Scott Hirsch 0:00:12.2:
Hi. Welcome to our RPM session, revenue performance management. The next four sessions we have programed to talk a lot about all the work we've been doing over the last year or so to really bring all of the revenue performance management products on to our application framework and inject them with AI. We've planned four sessions for you this afternoon. I hope you can make it to all, or most. This is the opening session. We're going to be talking about the CRO future and the CRO's AI playbook. In session two, I will be doing a fireside chat with Anaplan's SVP of revenue operations, Justin Edwards. That's a great opportunity for folks in the audience to be able to ask questions as well, so we'd love it if you would stay for that one. Then our third session is going to be a deep dive into our Sales Forecasting application, which we launched in March. So, brand new, hot off the presses. Then at four, we actually are doing a fireside chat with one of our customers in [unclear words 0:01:15.8]. So, really excited to have you stay for all or as many of the sessions as you possibly can.
Scott Hirsch 0:01:22.6:
Right now, I'm here to kick off our opening session with Kyle, myself, and we have a guest, Anthony Erickson, from SBI, who's going to be coming up for a fireside chat about halfway through the presentation. The agenda, just really quickly, we really want to introduce this concept of the CRO of the future, and what types of tools will be necessary to actually lead sales in this new universe we're entering into, with a very complex buying journey, and so much change, and a need for so much agility. Just based on global factors, tariffs, all of it. We've never had to keep up with the pace of change that we've had to keep up with now. Then bring [?Tony] up for industry perspective, and then I will be talking a little bit more about our AI strategy. I'll try not to repeat from the opening plenary and do some storytelling around some of the RPM stories that we're bringing to life with our AI solutions. Then finally, our roadmap for what's coming next. We'll have time for some Q&A at the end. So, without further ado, I'm going to hand it off to Kyle to get started.
Kyle Welling 0:02:32.0:
All right. Thanks, Scott. All right, so like Scott mentioned, I want to start off with our perspective on what we believe the CRO of the future is. You can't talk about the CRO of the future without talking about sales. As we all know, sales has never been easy, right, but today it's more complex than it's ever been. These challenges fall into two primary buckets. We've got our external market factors, as well as our internal company challenges. So, we'll start with the external pressures here. So, first, the very nature of the products we sell is incredibly complex. Tech companies have moved from on premise to SaaS to now generative AI. Manufacturing companies are now bundling IoT with services and software, and managing a very complex after sales and distributor network. Life sciences has gone from simple pills to very complex, personalized, therapeutic remedies. In addition, the era of growth at all cost is over, right? The focus is now on efficiency, and companies are scrutinizing opportunity profitability and customer lifetime value to drive go-to-market investments and boost seller productivity.
Kyle Welling 0:03:53.9:
These external pressures expose and amplify some of the internal challenges that we see within our organizations. The biggest internal challenge we see is disconnected planning and data. Think about your sales, presales, SDR, customer success organizations, as well as other teams like marketing and finance, each of them working independently in their own silos. This results in misaligned plans, missed opportunities, and an increasing cost of customer acquisition. Teams are also caught in manual spreadsheets. This makes it impossible to do the valuable scenario modeling and analysis for plans, and makes it a nightmare to collaborate with go-to-market leaders to drive insights, get input, drive decisions, and ultimately outcomes. The result of all of this is a crippling lack of agility. This means there's no ability to formally plan several times a year, making it impossible to adapt to those external factors and internal challenges that we all face.
Kyle Welling 0:04:59.4:
The customer journey is changing, as well. Research shows that two-thirds of buyers prefer a rep-free experience. This means your go-to-market teams must be strategic advisors focused on the right accounts at the right time, with sprawling roles and role ratios that effectively cover not only the buying cycle, but also the entire customer journey. This is a research that was done by the Winter Group and published last month, and one of the most interesting things that I pulled out of this - and there's a little footnote down here - is that a source of information that barely existed two years ago has now surpassed Google and become the second-most accessed piece of information when buyers are researching vendors. So, LLMs have played an increasing role in buyer information and making them smarter. Another statistic is that 83 per cent of buyers are already familiar with vendors before they land their initial sales call. So, buyers are becoming increasingly smarter in the sales cycle, and sales teams and sales operations teams need to react.
