ServiceNow, Inc. (NOW) Earnings Call Transcript & Summary

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All right. Welcome, everyone, to today's session. Enhanced Service Delivery While Reducing Costs: Key Strategies for Success. The video did a nice job teeing us up there, but one more reminder, I really want to make sure that we use that Q&A section for questions that are coming in throughout the session. There's two of us here today sharing this. So we'll do our best to answer those questions that come in during the session, but we have a dedicated session at the end to address anything that we can't cover real time. I have to put this slide up here. This is the slide that says anything that we say are due here today cannot be held us -- against us in the court of law. It is also the slide that says, hey, as part of this conversation, if we happen to mention anything in might be coming on the ServiceNow platform or with the solutions that we're talking about here today. You should take those as forward-looking statements, make no decisions based on them whatsoever because they're always subject to change. I'm assuming you've read the final print. Now there's two of us here today, and both of us will introduce ourselves now. My name is Dan Grady. I'm part of the product team for our in-platform process mining solution called process optimization. I've been in ServiceNow for about 7 years, covering a number of different products, all in the analytics AI reporting space. With me here today is Andre. Andre, do you want to take a moment to introduce yourself?

Yes. Welcome, everyone. Thank you for joining us. My name is Andre Ramsarran. I'm the outbound product manager in our customer service management business unit, and I specialize in things like process optimization, our agent experience on the desktop as well as our -- some of our AI capabilities in terms of task intelligence and things of that nature.

Perfect. Thanks, Andre. What are we going to cover here today? So we just did that little quick introduction. Andre, is then going to run you through a process optimization overview, kind of cover the why and what of in-platform process mining. We know what's a new muscle for a lot of you. Well, then in transition over, I'll do a quick demonstration of the solution then Andre and I will just have a little bit of a back and forth, talking about some of the common questions that we get from customers when it comes to in-platform process mining and then we'll wrap up getting to any of that additional Q&A that we're not able to cover real time during the session. All right. With that, Andre, I'm going to let you take it away.

