see, it can also help you understand which parts of your application might be best suited to go to SaaS. Um, is it gonna be a separate product? Is it gonna be sort of a hybrid environment where you have an on-prem with some functionality, uh, as part of, you know, perhaps collaboration that lives in the cloud, but without product analytics, um, you're gonna be sort of shooting in the dark. So Dan, I think we've got another poll coming up.

Dan B. (00:14:08) - Yep. Yeah, so this time, uh, we have this poll, which is how well does your organization gather product usage data?

Dan B. (00:14:18) - And so again, there's sort of a, a range of options here. Uh, you can currently do this very well, or you have some capability, but it requires some manual processes or engineering work, or there's a plan to do it in the next year or two, um, or you're planning to do it, but there's some kind of inhibitors, uh, or you have no plans to capture this kind of data. Now, um, you know, certainly if you have plans, but there's an optic obstacle in the way or some kind of, uh, a perceived challenge. You know, it might be interesting if you could, you know, punch in the chat what the reasoning is, uh, that you, you've had some, some hurdles to overcome. Um, then I guess another good question might be, you know, how big of a concern data privacy is and whether or not that was a major hurdle.

Michael G. (00:15:11) - Okay, so Dan, I'm not sure if you can see the results, but let me share them. Um, so interestingly, 0% can do this very well. I think people might be humble perhaps, but, um, 42% currently can do some of this, but it requires manual processes or engineering work. Uh, we definitely see that quite a bit. Um, 33% planning to do this over the next 12 to 24 months, 25%, uh, planning to do this, but unclear about customer acceptance or other inhibitors like data privacy. And I guess good news, nobody has no plans to capture this data. Not, not to have a double negative in there, but, um, it, it's good to know that we're, we're sort of smack dab in the middle of, of responses there. And, um, maybe throughout the presentation and during the questions, we can talk about some of those obstacles as Dan suggested. Mm-hmm.

Dan B. (00:16:06) - Yeah. And, uh, so actually kind of, you know, like Michael had mentioned, we have this monetization monitor, um, and, and the poll results from the modernization monitor are actually a little bit different, uh, than what the audience has responded with today. Um, so as you can see, um, out of all the companies that we polled, um, probably about two thirds of 'em said that they can either currently do it really well, or currently they can do it today, but it requires a manual processes. Um, and then you can also see there was like a sliver of about 12% who actually have no plans to capture that type of product usage data, which is a bit, uh, a bit interesting. Um, and certainly, you know, as, as the past few years have gone by when we've partnered with companies, um, you know, those companies have been at all sort of different stages in their analytics journey.

Dan B. (00:16:53) - Um, you know, some companies are certainly in a mature stage. It's not everybody we talk to. Um, some companies collect limited data today. Again, they require sort of that manually manual processing of log files. Um, certainly some companies who are just starting to think about collecting product usage data and what that means and how they're trying to use it. Um, and you know, certainly, you know, the earlier that you, you, you take on a project like this, the sooner you'll reach that destination. Um, but if you're just getting started again, it, it's, it's a good reason to sort of consider what your options are. Um, you know, whether or not analytics sort of fall in your own core competency or whether or not it makes sense to go with a more turnkey solution, um, to accelerate your insights. Again, there are all sorts of different options there.

Dan B. (00:17:41) - Um, so here we have a quote by Gartner, uh, which really notes some pretty strong industry trends. So by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset, um, and analytics as an essential competency. Um, so how does this align with what we've seen in general? So it's, it's really been great to work with so many different, uh, companies and partners, um, who have had analytics programs that have flourished, different individuals who interact with data on a daily basis, uh, different teams who are able to use their analytics data to make powerful decisions and have confidence behind those decisions. Um, and also organizations who have really holistically, again, across the, the organization adopt a data-driven mindset. Um, and certainly as time goes on, uh, we've seen more and more companies again reach a, a, a stage of maturity as time has progressed.

Dan B. (00:18:37) - So now what I'd like to do is just kind of define software usage analytics, um, for anybody who still might be a little bit confused or, or, or have some questions on what we're talking about today. Um, again, another quote by Gartner, a great definition here, uh, usage analytics is the detailed tracking and analysis of user interactions within a software application. Um, again, sell evident in some ways. Um, but when we talk about usage analytics, really what it is, is it's quantifying user behavior. Um, so usage analytics can help to answer questions like, what are my users like? How engaged are my users? What kind of functionality are my users employing? Uh, and then how does usage vary across different users, you know, individually within different customer accounts. And then also across your, your, your broader, uh, demographic user base as well. Um, so this kind of information certainly can be very valuable and help to drive critical business decisions and also ultimately make your customers happier as well.