Kyle Welling 0:06:10.4:
Oh, one more. Wrong button. All right. So, how can we help you adapt your strategy and your plans to this new normal, to this new customer journey? With customers controlling most of the customer journey, it's incredibly important to know your segmentation and how and where to focus not only your land versus your expand motion, but also how to align your different segments, your A, B, C, D accounts across your direct sales and overlay organizations, customer success, or even marketing. How you allocate resources across those different segments and teams could have a material impact. According to Gartner, organizations that simplify their selling roles are four-and-a-half times more likely to be a high-performing organization, because they aren't asking sellers or revenue producers to do everything. With the focus on the right roles and the right place in the customer journey, you can drive more successful outcomes. It's not just your segments and your capacities that need to keep up; it's how you assign accounts to territories, reps to territories, and how you set quotas aligned to pricing and your evolving revenue models to drive the behavior and reward the right behavior.
Kyle Welling 0:07:25.9:
According to the Alexander Group, 97 per cent of organizations changed their comp plans last year in response to a shift in sales strategy and changing sales organizations, where we've got different teams covering different aspects of the buying journey. As your incentives become more complex, connected data, processes, and governance is critical because you can't grow plan complexity by exception. So, to summarize, the role of the CRO is changing, and the CRO of the future needs to have visibility into what's coming before it becomes a problem. They need to be able to react to those problems. They need to model the different outcomes, but modeling is just half of it. They need to also be able to implement, measure, and react to those outcomes in real time. So, now the question is, how do we give sales leaders and revenue operations teams the tools they need to make better, faster decisions? Revenue operations leaders need to be able to see the whole board. Planning is no longer an annual exercise where we get to set it and forget it. Many of our customers are planning several times a year to adjust to those external and internal influences. As a result, the process needs to be faster; it needs to be more intelligent, and closely aligned to other stakeholders between sales, customer success, HR, finance, and others.
Kyle Welling 0:08:54.2:
Sellers are under increasing pressure too. With increasing quotas and more intelligent buyers, sellers need to spend more time selling, and they need to be smarter about how and where they spend their time. A critical role of the sales operations team is to not only give reps insights into who has a propensity to buy, but who's ready to buy now. So, I want to take a break here and invite one of our partners, Tony Erickson from SBI, on stage to give a little bit of insights from a market perspective. So, please help me welcome Tony to the stage.
[Applause]
Anthony Erickson 0:09:37.1:
Appreciate it, yes
Kyle Welling 0:09:40.3:
So, Tony, I thought we could start by a brief introduction of yourself, SBI, and what you guys do?
Anthony Erickson 0:09:46.7:
Yes, thank you. So, SBI, we are a growth advisory firm, meaning that if you want us to help you with finance, supply chain, or HR, you have called the wrong company. So, we are go to market all the time. That's what we do. We start with, what is the overall growth strategy? What is the thesis? If it's a private equity company, what is the mandate by the board if it's a public company, and how do we solve for that? It's usually a combination of adding more personnel, making those personnel more efficient, increasing the expectations of those personnel. So, that's what we do. So, we work with companies specifically to help them solve for their growth mandates.
Kyle Welling 0:10:30.3:
Great, thank you. So, I'm going to start with a pretty broad question here. So, Tony, from your perspective working with CROs every day, how is the role fundamentally changing as AI and new go-to-market technologies reshape how revenue teams operate?
Anthony Erickson 0:10:46.2:
So, one of the most significant changes of CROs right now seems to be the person in the chair. A lot of companies are flipping their CROs right now. I was actually talking with one of the big four recruiting firms not too long ago about a search they're running trying to find candidates. I asked them if the change is for change sake, or if they actually need something different. Companies are even a bit confused on that right now, because there isn't someone out there who has actually implemented an agentic sales force. So, you're asking people to do something that has not been done before. There will be people who rise who figure that out, but there will be a lot of people who, essentially, get replaced because they can't figure it out. So, that is one of the biggest challenges right now, is that SaaS-pocalypse, as we heard in the general session, is hitting a lot of companies, and there are a lot of companies that are not making plans. So, change is becoming the new constant. The reality of it is, as people are making change, they want it much more data driven.
Anthony Erickson 0:11:56.5:
This has been happening for years: we are moving away from the art-driven side of sales into the science side of it. To do that, you've got to have the right information at your fingertips in your reps' hands to the right prospects at the right time. So that's what people are looking for now, is data-driven CROs who can execute a systematic function based on data and facts, not based on gut feel and conjecture.
