Scott Hirsch 0:00:09.6:
Hi everyone. Welcome to go-to-market strategy, from revenue to reality session. I'm here with Kyle Welling, and we'll be speaking to you for about the next 45 minutes around how AI superpowers the CRO of the future. By way of introduction, I wanted Kyle to introduce himself first, and then I will do an introduction and take you through some introductory slides, and then we have a pretty full agenda for you for the next 45 minutes, but we do have time for Q&A, so there will be time for Q&A at the end.
Kyle Welling 0:00:41.1:
My name's Kyle Welling. I lead the revenue performance management, or RPM domain here at Anaplan, so responsible for all RPM applications from a go-to-market standpoint. That's part of my role. The other part of my role is I lead our delivery advisory organization for all applications across the board, so revenue, supply chain, finance and workforce planning. So I've been here at Anaplan for about ten years now, but I've been in the Anaplan ecosystem for about 13, so I've been around for a really long time, and I've never been more excited about our approach than I have been lately with applications and how we're moving forward, and how we're solving real problems with real solutions. So yes, I'm looking forward to talking more about that in a little bit.
Scott Hirsch 0:01:23.5:
Fantastic. Thank you. Yes, my name's Scott Hirsch. I joined Anaplan last September. I came through the Syrup acquisition. Syrup was focused on retail. We were doing inventory optimization for retail powered by AI models, really to get the inventory to the right location and at the right price point to sell through at full price. So, it's very much a margin and revenue maximizing value proposition. I have 15 years of experience doing go-to-market at SaaS companies, though, so like RPM and go-to-market strategy is very near and dear to my heart, and even though I've never been a marketer selling to go-to-market strategies, CRO and rev ops people, I've worked very closely with them for the last 15 years, so it's almost like coming home, in a way. One of the things that I observed when I first started working with Kyle and the rest of the team on RPM was that there's so much overlap between what we were doing at Syrup and go-to-market planning, and it's not exactly intuitive out of the gate, but we were really focused on getting the right product to the right location, and in the right quantities to meet demand, and that's very much what go-to-market planning is trying to do as well. You're just trying to make sure that you have the right capacity, the right sales coverage, the right data upon which to optimize exactly how many people you need in the field and what their quota should be to be able to achieve your revenue objectives, and then constantly adjust that throughout the year, which is very much the way retail works as well. It's really interesting that, when you stand back and squint, like so many of these use cases are so similar, even across an organization as complex as Anaplan.
Scott Hirsch 0:03:06.0:
So, we have a great agenda lined up. I'm going to do some introductory remarks around the CRO of the future. Kyle's going to come in and talk about our new application that launched today, Sales Forecasting, and give some more detail on that. You can also go and see a demo of that in the silent demo room I believe, if you'd like to later on. Then, we're going to bring in our special guest from Amazon Web Services to provide an industry perspective, and then we'll be talking a little bit more about our AI roadmap and how it ties into what we're doing with revenue performance management, and then what's coming next to the roadmap for the rest of this year. Then, like I said before, we'll have plenty of time for Q&A. I wanted to just start by saying, revenue is under pressure. Nothing has changed, but everything has accelerated and compounded to make the challenge of go-to-market planning and revenue forecasting and orchestration even more challenging in today's environment.
Scott Hirsch 0:04:04.8:
There was a story, if anybody was in the session this morning with Katie Bird from Nasdaq, and I loved her example that she gave when Joe Horsey asked her, basically, 'So what's the equivalent of the supply chain disaster of the containership losing 40 containers for Nasdaq?' because you would think that maybe there isn't the equivalent for Nasdaq, and she just, without missing a beat, basically, say, 'Oh, yes, something as simple as Anthropic issuing a press release and the market tanking, which is great for our trading business, but terrible for our index business.' Her point was, basically, that happens every day, and so we're constantly having to assess new market conditions and adjust on the fly, so without the right toolsets you're unable to do that and you're unable to react as quickly or as effectively than you would be able to otherwise. That's just one example. We're going to share some more examples in the course of the talk today about how using tools like Anaplan for revenue orchestration can help you really get ahead of any of the market changes that are coming and quickly react.