Dan B. (00:19:40) - Um, here, sort of a, just a, a general question for the audience, and again, I'd, I'd encourage you to enter your, your comments into the chat here, but, uh, really what are the most effective ways that you've found to collect customer feedback? Um, so what kind of methods are you employing today, which are most effective? And, you know, potentially why? If you just wanna type in a sentence or two. Um, and, and then, you know, as you can see here, we've listed out some, uh, some examples, um, all sorts of different ways that feedback can be collected. Some are qualitative, some are gonna be quantitative. Um, some methods are gonna require a high degree of automation, but some are also gonna be really manual processes. Um, so again, there, there's all sorts of different methods and each has its own challenges and benefits as well.

Dan B. (00:20:25) - So, you know, when collecting subjective feedback, your customers might say different things in a face-to-face conversation, um, compared to what they would say in an email survey or, you know, if, if they even decide to respond to the email survey. Um, you know, you're also probably familiar with, uh, the metaphor, which is the squeaky wheel gets the grease. Um, and then of course, even objective data can have its own set of challenges as well. So objective data is gonna require an appropriate sample size. Certainly highly automated systems are gonna be necessary for, uh, results which are, are data driven. Um, and extracting usage data from, again, system logs or other kind of manual processes is gonna be a different piece than using its solution, which is explicitly tailored to solving that, that problem of usage analytics. Um, really what's key is that, you know, every method really, they all have their own place.

Dan B. (00:21:20) - Um, and a complete picture is gonna require augmenting some of that objective data that you're collecting with that subjective customer input. Um, again, this is an idea that kind of closely relates to the benefits of usage analytics because it empowers you to be more targeted in, in how you collect your customer feedback as well. Um, you know, you, you're making sure that you're asking the right customers the right questions and, and you're getting the right answers from the right people. Um, just kind of as a, an example, right? You don't wanna send a customer survey to somebody who's barely used your product or who hasn't used a particular feature that you're trying to get feedback on. Um, and again, I think this all kind of ties in closely to sort of a holistic overview of, uh, of analytics and the different ways that you can, you can get customer feedback.

Michael G. (00:22:09) - Yeah, and actually, Dan, there's some talk in the, in the chat about, um, customer interviews and being a big source, but also being a challenge to get those. And I think you're right about using some of the, the usage data you can get to sort of segment your audience. Um, and we'll talk about this a bit more about pushing out, uh, in-app messaging to some of those folks. Cuz you're right, you do want to ask survey questions or customer interviews of the people who are most experienced, but sometimes you actually want to ask people who maybe haven't discovered a new feature that you've put out too. And using that segmentation is, is a great way to get there, um, and identify some of those folks to help you target those customer interviews a little bit more.

Dan B. (00:22:51) - Cool. So here we actually have, uh, kind of an interesting, uh, interesting quote from Jim Barksdale. So he's the, uh, former, former CEO of Netscape. Um, and really it might help to answer the question, well, what, what good is it? What good is collecting all this data? Um, so here you can see the quote, if we have data, let's look at data. If all we have are opinions, let's go with mine. Um, so you might be familiar with the acronym, uh, hippo, h.i.p.p.o. It stands for the highest paid person's opinion. Um, certainly everybody believes their own opinions are correct, and in the corporate world, what this means is understanding that the opinion of, you know, somebody in upper management or the executive team is usually gonna dictate how and what decisions are made. Um, I'm sure that many in the audience can attest to this, but persuading others of your opinion can certainly be difficult, um, in that respect.

Dan B. (00:23:41) - And it could certainly maybe even be impossible unless you have the right data to back up your opinions. So again, just another, another reason to talk about product usage analytics. Um, as far as the types of different questions that usage analytics can help to answer, um, you know, really what it's, what, what, what the questions are helping you to do is, is to get the right set of data, uh, so that you're not going on your own gut feel. So early on, this type of information was really only available for SaaS or web products. Um, but today, even the most basic information can be incredibly useful for products that are desktop or on premise. Um, I can think of a case with a particular customer who is able to make product decisions within a few weeks after deploying a usage analytics solution for their, you know, installed desktop product.