Kyle Welling 0:12:25.4:
So, I'm going to go a little bit off script here, because this isn't one of the questions we talked about, but as we see that shift from art to science, sales has always been a very soft skill, relationship-based profession, right? It relies heavily on your ability to communicate and influence and drive outcomes. Are we losing the artists in favor of the scientists in the office of the CRO?
Anthony Erickson 0:12:55.1:
That's a tough one. I would say no, because at the end of the day, we will build agents; agents might negotiate with agents. You could envision a future where MSAs are being negotiated via two agents that are talking to one another.
[Unclear speech from audience 0:13:17.4]
Anthony Erickson 0:13:21.4:
Should we answer it? [Laughs] At the end of the day, people buy from people. So, a lot of the legwork and a lot of the pre-work will be happening. This is one, it's kind of close to home for me, because I have a college daughter, sophomore in college, and she just got elected as president of the sales club at her college. She wants to go into sales, and I'm sitting here like, what is the entry level sales job of the future? One of the interesting things about it is, I think it's going to actually skew back to some of the art, because some of the science is almost table stakes now, because these kids are coming out of school, growing up with GPTs at their fingertips. So, I think we're going to see a model where we're going to agentify the ISRs and the BDRs, and that'll all be done through magic, and leads will just come in from the heavens. We're going to go back to a seller who's going to get a carved out territory that's probably going to be a bunch of crappy accounts when they first start, and they're going to work it on relationships. I think we're going to see people going back to the basics in selling, because we'll have all the other enablement to help them position for it, but at the end of the day, people want to buy from people, and people want to talk to a person. At some point when you're signing a contract, agents don't sign deals; people sign deals.
Kyle Welling 0:14:46.9:
Yes. So, picking on that, that art versus math - or science - theme, territory design has historically been part art, part science. We've seen data and AI changing that, especially when it comes to creating more equitable… How have we seen data and AI change that as we see AI and data change? What qualifies as equitable and how we drive performance-driven coverage?
Anthony Erickson 0:15:11.5:
So, one of the things we do with a lot of companies is we help them actually structure their go-to-market model. Should they be in a hunter-farmer model? Should they be in a geographic model where they're just - everyone's on a patch of dirt, selling to a ZIP+4? Should they be in a vertical model? There's only so many different ways you can organize a sales team. The interesting thing, though, is that, inevitably, however you organize, every time we start working with a company for the first time, they have usually gotten lazy in territory management, and you end up with the haves and the have-nots, and you end up with the people who have 500-plus named accounts in their name. When you actually go into the CRM, you see that they touched six per cent of them over the course of the last year. So, all those other ones aren't even being worked. In the future - and this is one of the things I'm excited about, that Anaplan does well, is it actually allows you to dynamically reallocate territories. Great companies do that on an annual basis, but we're going to start seeing companies go to semiannual and even quarterly, because in the future, we're going to see people putting their sellers on to the right opportunities, rather than hiring a bunch of new sellers.
Anthony Erickson 0:16:27.6:
So, I was talking to a board chair recently who's chairman over several different companies, and the comment that they made was, 'We're about to enter annual planning season. I know you said we don't have that, but we are about to enter it. As we go into that model,' he said, 'Every single company I'm working with, they're going to get two messages. "Your target's going up, and I'm going to expect you to do it more efficiently. It can be through less heads, you can pay them less, or you can find a way to introduce technology, but it's got to be less than a zero sum game, and we're going to expect more."' So, we are now heading into 2027, for those on a calendar year. We are into the era of people expect AI to actually have a justifiable ROI, because we've been talking about it for a couple of years, and now we're into the show-me moment. That's what this annual planning cycle is going to have in it.
Kyle Welling 0:17:22.7:
So, I agree with that. There is an increasing expectation that AI delivers value, but how we leverage that AI is still a big question mark. One of the questions I have here is, if AI tells you the optimal way to rebalance territories, but it disrupts rep ownership and continuity, what wins? How do you pick the right path forward?