Scott Hirsch 0:05:13.6:
One of the key themes that I think is important not to overlook, is really the fact that the customer journey is also changing, so despite the fact that marketing conditions are changing faster than ever, the way customers buy is also changing really rapidly, and so, for example - I've been a marketer for 15, 20 years - there's almost no similarity between the customer journey of 15 and 20 years ago and the customer journey of today. I think it's not outrageous to say that search engine marketing is going to be a thing of the past in the relatively not-too-distant future, because so many of us are actually getting our questions answered using LLMs. We're no longer clicking on a link that takes us to a page that can be optimized for ad spend, and instead, we're doing a lot of our own research. That aligns to a larger market trend that's been going on for longer, which is people want to do their own research, because they don't necessarily want to talk to a sales person. Sometimes they're not even going to talk to a sales person until they've already made a selection of the vendor that they want to go with, or a very short list of the vendors that they want to go with.
Scott Hirsch 0:06:24.1:
So go-to-market planning, it's just less predictable than it was in the past, where you can rely on your SDR team and your marketing team to reliably fill the funnel using funnel metrics that have worked for years and years and years, and you have to be a lot more reactive and flexible in real time. So, I just wanted to tee-up the conversation just by saying that, because that's another reason why the ability to be more agile is going to be increasingly important for rev ops leaders in the future. Another story that I wanted to share was, this also transcends way beyond just traditional software sales, which I think is where a lot of these concepts and ideas really matured over the last 20 or 30 years, and other industries are having equally complex go-to-market challenges that they need analytics tools to help them solve. So, I wanted to ask, how many people in the audience here are from financial services? Okay, we've got a couple. So we've been working with an insurance company recently, and the amount of complexity in their go-to-market planning process was just really illuminating, in terms of, for any particular zip code for them, there were three different channels that could be serving that individual consumer, two of which were direct and one was indirect. For each of those channels, each of the seller types had different eligibility to be able to sell different products, and for each of those products there was also an additional layer of complexity around the profitability of each of those products, depending upon who sold it, whether it was through the wholesale channel, the director, or some other indirect channel.
Scott Hirsch 0:08:10.7:
Then, finally, the coverage for a particular zip code, even if they've done the research to know, oh, our target demographic is growing in this particular zip code, knowing the right mix of coverage to have in that particular zip code is a really critical problem for this particular insurer that they really wanted our help in solving. A layer on top of that, the fact that insurance also has no concept of quota - it's usually just a straight commission model - and there's no out-of-the-box one-size-fits-all go-to-market planning solution that would work. So that's just one example of how this transition away from SaaS point solutions and into platforms is I think a trend that's just going to continue as well, as the market starts to mature in terms of how it wants to leverage technology to solve these challenges. So, across the business there's new revenue models, there's subscription versus just annual contract value, versus usage-based pricing models, which adds additional complexity. It's something that Anaplan is already working on addressing. Then, different ways of segmenting your target customers, different ways of optimizing your go-to-market teams.
Scott Hirsch 0:09:30.3:
So, this all leads to the need for what we're calling the CRO of the future, and when I say CRO of the future, I also mean revenue operations leaders and sales leaders too, but the CRO of the future really needs to be able to see revenue risk early, and not have any surprises. In order to be able to do that, you have to be able to look at data that's believable and can be analyzed in real time, because you don't always have days or weeks to do some storytelling around some analytics that you're doing in a spreadsheet outside of your source data. Then, finally, revenue orchestration also becomes increasingly important, particularly if you're working for a fast-growing or a public company that has particular revenue targets. You have to work very closely with your partners in finance to make sure that you can meet the expectations that you've set for the street. The good news is that these toolsets exist today, and so we really just want to be at the forefront of helping superpower the CRO of the future. Kyle, do you have anything to add to that vision of the CRO of the future?
Kyle Welling 0:10:45.1:
No, other than… I'm not sure which mic I'm supposed to use, because somebody told me… I think the hand-held. So I think everybody understands that the role that the CRO of the future will play in not only the sales team, but also the entire revenue-generation engine, is changing, and it's becoming increasingly important that they can see over the horizon and they can anticipate change, but they need to anticipate that change intelligently. We can't guess. So, I think that's where we're going next.
Scott Hirsch 0:11:15.5:
Cool.