Dan B. (00:24:30) - Um, and this was driven by relatively basic information. So they were able to get data on what their end user's hardware and operating system metrics look like, and they were able to tweak and optimize their, their modeling algorithms, um, to make use of powerful hardware that their customers were running that they didn't even know about. Um, certainly of course, as you start to dig deeper, you start to ask more questions, things like, what actions are occurring in my product? How are users engaged with my product? What kinds of users do I have? And also what data is specific to my product? And we're gonna go through some examples of the types of data and the type of reports, uh, that will help to answer those questions.

Dan B. (00:25:12) - So first, let's talk about feature usage. Um, so this is really, you know, a lot of the, uh, the meat and potatoes, let's say, um, of, of usage analytics solutions. And when we talk about feature usage, we're talking about features and functionality and different actions that can occur in software applications. Uh, you can see we've got a screenshot of a, a typical report here. And this type of report is really important because every vendor cares about how their software is being used. Um, so you can see here, uh, what type of actions are occurring within your product. You know, again, features, functionalities, controls, dialogues, uh, different actions that users are taking, uh, what features are used the most, what features are used the least, how engaged are users, where are my stickiest features? Uh, this also helps to answer what kinds of users you have and how might different personas, uh, vary as far, as far as how they, they use your product in different ways.

Dan B. (00:26:06) - Uh, so these kind of insights are crucial for making data-driven roadmap decisions on your product. Uh, and you can also sort of imagine how this type of information is valuable when we talk about migrating to a SaaS world. If you're releasing a SaaS version of your product, or if you have plans to release a SaaS version of your product, one of the questions that you need to answer is, which features are most important? Which features do I need to migrate today? Which features can stay in the desktop product versus, which should really be moved over to my SaaS product? Again, feature usage is something that every, every software vendor has, uh, uh, an interest in learning about.

Dan B. (00:26:45) - Next, we talk about geographical insights. Uh, really when managing a global application, which I think most software vendors probably are, uh, you might not necessarily have a physical presence in every geography despite that. So you need to understand where your product is most popular, and you might even see activity in places that you might not necessarily expect. So this can, uh, drive localization efforts for the product team, for the marketing team, for the sales team as well. And, you know, certainly later we'll actually be talking about the story of a, a customer who is able to drastically increase their survey response rates, uh, after a relatively small localization exercise. Again, tying back to the idea of augmenting qualitative data, uh, based on the quantitative data that you're, you're tracking and vice versa, user engagement.

Michael G. (00:27:39) - So, yeah. So we touched upon this earlier and we'll just dig in a little bit deeper. Uh, you know, review what we're talking about about pirate metrics. You know, we talked about acquisition, activation, retention, referra,l revenue, common metrics tracked by SaaS vendors. Um, and if you're coming from an on-prem world might be a little bit new or, or interesting, um, or previously un unattainable, right? You can't measure it. But, uh, Dan's gonna share now about, um, measuring daily engagement and how that can help.

Dan B. (00:28:09) - Yeah, so when it comes to quantifying user engagement, you know, this is gonna typically be your, your daily dashboard. So you can see in here overall activity is if it's where you expect it to be, if customers are engaged day-to-day within your product as a whole. Uh, and you can also get a feel for how many users might be coming on board, and also how many might be abandoning your product as well. Uh, so you may or may not be familiar with the term churn. Uh, that's a term that's typically used in the SaaS world, and the concept of churned users really relates to the concept of, of like lost users, and it can be applied to subscription products, uh, including desktop and, and on-premise products as well. So this, this concept of churn is directly, uh, applicable, uh, from both SaaS products and also, uh, on-premise and desktop products. Um, really the, the advent of usage analytics has helped desktop producers to quantify these metrics in the same way that SaaS vendors typically would. Uh, and again, this report is very shareable across your organization, uh, really just for understanding the overall health of your product in, in business as a whole.

Michael G. (00:29:13) - Yeah, and Dan, even from an on-prem perspective, right? You know, you, your sales team is always gonna be worried about, you know, losing business, uh, when a subscription is coming up. The more advanced notice that they can have, you know, see those trends when downward daily engagement within accounts is just gonna help them make sure that they're addressing issues well before it's time for renewal. So definitely good data to be using here.