Anthony Erickson 0:17:50.0:
Now we're going back to art. At the end of the day, when you're balancing territories or thinking about territories, you have to think about breakage, but don't think about breakage as the one person who carries the quota; think about it as a team. So, if they're an active client and we're looking to expand, just because a seller who's in more of a hunting motion got us in the door, they don't have to still be involved if the CSM is continuous and that person is still going to be involved. So think about breakage as a team; don't think about it as one individual, because if you're fundamentally going to shift territories, you're going to have a lot of breakage maybe at the quota-carrying level. If you've got a PS model, or if you've got a CSM model, or if you've got some sort of an ongoing model to serve your clients as long as that remains continuous, you're not really going to be that disruptive. Now, one thing that reps don't like is when accounts get taken away from them, but one thing that they do like is when lucrative accounts are given to them, and you've actually got good data to justify why they do that.
Anthony Erickson 0:18:53.2:
A couple of years ago, we were working with a company, and we were just doing these models using Alteryx models and other data modeling, and we created something called the hot territory matrix, because this company was adding so many reps, they actually used it as a recruiting tool to show, 'Hey, this is why this is such a lucrative tool.' They were going from 100 reps to 200 reps to 300 reps, and they were having to recruit reps into a territory, not just into the organization, and show them why it was such a lucrative territory, and how much potential there was. Having that kind of data is not just good for your existing reps, but it's great for ramping, it's great for recruiting, and it's great for retaining.
Kyle Welling 0:19:35.9:
So, when we talk about the age of AI, there's a lot of AI theater in revenue tech right now, with dashboards and copilots. What separates tools that drive real decisions from those that just look good in demos?
Anthony Erickson 0:19:53.5:
I think it's going to be the things that ultimately yield more selling time. So, on average, when we work with companies, one of the first things that we do is, we do a time study, because if you actually survey hundreds of sellers and ask them where they spend their time, shockingly, they will tell you. Typically, they even over-inflate how much time they're actually spending on selling versus non-selling activities. More often than not, you would be shocked at the number of people - when we surveyed thousands of reps - how many of them are spending less than half their time actually doing selling activities. So, the AI tools that are going to win are the things that minimize all the back-office BS, the things that get them back out into the field, that help them get to a client so they're doing more customer-facing stuff. If you just think about that simple metric, say the average company out there right now is running around 50 per cent selling time, and you have 100 FTEs selling, you could unlock the equivalent of 50 net new reps just by getting to a best in class of 70-ish per cent selling time. That is virtual capacity to unlock right there in your organization.
Anthony Erickson 0:21:09.5:
If you don't have the AI tools to give you the visibility into where people are spending their time, how much time they're actually spending, and now you've got all that. You've got that, with tools that connect into emails and calendars and calls, and you understand where they're at at any given moment. If you're not leveraging that kind of tool to unlock the latent capacity inside of your company, you're going to get lapped by your competitors, because they are doing it.
Kyle Welling 0:21:34.6:
So, a follow-up question to that. So, for the CROs in the audience, or revenue operations leaders in the audience who are still operating with relatively static plans, what's the first step they should take to move toward a more adaptive AI-driven model?
Anthony Erickson 0:21:51.7:
I think the first step is they actually - you need to have an AI strategy. Right now, every single piece of rev tech that you've got will tell you it's got AI embedded in it, but do you actually understand how that AI is supposed to enable your reps to be more efficient, to be more effective? Most people are getting the story - to your point - the smoke and mirrors for all of their install base vendors on how they're using AI, but is it really something that is agentic? Is it really taking time off of their plates, or is it another way of reporting something that they're already doing? Is it doing tasks that otherwise had to be done manually? To me, that's the big thing that people need to figure out is how are they actually removing stuff from the front line and the management layers? All that mundane stuff should just be done, and we're in a world where it can be done.
Kyle Welling 0:22:44.7:
So, speaking of very manual and mundane, forecasting is still one of the biggest pressure points for CROs. How is AI changing the forecast confidence, especially when it comes to understanding communicating risk in the pipeline?
Anthony Erickson 0:22:59.4:
Yes, so forecasting is where it's gotten really interesting. I know you guys are going to be showing the forecasting solution, but the number of inputs that you can now put into forecasting tools to confidently forecast is staggering. No longer do you have to go around and talk to every single rep and do your roll up. I started my career at IBM, and it was fascinating, the way that that cadence ran. On Monday, it was the front-line managers. On Tuesday, it was the regionals. Then all the way up to Sam Palmisano, on Friday, got his report out. Then Monday, we started over again, and went through the whole process. That was just a ton of wasted time, just reporting on stuff that is systematically available and at our fingertips, and you need to move to a forecasting model where you're hitting on the moments that matter, not asking for things that are systematically already available to you.