Kyle Welling 0:11:19.0:
So now the question becomes, how do we give sales and revenue operations leaders the insights they need to make better, faster decisions? Right now, more than ever, it's important that revenue operations and sales leaders can see the whole board. What I mean by that is, planning is no longer an annual exercise where it's done once a year and you can set it and forget it. We see a lot of customers now that are planning multiple times a year in response to both internal and external factors. This means that the planning cycle needs to become faster, more intelligent, and needs to be more connected to other parts of the organizations like the HR, finance, supply chain, customer success. We need to make sure that everybody is on the same page. Sellers are under pressure too. They need to be more productive. They have increasing quotas, they have smarter buyers - buyers now are more intelligent than they've ever been - so they need to be smarter about how and where they allocate that time. A critical role of the sales operations team is not only to give sellers a list of accounts that have propensity to buy, but also that are ready to buy.
Kyle Welling 0:12:31.8:
So, I think Anaplan is uniquely positioned to solve this problem, and give revenue and sales leaders accurate information they need to make informed decisions quickly, but also to make those decisions in the context of both the upstream and downstream processes. Again, aligning sales with HR, finance, supply chain and other stakeholders. I'll get into most of these a little bit later when we talk about our roadmap, but as you would have heard in our keynote and from Scott earlier, I'm excited to announce the launch of our new Sales Forecasting application. This fills one of two critical gaps that I see in our current portfolio, which makes me really excited. There's a couple of interesting facts that I want to go through, in addition to the quote you see on the board here, and I didn't memorize them. One, according to a Gartner study, is that only seven per cent of companies have a sales forecast actually over 90 per cent. That's not a forecast rollout problem, that's a business problem, and it's a costly one.
Kyle Welling 0:13:36.5:
Another study by Gartner says that 69 per cent of sales operations leaders say that sales forecasting is harder today than it was three years ago. Things like role simplification has made teams incredibly more productive, but more go-to-market teams across customer journey with more complex hierarchies in executing more complex revenue models, makes it more difficult to measure go-to-market performance and forecast sales. So, what does this mean? The challenges we see aren't unique to sales forecasting, but they have a unique and costly impact on the business. Time spent gathering and stitching together to spare data, we use a little time to do what we expect from our sales operations team. Pulling together data from multiple sources, some standard, some not standard, being buried in the tactical work, leads to poor adoption of tools and processes, which leads to poor and ineffective insights, and doesn't leave room for any forward-looking work that we expect.
Kyle Welling 0:14:41.0:
So, from this, we were able to determine the direction that we felt the space was moving, and what we heard was that traditional forecasting tools were not working, simply because they served as a visibility tool. It was just a visibility layer into the pipeline. So, the number-one ask that we heard was that we need to consolidate the revenue tech stack. Each additional tool costs administrative overhead and it costs money to the business, and so the spreadsheets plus point solutions are no longer a winning scenario. Number two, leaders want forecasts to be more than an inspection dashboard. They need to orchestrate the entire revenue engine. Traditional tools are exceptionally good at pipeline visibility, deal inspection and activity tracking, but the data is isolated. Customers now want more value out of their data, like forecasting tied to compensation, forecasting tied to quota, to capacity, and forecasting tied to demand. Forecasting that reflects the entire revenue engine. We also see a growing demand for real-time scenario planning, the heartbeat of the Anaplan value proposition. Teams need to understand the impact of changes before making them. Leaders are asking for systems to not only show the current state, but also to let them model possible future states without relying on spreadsheets.
Kyle Welling 0:16:06.6:
The fourth and final thing here is, leaders don't just want accuracy, they want explainability and trust. Leaders don't just want to know the number; they almost always ask why. They want the forecast they can trust, defend, and ultimately act on. Explainability transforms forecasting from another to-do, to a source of a confidence and understanding, and the explainability is also another pertinent AI-driven insight. So, as you can see here, sales forecasting is made up of many, many discrete activities. From a frontline seller standpoint, it's primarily based in pipeline generation, account research, prospecting, but also things like activity capture and call recording, and then for sales leaders in operations, they do a lot of coaching, they do a lot of deal management, forecasting, and they also need to align the financials with the demand and workforce. The Anaplan Sales Forecasting application we're proposing is a single source of truth for revenue orchestration, bringing together go-to-market teams, finance and supply chain, and uses AI to highlight the risk in the pipeline. The upside behind the commit, it gives leaders visibility to drive revenue growth and decision excellence at scale. With an emphasis on sales leadership and executives, the sales forecasting application empowers those leaders with forward-looking analytics, to track performance and make smarter, faster, go-to-market decisions.
