Name: How CROs are driving 95% forecasting accuracy with AI
Uploaded: 2025-07-09T16:31:59.408Z
Duration: 43 min 28 s
Description: How CROs are driving 95% forecasting accuracy with AI

Transcript for "How CROs are driving 95% forecasting accuracy with AI": Hello, everyone. Welcome to this session. Good morning, good afternoon, or good evening wherever you're tuning in from. We're joined by John who is his senior director of revenue operations over at Clari. And John's gonna be talking all about how CROs are driving 95% forecast accuracy with AI. So, John, welcome. Great to have you here. And, I'll pass over to you. Awesome. Thank you. Good morning, everyone, and good afternoon for those joining around the world. Really excited to be here today. So like Jack mentioned, my name is John. I lead our revenue operations team here at Clari, and this is the name of the game. The ability to drive your forecast, the ability to drive the level of inspection, into your pipeline is critical every business today. Competitive pressures are all times high across the board. I'm lucky I get to talk to a lot of our clients, on a weekly on a weekly basis, and the consistent themes that I am hearing are really twofold. One, the ability to drive pipeline and the bill ability to convert that pipeline through the funnel. Getting qualified pipeline is getting harder and harder and harder, for companies across the world. The other component of it as well is around retention and forecast and how do we drive, retention efforts because budgets are being constricted at rates, like, aggressive, aggressive rates as companies shift far more towards profitability, and we need to pay attention to that and manage that through the entire funnel. So today's conversation is really tangent really core to that is how do we drive predictability in the business, and how do we drive predictability with our stakeholders, and how does AI actually layer into this? So with that, the AI promise is massive. You cannot log on to LinkedIn. You cannot go to a conference. You cannot go anywhere without everything being at about AI. I was with Gartner chief sales officer conference, last month, and everything. Every single session was about AI to the point where some attendees were like, is there anything else? Because everyone is talking about this really consistently. But with that, I think one of the biggest promises that came out of that is this is getting tactical now. AI of two years ago was much more ethereal, much more theoretical. We are really moving into the tactical motions of how do you deploy this. And at that conference, the call from Gartner to CROs, CCOs, everyone are part of the revenue spectrum in that room was this is all of our responsibility. This is not IT's role. This is not the CIO's role anymore. Deploying AI and doing that, intentfully is the responsibility of revenue teams, revenue operations, sales leaders, you name it. We cannot just put this on to other individuals. We have to be core to the conversation. But with that, the AI in revenue is massive. It make it can make everything better from forecasting, from pipeline manager, from risk detection across deals. But there's one huge caveat, which is the data. And right now, this was based on a Clari Labs study that we did this year, and 67% of executive revenue leaders do not trust their data. That is massive. AI is not magic. How are we expecting AI to move the needle if the raw data that we're feeding it, we don't even trust? Our leaders don't even trust. It's not gonna be this, you know, magical machine that you pump junk into it and out spits diamonds. Nothing like that exists, and that is hard. Doing the work to get ready for that is difficult. But that is the challenge in front of us all is how do we pay more attention to our data infrastructure as revenue operations teams, as revenue teams? Are we feeding the right data into these algorithms to get good reliable insights that will actually guide us to better decisions? That is fundamental. And too often in the revenue operation space, we hyperfocus on the CRM. The CRM has been our repository for revenue data. How can we feed more and more and more data into that? But I think that is really fundamentally changing, really fundamentally changing now that revenue data is not just in the CRM. It's not even just in the sales tech stack or the CS tech stack or the marketing tech stack. It is really end to end from the entire company. This is just a smorgasbord from an illustrative perspective of different platforms that may play into that, but there are so, so many more. And the ability to harness that revenue data from your raw FP and A systems, from your marketing side, from your sales side, from your CS side, from your raw product, and the usage of your products, with with your clients, bringing that entire ecosystem together is critical, to make sure that the data that we are feeding these algorithms, in particular for forecasting, that cannot just be based on CRM data. You are looking at a myopic view of the world. If it is that, how do you broaden that out? The answer for us, and I think this is a part where revenue operations teams have to change our skill sets from traditional operators into really influencing the data strategy. This happens at the database layer. This data is coming together on those database layers, and your ability to harness that and bring that and knit that together into, you know, tables that you can feed algorithms, that is most important. That is, I would say, step critical one is getting a hold of your data and identifying all the important aspects of that data across the board to bring it together at a data layer. That is what we are pretty heavily invested in from the Clari side, and we have are focused on how do we actually gather data directly from the database layer by, you know, not having to go to the CRM for every mode. How do we directly ingest that into, our instances to provide that service, and to provide that visibility so we can identify, alright, these are the important data assets across this ecosystem. Everything doesn't have to reside within, within the CRM itself. So with that, with the risk of doing this, the risk of inaction here is putting more and more revenue at risk. This again comes from our one of our Clari benchmark studies that we've done that without the care and feeding, we estimate there's about 26% on average of companies' date of revenue and pipeline that is at risk without this care and feeding. If we are not populating and servicing those risk across deals, every every sales team has a ton on their plate right now. They're being bombarded from every direction on different things to focus on, different platforms to engage with. That adds up and really leads to the opportunity and the miss here. If we don't put the deals and focus across the entire funnel, not just the end step, then we are going to miss out on this revenue, and we estimate that to be about 26% of the potential for clients across the board. So our goal, and I think the goal for every single operations team out there, is how do we deliver the right signal to the right team at the right moment? That's really complex. Simple statement, highly complex, but it actually comes down to enacting this. And with that, the best analogy