Kyle Welling 0:24:00.0:
Great. So, one final prompt here is, any final words or message to the audience, or maybe people who are investigating in AI strategy or how to improve their revenue operations teams through AI or inefficiencies, what would you tell them?
Anthony Erickson 0:24:15.2:
First of all, I love the fact that you prompted that question. We've all changed our vernacular in how we ask questions now, and we work in AI lingo. The most important thing is, what I would leave people with is a point that we were making a few minutes ago. This is going to be the year that you have to demonstrate returns on your tech investments. It is going to become expected, because we're all coming out of - for many companies - what was a pretty difficult year. Then tariffs rolled out; then wars happened; then other things happened. People are getting to the point where we're done with the excuses. The world is a difficult place. There will always be some macroeconomic challenge that we're facing. Even in these times, there are winners and there are losers. So, if you're not one of the winners, what are you doing to change your trajectory? We're kind of to the point where we're moving away from pilots, we're moving into results, and we're going to stop making macro excuses, and we're going to start driving for expectations.
Kyle Welling 0:25:20.3:
That's great. All right, thank you so much. I appreciate it.
Anthony Erickson 0:25:23.0:
Yes.
Kyle Welling 0:25:24.2:
Look forward to the conversation going further.
Anthony Erickson 0:25:26.1:
Yes, thank you.
Kyle Welling 0:25:26.9:
Thanks.
[Applause]
Kyle Welling 0:25:34.5:
All right, so now, speaking of AI, the next section here is about AI and what Anaplan are doing about AI relative to RPM. So, Scott Hirsch.
Scott Hirsch 0:25:52.3:
Hi, there. I'm going to try not to repeat what was covered this morning, and instead just add on to it, with a little bit more storytelling about what we're making possible with go-to-market planning and revenue performance management. I think [?EJ] went through this slide in great detail this morning. Oh, and by way of introduction, I lead solutions marketing for our revenue performance management line of business. I actually came to Anaplan through the acquisition of a company called Syrup, which was an AI-powered inventory forecasting solution for fashion and apparel. It's actually been built into the Anaplan platform since we were acquired last September. So, I come from an AI company, and so I'm going to try to bring a little bit of an outside-in perspective into how I talk about this. EJ went into great detail on this this morning. The way that I think about this slide is the bottom layer, Anaplan's been doing for years and has incredible strength and foundation there. Just above that, ADO is required for AI. Data harmonization, data synchronization, the ability to access data across multiple business use cases and context.
Scott Hirsch 0:27:00.7:
The next row up, applications is really the secret sauce, and I'm going to talk a little bit more about that in a second, but that really shrinks the time to value for any customer who wants to adopt an Anaplan solution. Then the magic on top is the AI stuff, particularly around CoModeler, which I'm going to talk about in some detail, and is maybe slightly different from your average run-of-the-mill agentic story. That's my introduction! So, I'm not going to go into a ton of detail on this slide, other than just to say the CoModeler and Sales Analyst are available today. The way to think about those is CoModeler is the tool for model builders, and really amplifies their ability to execute quickly and in an agile way any fantasy idea that they have for a model they might want to build. I think of it almost like vibe coding for Anaplan model builders. Sales Analyst is available today. Think of it as for users. Model builders, it's for users, the ability to quickly ask a question of any data that's in an existing Anaplan model and get an answer back. Currently available in our Territory and Quota application. I think in the next month, it's going to be available for all of our other go-to-market planning applications, and then Sales Forecasting around mid-year, if I remember correctly.
Scott Hirsch 0:28:23.0:
Agentic framework is what's coming next, that's your ability to build your own agents using a common framework that accesses any Anaplan data. Then coming after that is sort of enhanced tools that could be used as independent agents or by any other agent. So, for example, the tool to actually build a workflow is something that I really don't see a lot of other companies being able to do that, other than Anaplan, because we have the business context built into the applications, and we have access to all of the data. I'll talk a little bit more about that in a second. In terms of Anaplan CoModeler, like I was saying before, I think of this as the vibe coder. The two use cases that are de rigueur, kind of obvious or the build and optimize use cases. So build is like, 'I have an idea about a model dimension that I want to add to my model. Let me just go into CoModeler and see if I can do that really quickly.' So, that might be something like - we have this notion of wanting to retire territory. Maybe a territory goes dark, or maybe our market is really changing. If you wanted to go in and very quickly just model out and see what it would be like to add a status for any territory, and that status might be active or retired, you could quickly and easily do that.