All right, well, it's always wonderful to start off with some insightful thoughts or quotes. This is actually something that I thought came from my yoga class, but I was surprised to learn that it's actually a famous American science fiction writer, Ursula Le Guin and it goes like this. It's good to have an end to journey towards, but it's the journey, that matters in the end. And I think that will be kind of a salient theme of our presentation, the idea that customers go through journeys and end up at certain outcomes, but often the business is unaware of the journey that they went through to get to that outcome. So I think this is a nice quote, a nice salient point to come back to. We'll hop over to the next slide. So from a customer service management perspective, this is often how we think about the world. A lot of companies spend their time with the engagement layer. They wonder about how is it that they engage with their customers, whether they're coming in by phone, e-mail, portal, messaging apps, whatever the situation may be. And for a lot of service organizations, after they've done that initial engagement if the customer needs any additional layer of support, they enter into what we call kind of a black hole. And from your personal lives, you might know what this is about because if you've ever had to repeat yourself or get moved between different teams as you go through a support process or kind of enter a support process where you don't know where it begins and ends, you've kind of experienced that customer service black hole, especially if you've been transferred to a call queue where they say, "Hey, your call is important to us. That's why we're going to put you on hold for the next 20 minutes" as an example. So with CSM, customer service management what we do here at ServiceNow is we help to structure that workflow, if you would. So we kind of -- what we do is we structure that workflow to kind of coordinate front, middle, back office type of personas in order to get the work completed. Now for a lot of organizations, when they design these support processes, they have a path in mind. This is kind of like the idealized path of where people will walk, how they will move, whether they're using bicycles or skateboards or accessibility and things of that nature. That's what we refer to as the design path. And typically, these design paths are looking to accomplish things in terms of completeness and efficiency. But when users actually use a support process or go through a support process, they might have a very different experience. They might not experience the design path. The reality is they may go on a very different type of way. And this metaphor also applies to business processes. So as a support organization, you may believe that your customers are going through a specific design path. Perhaps it's a quick one-and-done type of service scenario or a multi-touch scenario. And the belief is that the customers are moving through nice and streamlined in a linear manner. However, the reality may bear something very different. Perhaps they are circling around within one team or getting ping pong back and forth between different tiers of support before they get resolved or maybe just enduring an extraordinarily long support process and it's compromising service level agreements and things of that nature. So when organizations try to peer into the service experience to figure out what's going on, it can often be a challenge. For example, what they may end up doing is grabbing a whole bunch of manual reports and collating data that doesn't match. They may end up going into workshops with different internal teams, which can be very time consuming or they could hire external business consultants, which is very costly or they can seek internal alignment, right, where they try to get a working committee happening across functional teams, and this could be very time consuming and cumbersome as well. So these are some of the challenges the customers face when they try to untwine that ball of wax to figure out what is the actual support experience about. Now some organizations will typically turn to traditional analytics in order to solve that problem. And when they look at traditional analytics, they're looking at real-time snapshot of information. So they may be asking how many cases do we have in flight at this moment in time. What type of work items are we working on, how are we performing against service level agreements and things of that nature. But one thing to realize is this is perhaps just 1/5 of the picture. This is the kind of the iceberg above the water line. Process optimization goes a lot more deeper than that. And what it does is it looks at the other kind of 4/5 of the equation, if you would. So this is where you can ask deeper questions like where is the process stuck. So where our bottlenecks and inefficiency is occurring. Is there a high level of variance? So are some customers waiting a really long time when other customers are just taking a minute to go through the support process. How many cases are agents working and what is their mean time to resolution and can that be improved? So with process mining, you can take a much deeper and richer look at the challenges that customers need to solve. So this is where -- this is an example of a dashboard that a manager may typically see. And again, this is more of the traditional reports and analytics type of dashboard. But with process mining, we're going to take it much deeper. So we introduced process optimization, which is part of the ServiceNow platform and it is used by a number of different personas. So it could be a persona like a process analyst or a business stakeholder in a specific functional area of the business. And what you have the capability to do is on one platform peering into the auto logs, you have the ability to gain insight into processes and where they may be getting stuck. And within a few clicks, a process owner can gain such insights so they can look at the process map and as Dan will demo they can really drill down and get a clear understanding of what is the reality behind the workflows that are unfolding within their support organization. So this can apply to personas of all different levels. So whether you're talking about process owners, business analysts, team managers, and here are some of the typical benefits that we see. So one area that we're going to help with -- have tremendous benefit is by reducing operational costs. So we're going to help to streamline workflows and identify and remove those bottlenecks and improve performance with machine learning. And this is a really critical thing, right? Because for a lot of organizations, they may have thousands or tens of thousands of cases that are flowing through a support process. And it would be beyond a human's ability to analyze all that information and identify patterns or even uncover hidden patterns. So we're bringing AI to bear to assist with this in process optimization. In addition to that, what you can really do is you can kind of fine tune your process and measure them against specific goals that you set for your service organization, and drive continual improvement. So what outcomes will this lead to? Well, at a high level, it's going to lead to outcomes such as increasing speed of your business processes and experiences of your end customers, increasing the productivity of your agents in terms of handling cases quickly, reducing mean time to resolution, reducing the number of customer touch and also remove risk as well, right? So if you have certain processes that are very expensive or customers are getting stuck, that can introduce risk to product adoption to -- it may introduce risk from a legal perspective or even from a compensation perspective, if your customers have contracted service level agreements and things of that nature. So that's a little bit of background on process optimization, and now we're going to hand it over to Dan to show us some of the magic.