Dan B. (00:29:40) - User flow. So this kind of closely relates to, uh, some of the, the, the aspects of feature tracking that we talked about before. Um, and, and really there's so much variation in user behavior patterns from one user to the next, and how different users might execute different functions or take different workflows within your product. And really understanding this can be a difficult task. So user flow reporting is gonna help to visualize the most common paths that users take within your application sequentially. So you could potentially discover roadblocks or points where users drop off within the product. And really, this data can help you to determine if you have an ideal customer experience or maybe if something needs a little bit of improvement as well.

Dan B. (00:30:27) - And, uh, speaking of user experience, I, I like this picture here. Um, as you can see, if you look, there's, uh, this is a picture of what's called a desire path. And what happens is, so foot traffic causes erosion, which eventually becomes a path, right? And typically these paths are gonna represent the shortest or most easy to navigate route between two points here. Um, and just like you see in this picture, these desire paths aren't necessarily gonna line up with what, uh, whatever the pre-planned walkways were, uh, were laid out. Um, and really this is, you know, very symbolic of a typical user's workflow within a software applications. Uh, you know, we often tell our, our customers to expect the unexpected when it comes to, um, you know, a user, user experience in workflows within a product. Um, really imagine how observing an end user performs a particular task, right?

Dan B. (00:31:16) - This is certainly a valuable exercise, just like we talked about doing, uh, doing interviews earlier. Just, just looking at how a user is engaged with your product is like a silent observer. Uh, maybe the user takes a workflow that's different than what you expected, um, or different than you designed. Certainly. Uh, so that's kind of helps you start to ask the questions of, well, why might that happen? Maybe, you know, this is sort of an educational and informational issue, right? Uh, related to, you know, onboarding for your product. Maybe you need to help customers out with some training, um, or maybe this data can be used to drive significant product changes as well. And again, the idea of having this user flow data of the, the sequence of steps that users are taking within your product can really help to drill down on these scenarios and really enable you to take action.

Dan B. (00:32:09) - So what I'd like to do is talk about a, uh, a customer use case next. Uh, and the customer here is a company called TechSmith. Um, they're a highly customer-oriented company, and what they wanted to do is they wanted to drive the value of their solution by improving the end user experience really universally, uh, for them, shipping new features and functionality was only half the job. They wanted to make sure that their end users were being connected with all these valuable features that they had spent all these time and resources to develop. Uh, they also had a desire to augment these, again, those qualitative insights with, uh, with comprehensive quantitative data. So they, they were sending out surveys and they wanted to augment this with product usage data. Um, and TechSmith had sort of built out their own homegrown solution. They decided that, uh, this requires a bit too much work.

Dan B. (00:32:56) - So eventually they switched to something a bit more turnkey. So what were the, what were the results here? Um, really the results of the program could be appreciated across the entire organization at TechSmith. So the product team had realized that their video recording capability that existed within a screen grabbing software, uh, was actually much more popular than they had, you know, originally assumed. Um, they were able to improve user experience by putting popular features up front in the UI and pushing back features that were a little bit less popular and more for niche use cases. Um, an advanced level of functionality that they started to explore was, again, that idea of user flow and path analytics. Again, quantifying that user journey through the application. Certainly the marketing team was able to make usage of the, the usage analytics data as well in their own way.

Dan B. (00:33:48) - They used the data to improve advertising and email campaigns by identifying specific user needs and trends. Um, and they also used the data they collected to dictate content management by focusing specifically on what their customers wanted. Um, you know, finally the data was also used pretty intensely by the, the sales and support team. Uh, so they used contextual, uh, messaging within their application to accelerate trial conversions, to assist with cross-selling different products. Um, they had also sent out targeted NPS surveys as well, which were localized to specific users. So this is the example they, I had mentioned before. Uh, so they did not have a French version of their product, but they sent out, uh, a version of their NPS surveys, which were localized within French. And it turns out that the response rate was way, way higher than they could have imagined, uh, for that reason, just by doing that small little localization effort. Um, and lastly, those user engagement metrics really helped to quantify product usage, uh, for their big enterprise accounts as well. So, going back to what Michael said, um, from an account management and customer success standpoint, um, you know, they were interested in metrics that related to customer retention and, and usage intelligence really helped to get them, uh, to a place where they felt comfortable quantifying some of these retention metrics.