Kyle Welling 0:17:41.3:
You can see here, as we think of sales forecasting traditionally, it sits at the end of what we're now calling the revenue orchestration. Upstream of that you've got a lot of pipeline management and coaching, performance analytics and account planning, but that sits in the middle of the entire forecasting process. Upstream of that, we've got plan approvals, plan design, and the actual compensation that drives the forecast plan. Downstream of that, we've got the demand forecast, the accrual forecast, the financial forecast, bringing together the single pane of glass. Having all stakeholders viewing the same data through their own lens is no longer a competitive advantage, it's the standard. So now I want to transition here and invite one of our customers, Raphael Van de Graaff up from AWS.
Raphael Van de Graaff 0:18:34.9:
Fantastic. So, thanks very much for having me today. I'm Raphael Van de Graaff. I'm a sales leader at AWS. I have a team of specialists responsible for AI and data, so working with enterprise customers and technology companies. So thanks for having me today.
Kyle Welling 0:18:52.2:
Yes, so I know you just gave a brief introduction, but maybe you could talk a little bit about your background with Anaplan, and expand on your current role at AWS.
Raphael Van de Graaff 0:18:59.3:
Yes, sure. So Anaplan is a forecasting tool that we use, a forecasting plan management tool that we use, and in my almost-seven years at AWS, we've seen a handful of these tools come and go, and even before that, and Anaplan's been the best so far, just in terms of its ability to marry business context and data with insights from our sellers, and the ability to add those insights alongside the forecast. It's been a really powerful tool in terms of how we think about and plan the business.
Kyle Welling 0:19:40.2:
So as we were preparing for this, you talked a lot about AWS' leadership, their insatiable appetite for insights, and actually you gave I think a really pertinent example of something you're working on currently, and I would like, I think the audience would really like, to hear about.
Raphael Van de Graaff 0:19:58.8:
Sure. So as you can imagine, right now our leadership at the VP and C-suite level are incredibly focused on understanding what's going on with our customers. They're trying to make long-term decisions around how we serve our customers in an environment that's changing very, very rapidly, particularly in the AI space. So the request for insights that used to happen maybe twice a year, has gone from twice a year to quarterly to monthly, and now I'm even writing documents a couple of times a month, to help our leadership understand what's going on in the market, taking revenue and pipeline signals and translating those into insights. So, yesterday, when we were prepping for this, I had a 700-page document of unstructured insights from the field that I was matching with a many-thousand-line pipeline report, and trying to generate a page of crisp insights. So that's the sort of thing that I think, as revenue leaders, we're all going to be responsible for more and more, and this is a place where technology can help, but also our partners like Anaplan can also help simplify that process and deliver those insights more quickly and more automatically without having to wrestle down all of this data.
Kyle Welling 0:21:24.5:
Obviously, the request for insights is becoming more frequent. How do you see AI playing a role in making it, not only more attainable, but also more accurate?
Raphael Van de Graaff 0:21:38.0:
Yes, so I think there's a couple of things. First and foremost, it's the ability to capture those unstructured insights. If any of you have been in sales for any period of time, those unstructured insights happen in all sorts of places. They happen in your CRM tool, they happen in emails, they happen in notes from accounts teams that they take, they end up in documents and SharePoint, or Quip, or wherever. One of the first things that we did was establish a tool to capture those unstructured insights into a central location. So if you're not doing that, I highly recommend doing that, but then you want to enrich that data using all of those contextual things that I just discussed, like Slack, email, meeting notes, all those things. So you want to be able to enrich those things as well, and then again, you need to match it up with the structured data of what's going on in your business, the revenue, the pipeline, the forecast, and that's where Anaplan comes in and gives you that source of truth on what's going on with your field and your business, to match up against that unstructured data with the observations from the field. It's really, really important I think that we all be able to move quickly, and so the final piece of this, okay, well, I get a signal that I need to adjust my approach, and I think we all know that that has top-of-funnel all the way to deployment implications if we're shifting how we're going to market and how we're changing our approach.
Raphael Van de Graaff 0:23:14.1:
You want to get those insights quickly, and then you want to be able to model out - and again, this is where I think Anaplan can come in - what are the implications of that. Do I redeploy folks on the sales team to go after an opportunity or a signal that we're seeing over here? Do I walk away from certain businesses because we're seeing another signal over here? Or do I ramp up my go-to-market team even more as a result of some of the signals that I'm seeing? Increasingly, we have to make those decisions really, really quickly.