Perfect. Thank Andre, I want to just click here, sharing my screen. And I'm assuming everyone is now seeing a dashboard on the screen. So going to take us through a quick demonstration, just so you get a feel of kind of how you might use process optimization inside of your organizations. And I always like to start my process optimization demonstrations on a dashboard. Just kind of take you through that process. But this happens to be a ServiceNow performance analytics-based dashboard but you don't need to be using performance analytics to get value-add process optimization. They just happen to be very complementary solutions. So a typical dashboard that somebody in customer service management use, where you have high-level metrics around your CSAT, your resolution times, your current backlog. And the trends related to those key performance indicators as well as the targets that we set from ourselves. I mentioned earlier during the introduction that I've been in the analytics space for quite some time. And one of the things that I've learned over my many years working with customers on analytics is that information tend to be one of the most indicative and contagious things at that there. You give somebody a little bit and they always want more. So the first thing that happens when we roll a dashboard like this app to someone in leadership is they start having all these follow-up why questions. Why are my closure times trending in that direction? Why are my CSAT scores not hitting the targets that we set for ourselves. Why, why, why? I mean, traditionally, it's been very difficult to get to all those whys behind those KPIs. But now with process optimization and a single click, someone is going to be able to go from a dashboard like this and drill down and start answering some of those follow-up why questions. So what we just did there is you have the ability from a dashboard to drill down into the process optimization workspace. And when I mentioned, I want to make sure I just reiterate this, you don't need to start there. You can come directly to the process optimization workspace as a user and start digging into any of the projects that you set up and mine for yourselves, but there is that linkage if you wanted to link the two things together. We're just going to give you a little tour of some of the information that the process optimization workspace would provide for you. So in this case here, we've mined about 2,300 customer service cases, demo cases, of course, those 2,300 customer service cases have taken 500 routes to get to closure. And on average, they're taking about 13 days to close. On this summary and insights to have inside of the workspace, you have the ability to configure specific KPIs for the analysis that you're doing. Again, since we're focused on customer service cases, resolution times and cases resolved on first contact are two very important metrics that we try to strive to improve. So we've included those out of the box for our customers, but this is completely configurable. As I move down on the screen on the summary and insights page, we get to one of my favorite parts of the solution. These improvement opportunities. So we provide, like we do in many areas of the platform, out-of-the-box content to help jump-start you on your analytics journey. And when it comes to process optimization that out-of-the-box content manifests itself into something we call improvement opportunities. We're finding definitions on we need to cover. So common inefficiencies and nonconforming activities that we see inside of processes. We highlight them right up front. So of these 2,300 cases that we mined, 435 of them or 19% will reopen cases that required us to go back to the customer for additional information or maybe we want to focus in on cases where a process step is skipped or cases where the state changes from resolved work to new. And we'll dig deeper into this specific use case as we move through the demonstration. In addition to the out-of-the-box, improvement opportunity provide you as customers have the ability to create your own finding definitions. And this is one of my favorite parts of the solution because it reminds me of when I was playing hide and seek as a kid. As a kid , when we're playing hide and seek at my house, I had a home field advantage. I knew the 2 or 3 best hiding spots. So when I was kid I was much more efficient at finding my friends than they were at finding me. The same thing is true with your business processes. Many of you probably have some good hypotheses about where the inefficiencies exist inside of your workflows. But maybe up until today, you haven't had the data to prove out the impact that those inefficiencies were happened. You now have that ability to surface those insights right here within these improvement opportunities. And that will help you prioritize where you should be acting or taking action first. Now as I scroll down this screen, beneath the improvement opportunities we provide you with something called variation analysis. And what this allows you to do is look at all of the different routes. In this case, 500 routes that these cases took to get to closure. And we can look at these routes by the ones that were most traveled so the ones that had the most cases moved through that route or maybe the route that had the longest average duration or maybe the routes that have the most steps involved with them and when you start looking at your own data in some process optimization this gets pretty scary, pretty quick. But it just allows us to -- it