Dan B. (00:35:13) - Next, I'd like to just take a, uh, a few minutes to talk about really different aspects of buying a turnkey analytics solution versus building your own. Um, so just like when you're evaluating sort of the build versus buy scenario for any other kind of technology, you know, you wanna consider whether or not this is something that falls, uh, within your, your core competency as an organization. Um, certainly data collection isn't the, the tough part about building out an analytics solution, but configuring the right infrastructure for reporting is, is actually a pretty substantial task. Uh, and you don't wanna spend resources on something that's gonna require substantial engineering effort. Um, it's especially true if the solution is gonna require continuous engineering support to get the type of reports that you need.

Dan B. (00:35:57) - Um, ongoing costs can certainly be substantial as they relate to things like hosting and maintenance and upgrades. Um, you know, we've also talked with different vendors who have hacked web analytics tools for their desktop or for their on-premise solutions. Um, and this can also be a little bit of an impractical approach as well, because web tools are specifically designed for web products, for example, uh, a constant internet connection might be required, whereas desktop products don't necessarily always have a constant internet connection. Um, a lot of these tools have been designed to do things like track page views, uh, not necessarily different features and functionalities within a product. Uh, so desktop and on-premises applications really have a different set of technical requirements than a web application might, um, in getting this type of solution to work with a desktop product might take substantial engineering resources, uh, just like building your own solution, um, might. So again, just some things to consider if you're thinking about pursuing building your own solution versus going with something a little more turnkey.

Dan B. (00:37:03) - Now, when we talk about analytics, um, you know, certainly we like to talk to our customers about best practices as well, and we, we kind of refer to the journey as falling into, you know, three stages here, right? So, crawl, walk, run, kind of approach. Um, and something that we always say to our customers is, you know, you don't know what you don't know. So rather than jumping in headfirst, start out small. So, on day one, you'll discover all sorts of different, uh, eye-opening data and, and get all sorts of useful insights. This might lead to deeper questions, uh, specifically on your application and how your users are using it. Um, and then once you've got a mature program, you can do things like start to take a more proactive approach and engage customers based on, uh, what their behavior is and based on how they're using your product.

Dan B. (00:37:49) - Next, we always recommend that customers take the time to in to basically to invest the time in deciding on what's important to track. Um, again, this relates closely to the concept of crawl, walk, run. So rather than tracking everything right off the bat, start out tracking, uh, the different features and aspects of your application, which are most important that you want data on today. So this helps to ensure that you're not overwhelmed by the data that you're collecting. Um, and again, data collection's not the hardest part of, uh, the usage analytics problem. Um, it's a lot more challenging to have a dynamic reporting system that's gonna allow you to extract those meaningful insights. You don't wanna be overwhelmed by having too much data. Uh, next, the question of data privacy is always relevant. Um, and certainly your customer's privacy should be respected. Many companies that we partner with, uh, will provide their customers with an ability to opt in or opt out of usage data collection.

Dan B. (00:38:47) - Uh, you certainly want to be sensitive to personal data and, uh, understand what type of information is personally identifiable versus what's not. Um, and certainly, you know, with GDPR and some of the other regulations, make sure you use this usage analytics data within scope. So this is data that's really meant to be used to make your product better and to improve your customer experience. Uh, this isn't data that really should be shared with third parties in, you know, the, the vast majority of cases, let's say. Um, lastly, you know, remember to allocate your resources appropriately. So earlier, you know, we, we talked about build versus buy. Um, and certainly if you're building your own solution, this can be an expensive project, both in terms of both infrastructure and then also engineering resources and requirements. So take this into account, um, you know, potentially weigh out the benefits of, of building your own versus going with a third party vendor. Um, you know, most, most turnkey solutions are only gonna take a few days to implement, and that's gonna allow you to make more data-driven decisions a lot more quickly versus having to build your own from scratch.