Kyle Welling 0:23:45.3:
Yes, so we talked a lot about how AI is weaving into that decision-making process now, and so I want to hear from you - this is a two-part question - what do you believe are the two biggest challenges that AI will help solve for the CRO and sales leadership of the future? The second part of that is, what do you think the biggest challenge is that AI either can't or won't be able to solve in the future?
Raphael Van de Graaff 0:24:12.4:
I'm going to sound like a broken record, but I think it's a combination of taking insight and then moving into action. So I think AI is increasingly allowing us to take those insights that I've just talked about and find the real signal in the noise, but then quickly convert that into action. This gets into a little bit of the productivity benefits of AI. How can I establish a consistent go-to-market approach, a sales play, and follow that through all the way from the top of the funnel to revenue realisation? I think deploying that has become easier leveraging some of the AI tools available to all of you. The one area where AI won't help is deploying people to talk to customers. I believe the stat around customers wanting to make buying decisions without talking to a seller or a rep - which is why consultative selling is really, really important right now, but I think organisations will increasingly, or at least continue to, invest in field sales people to be out there talking to customers, because at the end of the day, that last mile of gathering those insights has to come from people on the ground. We've had the ability to do social listening, we've had the ability to find smarter ways to prospect and see what customers are doing. At the end of the day, the thing that AI won't replace any time soon is getting out there and talking to customers, and figuring out what they're struggling with, where they need guidance, and where they need our help.
Kyle Welling 0:25:58.0:
Yes, I think that's really good, because the way that I see AI helping is it's going to do a lot of the heavy lifting upfront to get sellers in front of the right people, in front of them at the right time with the right message, but you'll always need somebody to help close the deal. You'll always need a seller to take it that last mile, because in the end, people buy from people. I know it's an old, tired saying, but I think it's true, I believe AI will never replace, an AI will never completely fill, the sales cycle.
Raphael Van der Graaff 0:26:30.6:
Yes, I would just add one thing, and I think it's super-fascinating - Scott touched on this - but one of the things you ought to be thinking about is where your customers are researching. From some of the technology companies that I work with, they're absolutely thinking about how to make sure that they have a presence in Claude, OpenAI, ChatGPT, as a path by which customers are researching solutions and figuring out what they're going to buy. You all have to be there and plugged into that in order to catch those buying signals, so that you can go and engage with them.
Kyle Welling 0:27:07.6:
Any final thoughts or last words you want to leave us with?
Raphael Van der Graaff 0:27:10.7:
Sure. One of the things I think we'll see a lot this year, for those of you who may already be starting to see this, is people playing around with agents and agentic automation, both personally, and I think this will quickly bleed into your corporate lives. We're seeing this at AWS and Amazon. The speed with which people can develop solutions to automate things for themselves is incredible. It's just unbelievable how quickly people can build things. Especially for those leaders in the room, I would caution you to be careful and think about picking a partner. Anaplan is a solution that will help ensure that whatever you do, particularly with AI, is going to be surrounded by the necessary guardrails and security, and all of those things, but then also they're going to provide the business context. One of the hardest things about getting value out of AI is you're the interpretation layer. You're the person who's got to ask the right questions. A solution like Anaplan will take you 90, 95 per cent of the way there, because they're going to surface the really important data, pipeline, revenue, segmentation. Those sorts of things are really, really important, and then can better frame the discussion and the solution.
Raphael Van der Graaff 0:28:42.8:
So you really want to be a little bit careful about running out and vibe coding a sales automation or forecasting solution. I encourage you all to focus on productivity, maybe automating some unique sales plays. I'm not saying don't get your hands dirty with this technology, but make sure that you're also thinking about it and relying on key partners like Anaplan, to ensure that the really, really important functions like forecasting are solid, have the right business context.
Kyle Welling 0:29:18.5:
That's great. Thank you so much. I really appreciate it.
Raphael Van de Graaff 0:29:20.6:
Yes, thanks for having me.
Kyle Welling 0:29:22.2:
Thank you.
[Applause]
Kyle Welling 0:29:29.0:
So I think that's a really good transition into the next spot here, around Anaplan's AI story, so I'm going to invite Scott back on stage to talk about Anaplan and AI.