helps guide us in terms of where we might want to start our deeper dive analysis. And then whereas variation analysis shows us the entire route that cases are taking, bottleneck analysis allows us to look at the individual hops or between, whether it be between states or assignment group handoffs inside of the process itself. And again, we can look at those hops by total duration, average duration, the ones that are tackling most often. All of these are both variation and bottleneck analysis on the summary and insights page are meant to help us jump-start that process. So any time we see an outlier here, we can click this button and immediately view the visualized process map for just that subset of data. So just here on the summary and insights page, a tremendous amount of insight that many of us did not have in the past around how our business processes are working. Now we'll jump over to the analyst workbench. And this is the -- while there's a lot of power on that summary and insights page, there's a tremendous amount that you can do at a deeper level inside of this analyst work bench. So I just want to walk you through some of the flexibility you have to answer some questions about the process itself from the analyst work bench. So over here on the right-hand side of the screen, I can bring up this model options area. And what this allows me to do is to get the basic statistics about the data that I'm looking at, again, those 2,300 cases, the 500 different routes, the average duration of tools. Now you might be saying to yourselves, well, well, well, Dan, you keep talking about these 500 routes. That map doesn't look like those 500 different paths to get to closure. And you'd be right. And that's because, by default, we just show you the top 20% from the volume perspective. Because if I started to expand this out to show you all of the data, it's going to get pretty messy, pretty fast and pretty scaring. And if there's anybody with a risk and compliance background, looking at this, they're going to get pretty scared because each one of those individuals lines is some potential risk for the organization. I'm just going to bring this back to the, let's say, 30% to make it a little bit more readable, hide this and zoom in a little bit. Now for those based on the survey results, I think many of you are in the same place that I was when I first started looking at process mining. It's a new muscle. It's something we haven't been doing in the past. So I often get asked, Dan, when you see a map for the first time, what do you look for? I mean for me, these maps, they kind of remind me of ski tracks. And when I think back to when I was kid when I would ski a lot more than I do today, any time I started going down the mountain, but then for some reason how to go back up to the mountain. It certainly wasn't the best experience for me, and it definitely wasn't the fastest way to get down the mountain. So what I usually typically look for on these maps is any sort of lining that is going back up the hill because that's an indication of rework, right? It's not the most efficient way to complete the process. So this example here, where I'm looking at my resolved in-progress node. And what you can see here, you noticed a little numbers right there on the node, it shows me the number of cases that book that trip as well as the average duration, and you can adjust this and look at different metrics, if you want. But when you click on that node, it shows me all the different metrics around this. I can see that, hey, for things going resolved in-progress, things essentially that got reopened. We had 470 unique cases of this and 493 total occurrences of that happening, 1 occurrence, 1 case even a bit 3 times. But probably the most important metric on the screen here is the total duration. There is 1 year productivity packaged up in those 470 cases just in that 1 step of process. We've got to be able to reclaim a little bit more of that or a little of that time back. Now another line that's jumped out here is this one here where I see 506 cases that, on average, take 3 days to go from in-progress to awaiting caller info. Right, 506 -- then I've got about 160 or 150 of them, my math is not that good, that took that trip more than once and one of them even took it 6 times. More importantly, once again, 5 years of total duration packaged up in just tickets going from in-progress to awaiting caller info. Again, another area of opportunity. If I didn't want to look at this on the map. I could use my bottleneck analysis to start looking at those individual hops right here on the map. So if I want to look at anything that's going into awaiting caller info, I can do that to you and then jump and just isolate that area. Maybe I want to focus in on the combination of those two, right? Things that get reopened so resolved to in-progress. What I can do is I can say, apply transition. And this will allow me to build the filter criteria to narrow it down to just the tickets that are going from resolved to in-progress some point of their lifespan. Maybe I want to make it even more targeted. I want to say, not only do I want to go from resolved to in-progress. I want to see where the next step in that life cycle is awaiting caller info. And I can hit apply, and I just going to narrow the map down to just show me those 147 cases that took that very specific path of resolved to in-progress, so it got reopened. And then we had to go back to the customer to get some additional information. At this point, what I can do is I can start using