Michael G. (00:39:57) - So, Dan, I I think we, that was a great presentation and I think, you know, people may have seen this video before, but it's, it's one that we love, right? Like, you know, like you said, you don't know what you don't know. You may not know that this is how your product is being used, um, and there may be a use case in which it makes sense to use it that way. But, um, I think now if people have, excuse me, some questions now would be a great time for us to jump into those. Um, Mike? Yeah, Mike B. (00:40:24) - Yeah, Michael, this is Michael Belsito back here. Thank you so much for walking us through that. I thought that was great. And I personally love the, uh, love that video at the end too. I think that's a great one to include. Um, yeah, just as a reminder, if you do have questions for Michael and Dan, um, just load them up in the q and a section. I'll take a look through there. Um, and I, you know, I actually had a couple questions that were emailed to me in advance, um, in, in case people couldn't actually make the session, they wanted to make sure their question got in. So I have some of those here. Maybe I can, I can start, um, and, and, you know, I'll, I'll direct these, but I guess both of you can, can weigh in if, if, uh, both of you have, uh, more to weigh in on with. But I'll start with Dan. Um, you know, in, in general, when we're talking about things like implementing usage, analytics into applications, it sounds like it might be a lot of effort. Uh, but what is that effort? Like what, how, just how, how much should people actually prepare for this? What are some of those first steps? Uh, maybe you could start us off with that, Dan.

Dan B. (00:41:31) - Yeah, certainly. Um, you know, this kind of ties in pretty closely, I would say with, uh, some of those build versus buy best practices that we wrapped up with. Um, you know, certainly if you're gonna build your own, um, you know, certainly in investing the resources into all sorts of the, the planning and and design stages, uh, is gonna be a valuable, um, valuable asset. And again, I, if you know, data and analytics is totally outside the core competency of your organization, you know, I, I I don't necessarily think that, um, you know, that's, that's gonna be the right approach. So again, we generally recommend going with some turnkey, uh, for companies that, um, that that doesn't fall within their sweet spot as far as the level of effort that's required. Um, generally speaking, there's gonna be some, some variation. Um, but the benefit of turnkey solutions is that the solutions are generally designed to be, again, turnkey and very much out of the box.

Dan B. (00:42:27) - Um, so when we talk to, to different customers, we generally recommend like two to three developer days worth of resources, um, to sort of instrument most functionality within the product. Um, and really that's all that's required in order to get, you know, both, both out of the box metrics, but then also custom attributes about the product. Uh, things like, uh, you know, custom properties or custom fields about your users, and then also all sorts of information on, on featuring event tracking as well. Um, so again, a turnkey solution, the amount of effort that's required there should be really, really relatively minimal. Um, now certainly there's gonna be some, uh, uh, some caveats to that, um, depending on, you know, what your company's sort of, you know, QA process looks like, and, um, you know, any, build variables that might come into play there. But, uh, generally speaking, a turnkey solution should only take a few days before, uh, before you're up and running. And then, you know, at that point you release your product, you're getting some, uh, some set of users who are adopting your latest and greatest, and then, you know, you'll have analytics.

Michael G. (00:43:38) - And Dan, fair, fair to say that, you know, most of our customers, you know, they're actually spending more time in the planning phase than the actual coding implementation phase, right? Like, to your point of doing that work upfront to identify what are those key features that you wanna understand, you know, identifying which ones you want to track most of the time spent there, right?

Dan B. (00:43:57) - Yep. Yeah, absolutely. That, that's a good point. That's something that I probably should have mentioned. Usually when we talk to customers, uh, we recommend investing more mindshare and, and more resources into the planning phase of figuring out what's most important to track. And the reason for that, again, is because if you're tracking everything, the data can be potentially overwhelming. You wanna make sure that you're getting as much value as you can. Um, and the best way to do that is to start out by focusing on, on what's important. Like Michael had mentioned, he, he, you know, made reference to that pareto principle earlier of, you know, 20% of the functionality drives 80% of the value maybe start up by just focusing on that 20%, getting data on that 20%. And then you can iterate from there and figure out, uh, figure out where you need to invest more resources and, and what the next question that you're gonna ask is gonna be. Um, so we certainly recommend investing that mindshare into the planning phase.

Mike B. (00:44:55) - That's great. Uh, I, you know, I have, there are great questions coming in. Actually, I'm gonna go to audience questions. Michael, I do have one question for you before I do. Um, again, this is one of the questions I got in advance, and it's how do I get other people in my company interested or excited in the usage data I'm collecting? And I think this is a great one because really data becomes more valuable when more people are using it, because now we're actually able to do something with it rather than just, you know, take a look at it and say, well, that, that's nice. If everybody in our organization is actually using it, it becomes a lot more valuable. So yeah, how, how can we get other folks within the organization on board and excited?