Scott Hirsch 0:29:45.8:
Optimize is more about using the agent to figure out, are there any suboptimal formulas, or ways that we're accessing data inside of the model that are impacting model performance? So, those are the straightforward and obvious use cases. One of the ones that's more interesting is this notion of extend. So, the idea of extend is that you can… The way I like to think about it is you're never locked into the model that you built! You can always take the model, you can build, and you can extend it further, and have the AI actually do all of the difficult work of making sure that the model didn't break. So, for example, in that scenario where we were adding that status about a territory, there may be many places where that dimension might get accessed in the process of doing capacity modeling, and seeing how retiring a particular territory might require that you rebalance all of your existing quota and coverage. The co-planner would do all the hard of making sure that the model didn't break, so that the planner didn't have to spend hours coming through all of the dimensions of the model to figure out how to avoid the breakage.
Scott Hirsch 0:31:09.0:
The other value proposition of CoModeler is, especially for an extend use case, is you're never waiting for Anaplan to release the next version of the application, because you have complete flexibility to extend the application that you deployed out of the box at any time. So, there may be some unique factors to your business that are just entirely business from what came out of the box. You can build those in CoModeler as extensions, and then they're part of your model, and you're still able to upgrade, you're still able to change your model in the future. So, there's a lot to CoModeler that's extend use case that a lot of our customers should be interested in. I'm going to have to move on a little bit more quickly, because I think we're almost out of time. This is my last slide. I just wanted to bring it full circle back from what Kyle kicked off with around different changing dynamics around the customer journey, and how we actually sell to customers.
Scott Hirsch 0:32:06.6:
So, the models that we have today to do things like go-to-market capacity planning and territory and quota may require dimensions that we're currently not even managing today, or we may have theories about better ways to segment the business so that - to Tony's point - you have the right sellers focused on the right opportunities at the right time, and you're not ending up in a scenario where somebody has 200 named accounts, for example. So, Anaplan's AI and CoModeler in particular will enable you to be able to run those experiments and do that. So, some things like - we're having Zoom talk later on today, and I'm really curious. I'm going to ask the question myself, if it doesn't get asked, but how does PLG, how does product-led growth factor into how they do account segmentation and scoring? I'm sure they have some accounts that there are triggers, where they see, oh, it's time for an AE to reach out. That could be modeled inside of Anaplan and brought into the Go-to-Market Capacity Planning process from the very, very beginning. That's going to change over time. That's not something that you can do once a year. It's going to change every single month and every single quarter. So, you can use Anaplan to dynamically model those scenarios.
Scott Hirsch 0:33:20.5:
I'm going to go ahead and hand it back off to Kyle here, to go through some of our roadmap highlights.
Kyle Welling 0:33:32.5:
All right, thank you. All right, so I'm going to try to give this its due time while also making sure we leave a little bit of time at the end for questions and answers here. So, I wanted to talk a little bit about what's coming next. Today, we've got four different applications in market today. We started off with Territory and Quota. We're currently on version two of that. We've got version one of capacity planning and account segmentation. Then as Scott mentioned last month, we released our first version of Sales Forecasting. So, all of these, so all of our sales planning applications will come up with new major releases next month. Segmentation and Scoring and Go-to-Market Capacity Planning we'll come out with version two, and we're actually releasing version three of the Territory and Quota Planning. The thing that I'm actually most excited about that we're working on is the [unclear word 0:34:27.7], our incentives. So, Anaplan is now starting to develop a solution toward a full Incentive Compensation Management tool, started with credit allocations.