my breakdown from the left-hand side of the screen to get a better understanding of these 147 cases, whether it be by product channel, assignment group, category or reassignment talent, if I will. I'd like to use channel analysis. Anytime I see something is going back into awaiting caller info. I'm always interested in the channels that they're coming in because there's got to be a way to improve the intake experience to gather some of that information upfront. So if I look at this here, for these tickets that got reopened and they went back to awaiting caller info, I can see the majority of them came in via self-service. And on average, it was 3 weeks and 2 day's worth of duration packages up in just those tickets. And then I have another set here that came in via portal. Those are pretty structured intake channels. Let's see if we can have enough opportunities to improve those. E-mail is pretty unstructured channel. You would expect a little bit of back and forth. So I'm going to skip that one for now. So I can click on these and apply and it will narrow it down to just now these cases that came in via self-service and portal that took that path. I wanted to continue to dig deeper because I'm in the platform. Of course, I have this ability to come in here and say just show me the records from this specific node. And then this is a little ServiceNow hidden capability that most customers don't know about. And I got to this list of those specific cases that met this criteria, but like I mentioned earlier, information is super addictive and continuous. You probably want to ask more questions about this right here from this list. One, I could launch process optimization and mine just this data. So you can run process optimization from any list that you have on the platform to dig deeper into that list or I could launch something called interactive analysis. And what this does is take that list and turn it into an interactive dashboard in a single click. So I start getting to better understand that data with those of 100 and I think, 11 cases that we mined just there. Now secondarily, maybe I don't really want to look at. I want to use some machine learning to help me understand this data a little bit better. These 102 cases that came into this state here. I could run some root cause analysis or I could run some cluster analysis. And what cluster analysis allows me to do is look at the unstructured data inside of these cases, the short description, the descriptions, the work notes to see if there's any patterns or opportunities. And in this case, I have a cluster of tickets that look like they're talking about loyalty service premiums subscriptions, let me just focus on those. And I'll see that there's a bunch of questions about, hey, what is my loyalty at subscription annual premium? Or what are the benefits? These seem like standard FAQ style questions that maybe I should have a knowledge article for. Perhaps I should create some virtual agent conversations to help answer those questions and eliminate some of the back and forth that's going on. So now that I've identified that opportunity, what I can do is I can come over here and I can start collaborating within the organization saying, "Hey, you know what that able -- take a look at our current FAQs. Can they be improved? " and then it's snapshot and now able to get notified, they'll come into the workspace. You can click on [indiscernible] get immediately to this point what I was looking at. But then more importantly than just that collaboration is this ability to right from here, create a continuing improvement initiative or an automation center request. And if you're not familiar with those two applications on the ServiceNow platform, both of them are designed to help us capture, track, prioritize and then communicate the impact of all the improvements that we're making on the platform. And that's a big piece of this process. If you think about continuing improvement, it's really a 4 phase process in most organizations. Detection, you need to identify that there's an opportunity to improve, then you need to analyze, once you made aware that something is not going according to plan, that's where process optimization comes in, it helps us analyze where and how we could be doing better. Then you need to actually make those improvements. And there's lots of things on the platform that help us make those improvements to our processes like the virtual agent, like predictive intelligence. And then what you need to do is you also need to make sure that these improvement opportunities get captured, tracked and followed up on. And that's where a lot of organizations fall short, right? They see something interesting in the data then maybe put into a slide they go tell a friend at lunch, and that's where the insight dies. The ability to kind of capture these things allows us to take this type of analysis and move it from what is like a onetime science project to something that becomes part of our continued improvement routines because this is really something we should be doing on an everyday -- not on everyday basis, but a more regular basis to help us understand how we can continually tweak our processes to squeeze as much value out of them as possible. With that, I am going to end the demonstration and we'll come back over to so much more I could be showing you but I'll come back over here to presentation mode. And I just want to confirm with my friends here that we're back on the slides. Andre, you've seen slides here. Unmute Andre if you're still here.