Michael G. (00:45:34) - Yeah, that's, that's a great question, Mike, and, and one that our prospects and customers face from time to time when they're trying to, you know, argue that this is something that we need. And obviously that's part of the point of this webinar and why I upfront spent a lot of time talking about, you know, SaaSification and that, that move to different environments and sort of the different metrics that you need to understand, um, and have access to. And if you can't get those, that type of data, you can't compete. That's super abstract though, right? So like we always say, you know, find those metrics that are really important to your company, and then figure out which different, uh, buck silos in your company, you know, different aspects of, of the data that you could be collecting. You know, so wouldn't you like to, uh, have deeper insight into, um, churn, right?

Michael G. (00:46:19) - You're talking to sales that would be good to know, right? So we're not rushing at the last minute to save an account. Um, if you're talking to product management and you're trying to convince a broader team, you know, don't you want even that basic, uh, metrics of, of environmental that Dan was talking about, like that customer that had six weeks worth of data, saw other customers were on much more robust hardware, and were able to accelerate their roadmap. You know, that's good to know, right? You, you're not gonna, your roadmap is gonna be very competitive then, and you're gonna be delivering, you know, the, the features that your customers really want. Um, and then, you know, marketing, you know, Dan's, Dan's comment about, um, you know, looking at, you know, okay, what are the topics, what are the trends? What are the features that people are most interested in?

Michael G. (00:47:06) - Making sure that that's where you're driving your marketing messaging toward, right? Um, you know, even going back to that April Dunford story about database versus data warehouse, having those insights, you know, for a product team, for a product marketing, product management team is super helpful. Um, and then I would say once you have some of that data, you know, if you have Slack or teams or some sort of internal chat system, make sure you're sharing that on a regular basis, you know, a metric of the week, and then asking people, what, what metrics would you like to see? And, and get that whole company engaged is sort of a good precursor to, um, sort of a further maturity stage where you actually have a software usage analytics built into your executive dashboard.

Mike B. (00:47:51) - That's great. Thank you, Michael. Uh, you know, this audience question came in and yeah, wondering if other people could be in the same boat here too. In this particular case, they're converting, uh, a few products for the web, but the legacy products that they have that are actually heavily used, they didn't actually have tracking methods in place to know what things to sort of keep first, which to add later. So they're wondering what they can do, um, and it's also the case that their products, um, they're building components that it's like their customers customer, um, let's see if I can understand this right. They're saying they can't track anything because their end user is not the customer, it's the customer's customer. So wondering if either of you have sort of insight to share on this one?

Dan B. (00:48:34) - Yeah, I guess, uh, you know, without getting too much into the sort of the technical weeds and, and the method of, of deployment for this solution in particular, uh, you know, we, we have worked with other customers in the past who have made, like, uh, designed an SDK or a set of libraries, which are, you know, embedded in other products. So where, where the libraries end up is again, sort of in, you know, the, uh, the customer's, customer's environment. Um, and we've seen different approaches there, certainly, uh, customers who will, um, you know, basically embed tracking capabilities within like the debug version of the library. That way they kind of, they're able to, to quantify how the debug version of the library is being used, which isn't necessarily the one that's gonna be shipped with the product. Um, and certainly I think that, you know, just, just generally speaking about, uh, some of those legacy products that aren't as heavily used or, or that, that are still heavily used, um, in sort of trying to figure out what, what features are most important and which are gonna move to the web, um, you know, like, like we had said earlier, um, you know, we definitely recommend taking that sort of crawl, walk, run, incremental approach.

Dan B. (00:49:41) - And just because you don't have any data collection system in place today doesn't mean, you know, you, you, you can't start tracking those legacy products. Uh, certainly a lot of companies that we talk to are in the same exact boat. So they've got a legacy, an older product, they're not making, uh, any significant feature enhancements too, but they still want data on those products so that they can, um, you know, keep their customers happy and decide what's most important to move to their web or SaaS based solution or, or to the next iteration of their, their on-premise products as well. So even if you're not collecting analytics on those products today, um, certainly, you know, the, the best time to start could certainly be today or, or, or tomorrow or sometime in the near future. Um, because that's, that's the best way to get information, uh, is to, to, to start collecting it.

Mike B. (00:50:28) - Yep. Yeah. Great. I appreciate that. You know, somebody else asked the question, and we have time for a couple more questions here. Somebody else asked the question if, um, you, you had recommendations on what turnkey solutions, uh, people could use, and they added the caveat, Hey, expect many are tied to a specific UI framework or maybe even a specific platform. Um, I might have an idea of, of what you all might recommend, but I, yeah, how would you, a I particularly the latter where, you know, hey, are solutions necessarily tied to certain frameworks? Maybe you could speak to that a little bit.