Scott Hirsch 0:29:38.7:
I can't cover this any better than it was this morning, but Anaplan's AI strategy is really - one of things that I was really impressed by, having come from a company, Syrup, which was built on an AI go-to-market from the very beginning. Like we were AI native, and our whole value proposition was based on the quality of our neural network and machine-learning models to do inventory forecasting at the size, colour, style location level. So we were native, but one of the things that I was most impressed with when I got to Anaplan was Anaplan has actually been doing some pretty sophisticated AI, ML, data calculation work for more than a decade. So it's all built on the foundation of the advanced calculation layer, which Adam Their went into a lot of detail about this morning, but things we've been doing for a very long time, and including the Anaplan Forecaster which recently got completely rebuilt, and Polaris, the data calculation engine, everything being API-powered from the very beginning, and then, with the acquisition of Syrup, basically, it's a forecasting model that's been custom-built for a particular industry vertical. As Adam mentioned this morning, there's going to be more and more of those over time, so we'll be doing more very specific foundational ML/AI-based forecasting and optimisation models by industries that all of the applications can leverage and take advantage of.
Scott Hirsch 0:31:08.3:
Where the magic really happens, in my mind, in addition to having all that really solid foundation, is in the application layer, and how what we've tried to do is take the most common business context and business use cases and align them with the data ontology across all of the applications, so that the applications not only know their specific use case, but they're also wired in such a way that they can actually pull data from other applications in other use cases. It really brings to life and makes possible the entire connected planning story, which is this notion of you should never be doing your planning in isolated siloes. Another just funny little story from my Syrup experience was, we would talk to lots of CFOs and CTOs about how they do forecasting as an enterprise, and this is at some very large retailer's - we all know the name - and they would basically say, 'Well, we do it this one way for anything beyond a year, so anything for a range, and we do it this other way for assortment planning and assortment optimisations.' So that's when they're planning out what products they want to sell in a particular season. 'Then we do it another way for allocation and replenishment.' In the course of talking it through with them, we were like, 'We should really just use one model, and just optimise it for different time series and time ranges.' So I really feel like that's where Anaplan is headed, is the ability to do that one source of truth in terms of forecasting optimisation and planning across all of your business use cases and contexts.
Scott Hirsch 0:32:45.5:
Then, of course, at the top level it's the agentic layer, and I would say that that's the icing on top of the cake, because if the cake itself isn't good, who wants to just eat the icing, but the entire agentic strategy, they'll be able to easily access data through tools like CoModeler and Agent Studio, and quickly understand data and make changes using it, and easy agentic interfaces, of course, are going to be absolutely required in the future. I think I already hit on a bunch of this. I can probably just move past it, but it's just, again, the notion that the agentic layer has several parts of it that are products that we're currently launching and in development right now, and with more coming. In terms of our roadmap, I think Kyle's going to go into more detail on this, just in terms of application-by-application, so I'll hand it back over to Kyle right now.
Kyle Welling 0:33:42.9:
Thanks, Scott. So you can see here on the board how we think about the entire integrated revenue performance management suite. It's no longer just territory and quota and sales compensation forecasting - those are major rocks that we like to dive in on - but it's made up of many, many different parts and pieces, and what you don't see on this slide is all of the connections into other parts of the business. We've got a heavy reliance on finance for target numbers, we've got a heavy reliance on HR for workforce numbers and hiring plans, we've got a heavy reliance on the supply chain team for demand forecasting and supply inventory planning. So there are many, many use cases around this that help drive, like we've mentioned, the entire revenue orchestration engine. So I'll dive a little bit into what we have coming this year, but before I dive into this specific application, I want to hit on data integration management. Obviously, this has been a huge investment for Anaplan, not only as a company, but also for the RPM team.
Kyle Welling 0:34:49.9:
By the next release, which is currently scheduled to come out in May, all RPM applications will be on ADO, and we'll have native ADO and Polaris as part of their deployment, but in addition to that, we're investing a lot into what you heard about in the keynote this morning, which is called the UDM, so the unified data model, which will sit within ADO, will help connect those prescriptive data models from not only RPM, but also from finance, workforce and supply chain. Together, we know what those other models look like, we know what data is in them, so we should be able to leverage them in context through each application. You may call it something different - it may be revenue, or volume, or something like that - however your business thinks about it, it's the same number, and we should be able to surface that number through all of the different applications and keep the context throughout. So Segmentation and Scoring, as I mentioned earlier, we've got V2 of Segmentation and Scoring coming out in May, which will include ADO, Polaris, but the thing that I'd like to say about Segmentation and Scoring, is this is where strategy meets execution. This is the first part of the process, where we take our sales and corporate strategy and we actually start modelling it out.