Yes. I'm still here.

Perfect. I think we'll move to the fireside chat portion of the conversation. [Operator Instructions].

All right. We'll start off with the first softball question. I think you hit the nail on the head. But just to reiterate a little bit, how would you say or how would you summarize the difference between traditional reporting and process optimization analysis.

Yes, sure. And that's a common question that most people have and if you think about it, again, reporting is designed to tell us what has happened and process mining and process optimization is about digging into the why those things are happening. But there's a level of depth that you just can't get to from a reporting perspective. It's looking at the data once the process has finished. It is like once it's all over, but that's great. But most of the things that we can fix and things that we can address, that's happening within the process itself. So as you saw in the demonstration, the ability to start asking very targeted questions about how things happen within the process, within the workflow, huge advantage and allows you to kind of ask much deeper questions than you ever have been and got blessed before with just traditional reporting.

Yes. It's kind of like [ peak ] into the Black Box, flight recorder kind of thing.

Exactly. Exactly. What we're talking about at this point in time.

Yes. One curiosity question. You were showing those breakdown filters. Can you -- are those just standard fields? Like let's say, I had a custom field but I want to do a breakdown filter by product family or by geography, for example, would I have -- or business unit, would I have ability to do that?

So those questions, those data points that are in the breakdowns, completely configurable. But any structured data that you have on a record inside of the ServiceNow platform, you can make available as a breakdown. So if you've got a field already that you didn't see me you show here today and you've created your own custom field perhaps for your process, those are going to show up inside of the platform again. Big benefit to this in platform approach to process mining is the fact that it is part of that one single platform. So all the stuff that you're doing on the platform, we just take advantage of that. We inherit that, it's not like you have to do any magical coding or any special kidney twist to get access to the things that you've done specifically for your organization.

Yes. And on that point, especially when you get into the AI, the clustering analysis and root cause analysis, do I need to be a data scientist or an AI expert or do I have to sit there and label a whole bunch of data in order to train a model like.

No. The beauty of everything that you just saw there. It's all configurable via lists and forms on the platform. One of the things that I jokingly say when people see this for the first time is that we've made it tremendously easy to get started. And I'd like to say, if you can dodge a wrench, you can dodge a ball or if you're comfortable building a ServiceNow report, you can visualize your process on ServiceNow and take advantage of everything that you just saw me do between the content packs that we provide out of the box that help jump-start you and that one little skill set of, hey, I just want to isolate the data that I need using a condition builder and you hit mine, you get to that screen that I was at and everything just pops up there, you can start doing the root cause, the cluster analysis without having to do any other configuration other than selecting what data you want to see inside of that project.

And then the other thing that struck me in the demo was just the phenomenal amount of data that you're looking at, is there any concerns about performance impact on the production instance? Or what's the story there?

Sure. I got, I think, a twofold answer to this question. So I'll address the performance one first because I imagine that, that's top of mind for anyone. We've designed this in a way to eliminate any sort of impact on your production instance. We use the machine learning infrastructure that we provide for both predictive intelligence and the virtual agent to do all the heavy duty number crunching for this. So essentially, how the process works is you create your project and you kind of configure what data you want to mine, you hit the mine button, it harvests the relevant data that we need in very small bite-sized chunks and then it passes that data off to the machine learning infrastructure due to the heavy-duty and crunching. That machine learning infrastructure that many of you may already know this it lives in the same data center as your instance. And then once that heavy-duty number crushing is done, that spits the model back, clean everything up that it needs to clean up and you're now mining and you're looking at the screen that you saw on the demonstration there. So no impact on our performance perspective. Second part of that answer is usually, how much data can I mine or should I mine. This is one of those areas where people see this, they get super excited and they just want to throw all their data into the solution. But I look at the last 7 years of my data. And the answer is you could. But that's probably not the best way to use the solution. If you think about it, what we're trying to do here is trying to identify inefficiencies within your process. I would hope that your process has changed, matured and evolved over the last 7 years. And usually, for most customers, what we tend to focus on is the last month or the last quarter of data because that usually is a relevant set of information to help us understand how the process is working today and where the inefficiencies might be. And that really the highest level answer to the question about what data should I mine is let's think about a relevant data set for the process that we're focused on to figure out how to answer the questions we want about the inefficiencies and opportunities to improve within that workflow.

Yes. The other thing I was wondering about is the kind of the persona of people within a customer organization that would look into this. A lot of customer organizations have things like PMO office, like for project management offices. I'm wondering if they should also have almost like a PMO office for process optimization because it strikes me as almost being not only about the technology but being a discipline, right, like a methodology or an approach to driving that. Just any thoughts or insights on that point.