Kyle Welling 0:34:37.9:
So, the number one imperative of any sales compensation team is to make sure that we pay people correctly and on time. We see that a lot of companies today struggle to do that very basic task. So, I mentioned that we're going to start with our credit allocations module here, and so you might ask, 'Why credit allocations?' It's a good question. So, as companies diversify their offerings by building hardware and software in services and expand into new global markets and adopt new revenue models, the straightforward process of sales crediting has become an increasingly more complex puzzle. Role simplification and proliferation of overlays can result in tens or even hundreds of different credits per transaction. Not to mention role-based splits, milestones, consumption-based revenue models, and so on. So, crediting can be highly complex, and if you get your crediting wrong, the calculation and the payment is absolutely going to be wrong, resulting in very time-consuming and manual despite resolution, and ultimately under or over payments.
Kyle Welling 0:35:47.1:
So, unlike other applications like territory management or Capacity Planning, ICM is not a single solution. So, we're taking a modular approach, starting with the right foundation, which we believe is credit allocations. The credit first approach delivers immediate value and the most critical and complex part of the entire incentive compensation process to reliably apply complex logic across high volumes, and transactions that make sure every payment is calculated correctly. This will be followed by further adaptations and additional development around the other modules here, around payment calculation, reporting, MBOs, and in-plan modeling. So, there's more to come on this. So, in addition to the ICM, obviously, we're working on a ton of additional development and releases, starting with ADO. So, starting next month with the release of ADO natively built into Segmentation and Scoring and our capacity models will now have ADO natively built into all of our RPM applications. So, ADO being the core foundation to all data management, and it brings enhanced data governance to orchestrate complex workflows, and with greater control and efficiency.
Kyle Welling 0:37:09.9:
So, I'm also very excited to announce the updates that we have coming to Segmentation and Scoring. Today, we have holistic market analysis across segments where you can define and refine your ideal customer profile, tops-down market sizing to identify and remain wallet share. Coming in v-two, we're now enable dynamic, go-to-market hierarchy planning. This takes all the work you've done and enables you to start building dynamic hierarchies from wallet analysis, rules-based segmentation and account coverage resource modeling. In addition, we will also have our sales agent natively built in. Also coming next month is our updated Go-to-Market Capacity Planning model. So, with the two new features I want to highlight coming with Go-to-Market Capacity Planning are ramping modeling. So, now you can analyze historic new hire performance using cohort metrics to define realistic ramp curves and improve quota-to-productivity assumptions.
Kyle Welling 0:38:13.5:
The second would be a best time to hire recommendation. So, it recommends optimal hiring timing based on modeling tradeoffs between expected revenue generation and cost through draws and incentives. In addition, we will also be including Sales Analyst native to the Go-to-Market Capacity Planning model. Last, but certainly not least, will be the significant update that we're making to our Territory and Quota model. So, Territory and Quota is now being completely rebuilt on the Polaris engine to provide better performance around sparsity and scale. So, enabling more granular top-down and bottom-up quota planning, tied to corporate targets to better support in-year quota replanning and adjustments. Scenario modeling is pervasive across all planning processes, with structured approval workflows for quota territory changes built into the application. In addition, we've updated the UX to incorporate significant advancements in our UX capabilities around mapping and additional insights.
Kyle Welling 0:39:19.6:
So, this is a very, very busy slide, and I certainly won't read the whole thing here. This is just to articulate the advancements that we're making not only in AI, but what we're calling the Revenue Command Center and Scenario Modeling. So, right now, in the Revenue Command Center, we have revenue orchestration and forecasting. In the future, we'll have finance integration, and in the future - and a complete ICM solution. For intelligence now, we have Sales Analyst currently in the T&Q. Next month, we have Sales Analyst built into the other go-to-market planning models. Ultimately, we want to have a future where forecast risk analysis and explainability through opportunity scoring. On the scenario modeling and what-if analysis, today, you have sales forecasting, your best, worst, and likely case modeling. The future, we want to have risk-adjusted recommendations to help have a better, more informed process. All right, I am going to very quickly move into the Q&A portion, because I know we only have a few minutes left.
[Applause]
Kyle Welling 0:40:25.5:
So, any questions, please feel free. Otherwise, I'll be around for a few minutes after this session to answer…
Scott Hirsch 0:40:30.7:
If you have a question just raise your hand. I'll bring the mic over to you.
Kyle Welling 0:40:43.2:
All right.
Scott Hirsch 0:40:45.0:
Please come and talk to us between the sessions if you have a question, or later on today. We appreciate you coming. Thank you so much.
Kyle Welling 0:40:52.4:
Thanks, everybody. I appreciate it.
[Applause]