Michael G. (00:51:00) - Yeah, I'll, I'll, I'll start at the high level and I'll let Dan get into the details around frameworks, but, um, yes, so we tried to make this a very educational webinar. Apparently it might have been a little too educational and not salesy enough, which is sort of the way I prefer it. But yeah, we, Revenera does offer a solution called Revenera Usage Intelligence, um, that lets you track usage of your, uh, desktop applications. Um, and we have it for a number of platforms. And, and Dan, you can probably dig into that better than I can.

Dan B. (00:51:29) - Yeah, sure. Uh, I, I think that kind of more, more generally speaking, right? So we had made mention of web-based products before. Um, you know, there are all sorts of different web solutions, you know, things like Pendo or MixPanel or even, again, some people hacking like, uh, you know, Microsoft or, or Google, uh, data collection methods to work for, for desktop and on-premise products. But, uh, yeah, again, we do have a, an offering for a variety of different platforms and environments, specifically tailored to, um, you know, desktop applications and on-premise applications. So we've designed and optimized the technology, uh, to fit right within that wheelhouse. So certainly if, uh, um, you know, if, if you're looking to explore a solution that fits into that kind of space, I'd, you know, encourage you to reach out.

Michael G. (00:52:14) - Was that a name drop, Dan, reach out? So, so actually we, we will talk a little bit because one of the nice benefits of, of our solution is this thing called Reach Out that lets you do in-app application messaging to your desktop users based on the segmentation that you can achieve using usage analytics. Sorry, I had to Dan.

Dan B. (00:52:33) - Yeah, great, great. Plug, ,

Mike B. (00:52:36) - Love the shout out there. Love the shout out. Um, and you did you, well reach out could mean that, or it could mean get in touch with either one of you. And I do want to point out that both Dan and Michael's information is right there on that, on that screen, so feel free to jot it down. But I'll also include that in my follow up email to everybody, um, little bit later, um, probably by the end of the day where it'll include recording this session too. Um, I, you know, I'm looking at the clock. I know we're, we're coming up on almost an hour here, so it might be a good time to wrap up. But what I would ask Michael and Dan, if there's sort of one big takeaway that you hope people are, are going to walk away from this session with, you know, sometimes there's a lot of information, right? So we're pulling all this information in and hopefully we can retain all of it, but if it's a situation where maybe people just retain this one big high level idea or concept or, or takeaway, what would you want that to be?

Michael G. (00:53:30) - Well, I, I guess I would say is, you know, given everything going on these days with on-prem versus SaaS / hybrid solutions, you need that data. You really do need that insight into how your products are being used to make a whole wide range of decisions, even if you're never gonna move to SaaS, just to make your products more competitive with SaaS solutions, uh, and improve them, improve the customer experience, deliver the value that people need, and, you know, monetize what matters. Um, data bottom line.

Mike B. (00:54:01) - Love that. Love that. Dan, is there anything else you want to add to that, or you think that's a good, good way to end?

Dan B. (00:54:07) - Yeah, I mean, I guess the only thing I'd add is, you know, we see all sorts of different, you know, objections about why customers won't, uh, won't act and, and collect data. You know, either because they don't know how to approach things or they don't know, um, they potentially feel as though they might be overwhelmed. And, you know, generally I think what our recommendation is, do something and, and, and, you know, really do anything. And then again, you can iterate, you can take an incremental approach. Um, and, and as long as you're collecting some data, you're gonna be answering some questions. And if you know those answers lead to more questions, that's okay. Right? It's all, it's all about the journey. .

Mike B. (00:54:46) - I love that. I love that. Well, hey, I appreciate the time and, and, uh, all the energy from both you and, and Michael. So thank you Dan and Michael, and, and I appreciate everybody being here and being a part of this industry webinar. I hope we all learned a lot. And, um, while we're wrapping up things here, if there are questions that come up later and that we didn't get to, um, again, you see Dan and Michael's info right here, I'm gonna send that to you as well. So feel free to shower them with questions afterwards. I, I, I know that they would welcome that. So with all of that, thank you again everybody for being a part of this. I'll be in touch soon with the recording and we'll see y'all soon. Bye everybody.

Michael G. (00:55:22) - Thanks everybody.