Yes. I mean, again, as evidenced by the poll that we had at the beginning here, this is a new thing for a lot of organizations, like getting this level of depth and looking at your processes this way and for opportunities this way. So we're kind of seeing a gamut of different ways in which organizations are starting to approach this or are approaching this. But in some organizations, they actually have centers of excellence that this kind of discipline sits in, where it be like, hey, a business unit, a process owner or a process knee or somebody that owns the process from a line of business perspective, they would partner maybe with somebody in a center of excellence that has a little bit more knowledge about ServiceNow and process analyst skills together to start answering these questions. I think the thing that we're learning most often is that this is not a one-person type of situation. Very often it involves like I said, a process knee, along with somebody with has a little bit of knowledge of how to use the solution. And together, they're able to come up with the best opportunities to impact first. That's usually a 2 to 10 situation. A little bit of analyst skills that know how to navigate the tooling and then you need to have somebody who really understands the process and then what the data is telling us. So we know how to act on what the insights there and what the best way to improve the process would be. So I'm finding that once we start to -- customers start using it, it really helps improve collaboration inside of the organizations between different parts because now they're seeing things about the process that they've never seen before, and most processes are touching a number of different stakeholders. So it gives us a data-driven way to have conversations across different parts of the organization that we haven't been doing before. They haven't had before.

Awesome. Well, that's all I had. Did you have any other thing that you wanted to add before we kind of wrapped up or...

No, I think let's just check the Q&A in here, I don't know if that's the next slide in here, but let's check the Q&A and see if we got any questions that have come in. And it looks like we definitely have a few. We've got one that's asking where they can get more hands-on training with this solution. So a couple of things to point out there. There should be a bunch of resources listed as part of this -- the window that during or the -- I forgot the name of this, which we're using right now. But there should be some resources as part of the interface that you're in right now. But in addition to that, there's a number of different opportunities to learn more about process optimization. One, is there's a Now learning course on the nowlearning site called Process Optimization Essentials. That's a good way to learn a little bit more. If you go out to the ServiceNow community at community.servicenow.com, there's a process optimization product hub and there's a number of different recordings out there. We host a monthly academy session. So there's about 13 or 14 different recorded process optimization academy sessions out there that dig deeper into a lots of different areas of the solution. We've just kicked off something called the process optimization use case series. So some common use cases like how to use process optimization to do SLA breach analysis. So how to use process optimization to channel analysis? Or how to use process optimization to analyze rework that's going on within a given process. So we've got 5-minute videos out there for those kind of use cases, another great way to learn more about the solution. So no shortage of opportunities to learn more about process optimization. I'm just looking at some of the other questions that have come in. And please, if you have questions, enter them in the Q&A. I'm trying to interpret this question here. I'm formulating how to best to communicate it to the group. So the question really, I think, is how do I use process optimization to understand reopened cases where a case gets closed prematurely, and then it has to be reopened and more work needs to be done. So one way to do that is if you remember back to those finding definitions or those improvement opportunities, the cards that were on that summary of insights page, there is a specific out-of-the-box configuration to highlight those tickets or cases that get reopened after they were closed. So that will help you narrow down your focus on those. And actually, it's one of the use case recordings that I just mentioned as well in the community. Once you've narrowed it down to that area, that's when you can start using things like, hey, let me look at the breakdown to see which is silent groups or which agents are having their tickets most often -- reopened most often. Or you might want to use that root cause analysis to help you understand and get more information around where does it continue your results? Or maybe when you get down to the detailed records, you use that interactive analysis. That's one way that I've helped work with customers on answering those types of questions using interactive analysis on just those cases that were reopened to identify, hey, which types of tickets and which agents are working on those, and that helps narrow it down. So maybe it's a training opportunity that's there or knowledge article improvement potentially. So that's one way to attack it. A number of different ways to address different use cases with process optimization. Any other questions that you have, please put them in the Q&A where we're coming up on. I'll just call it last chance for romance here. If you got any additional questions, please put them in the Q&A section now. If not...

All right. How about if we go back to the quote, right? So the quote that we had was, it's good to have a journey. It's good to have an end journey towards, but it's the journey that matters in the end. So as you saw today, we had a wonderful journey into process optimization and the insight that you can gain. I guess the call to action or the challenges, will your journey in process optimization look like? And thank you for joining us.

Yes. Thank you, everyone, for your time here today. Please check out the community and some of those resources there. If you have questions, you can post them in the community or reach out to your ServiceNow team that you work with on a regular basis. Just so you know, this webinar, along with many others are going to be available in an on-demand fashion. If you check out that URL that's on the screen there. So if you want to tell your friends that you saw this amazing webinar and you want they should see it as well. They can go to this location, find this webinar as well as some other webinars that we've made available.