Kyle Welling 0:36:06.3:
What accounts do we want to focus on, whether it's going to be a vertical strategy, a geographic strategy, an account tiering strategy, or maybe it's a combination of all of them, however you decide to go to market, this is where we actually start modelling it out. Which accounts do we want to focus on? What's the addressable market? What's the size of the prize? Then we get into how much they're worth. After that we need to make sure that we can cover those accounts. If we're going after 9000 accounts, do we have the reps, not only the direct reps, to cover that, but there's a sales overlay impact, there's a customer success impact, there's a workforce management impact, there's an operations impact. How do we make sure that we've got the right resources, not only people, but supporting tools and everything else to make sure that we're executing on that appropriately? Similar to Segmentation and Scoring, we've got version two of this coming out later this year in May, which again, will be native to ADO and Polaris, so really excited about that.
Kyle Welling 0:37:04.8:
So Territory and Quota was the first application that we deployed, and I believe it was the first Anaplan-built application that was deployed a couple of years ago. We are coming out with version three of the Territory and Quota app later this year, in May, as well, so all three applications are getting a refresh, and this one's a big one. So this is the one where we're converting this to Polaris. It will get a refresh across the board, it will have a sales agent, and all of the things that we've talked about from an AI standpoint embedded into this, as well as the ADO. Sorry, I'm going a little bit fast here, because I know we're running out of time, and I want to leave plenty of time for questions at the end. Sales Forecasting, this is the one that we just launched today, and we've heard plenty about that in this session. We'll hear more about it in later sessions. As Scott mentioned, you can actually see a live demo of that in one of the rooms next door, so I encourage you to go and check that out and hear about that. This is what the roadmap looks like for us, from an actual application standpoint, and one of the things that I want to highlight here is what we have coming soon, that we didn't talk about, and it has to do with ICM.
Kyle Welling 0:38:14.2:
So for those of you who have known me for a while now, my background is in ICM. I grew up in the ICM world, and so I'm really, really excited about what we have coming later this year. We're actually developing an incentive compensation management application that will be live in the latter half of this year, and we're going to start with the automated credit allocation. So for those of you, again, that are familiar with incentive compensation, you know this is the glaring challenge with incentive compensation: do we get the right attribution for transactions to reps and overlays? This is 80 per cent of the problem right here, and we hope to solve that this year, followed by the rest of the ICM modules that will be coming later. So with that, I want to leave time for questions.
Audience 0:39:03.3
I work on deal desk, so sales comes with exceptions for contracts, pricing, billing terms, even unwinding products, as you know. I just want to know, how do you handle all that within your system, because that's sort of a pattern? It can maybe say, 'The client is not comfortable with what we're offering,' and you can kind of see that predictably in the unstructured data. Do you have a way to handle that in your system?
Kyle Welling 0:39:38.6:
Yes, great question. So the candid and frank answer is no, but I do see a world in the not-too-distant future where AI does pull a lot of that unstructured data through existing contracts, customer profiles, interactions, notes. One of the unstructured data that Raphael mentioned around calls and emails and recordings that can pull all of that together, and put together a recommended list of SKUs or products within the contract, with the specified terms that we don't have to go and customize one-by-one every single time. So I see that as something in the future, but it's not something that we're working on today. Great question, thank you.
Scot Hirsch 0:40:18.8:
Can I add something to that?
Kyle Welling 0:40:19.7:
Of course.
Scott Hirsch 0:40:20.1:
Because I did talk to our product managers on this very subject, actually. Deal desk is a job, so congratulations on accomplishing it, but what they were saying was that they want to use the machine-learning aspects of Forecaster to be able to predict what types of deals are more likely to have exceptions than others, and give like a confidence interval for what the revenue might be. Over time that would get smarter and smarter, and the confidence interval would get smaller and smaller, but that's the way they're thinking about the problem.
Kyle Welling 0:40:56.3:
All right. Well, I think that's all the time we have today, and before we leave, I do want to call out - he might have just stepped out - but the product managers for a lot of the products that we just mentioned up here, particularly on the go-to-market planning side... He's actually in the back there. So, Thibaud, if you want to stand up and raise your hand? Thibaud is the product manager for all of our sales planning applications. A great resource for any questions you have. Insights into not only what we have today, but what's coming in the future, so I encourage you to find him and stop him and have a conversation. Thanks everybody. I appreciate it.
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