that I can come up with today is we used to describe revenue operations five years ago as the the air, the air control tower. You'd be seeing a tower trying to direct the planes and getting these planes to land. I think that's fundamentally changed in the different era, and the tech stacks have become distributed to the sense of I think that analogy starts shifting where we are today, and AI is only adding incrementally onto this as it's more or less that we are standing in the control tower. And instead of being able to talk to every single plane, we we have to pick up a two way radio to talk to one plane, then pick up a two way radio to talk to another plane. And each of those planes may be telling you different things. Each of the platforms that our teams are existing in may be saying slightly different things. And how do, us as operators make sure that, alright, this is actually the good data. These are what we wanna base this on. That's really difficult. For me tactically, I think that is why the database layer comes into such a play. How do we actually give our field teams a front end that unifies all those insights, that actually gives them across all these different platforms that we buy in these tech stacks? This is what you need to know. Every time we start swivel sharing, and we every time we ask our teams, more importantly, to start swivel sharing and saying, log in to this platform here to do this, then log in to this platform over here to do this, and then log in to this platform over here to look at this, The adoption rates across each of those field teams drop pretty dramatically. Sellers just don't wanna do that. They don't wanna exist in multiple platforms. They wanna exist in one place. And they wanna see that insight into one place that we can guide the decisions and make sure that our responsibility is to guide them through the day. What are the things that they need to be focused on based on that day to achieve their numbers? If we can centralize that, there are a level of adoption. The opportunity increases pretty dramatically, and that's what we need to be focused on, as operators. And that is our goal, here at Clari. So what we boil that down to and I think the goal of how do we unlock AI is what we are calling revenue context. What is the context in all the data across those ecosystem of, the ecosystem of tools and the data points that come from that that actually matter? Who did what, when that led to what outcome? If you do action x on a deal in the early stages, it may not be nearly as impactful as if you do it at a later stage. How do we actually build the data foundation so we can start identifying that? And that is going to constantly change and care and feed based on the environments that are gonna change over the next coming years. But how do we actually drive to those outcomes and make sure that our field teams have the insights, the right moment so they can influence the deal and progress the deals to move forward. That is our primary goal, and that's what we are wrapping up, into this context called into this, ability called revenue context. Let me say this as well. This is not, this is part of their struggle. All of our leaders are coming and asking, I need to do AI. I need to do AI. I need to do AI. That's a common theme I hear from every single revenue operations team I speak with. The goal, and this is the hard part, which I feel the conversation is changing a little bit in the ether, in the zeitgeist, is we've gotta do the data work. The data has to be structured that we have to be able to bring that together to answer this question, who did what, when, that led to what outcome. Without doing the hard work on the data and centralizing with the data from across those platforms into a single place, we are never going to push beyond. AI will never be successful because it will not be fed with a comprehensive set of data for deals to actually drive to better predictions and better forecasting. That is our goal. It is not the sexy work. No one wants to stand up at their board meeting and say, I think we should focus on data hygiene. I've been there. I've done it. There's never a positive response to that, but I think now that is starting to shift because people have invested, we estimate $13,800,000,000 in AI solutions in 2024. That's an insane amount of money. But the satisfaction with those solutions is sub 60% because we're expecting that AI to come in and change something that it cannot. Until we are get our data in a good place to feed those AI tools, then the power becomes untapped. But until that happens, we are never gonna see the value realization. So when we actually can build this data to answer these questions, then it becomes a motion of how do we actually guide the field team. And so there is an AI component to this, and a data component to this, but then there also is a very tactical, how do you drive consistency across the teams? Because if only one rep is using this, it doesn't really do anything. It doesn't matter. What much more matters is if we can actually get our entire organization to be operating on a consistent rhythm. And that is what we wrap up into a motion called cadences. This is a highly illustrative chart. A complete a very illustrative chart. But if every forecast meeting is the same and I have been at companies where every single week, that forecast meeting between managers and their teams, between managers and their VPs and VPs with the CRO looks the exact same every single week. You are only going to get the exact same results. The information coming out of the set of AI tools, in the go to market space is going to be vast. Everyone cannot just take that all at once and every single week review you know, have full insight into the spectrum. We are really focused, here at Clari at this concept about this concept of revenue cadences. How do you make each week different? So over the course thirteen weeks of the quarter, you can cover end to end of the entire funnel and make sure that reps are actually paying attention to every single one of those spaces, and we are building the data hygiene and the data quality, as a part of that to drive better insights from the tooling set. Every forecast meeting a rep is gonna wanna talk about their late stage deals. These are the deals that are cross line. This is what I'm committing. I'm focused on. How do I just drive those deals? That actually builds debt throughout those thirteen weeks. Because if they're not paying attention to the top of funnel, what happens when those deals close? What's the support required, and how do we bring a full company focus to getting those deals across the line? That has to be throughout the entire funnel, not just your late stage deals that may hit this quarter. That has to fluctuate between a current quarter focused, the next quarter and an out quarter focused depending on the life cycles of your business, how far that goes out so you can continually prepare. So having a structure to this is critical. It is hard work. We are constantly tuning and feeding our internal cadences at Clari. I was actually just doing doing it this week with our head of enterprise, is how do we start tuning this a little bit more based on what we're seeing and what we're feeling in the market? How do we drive that consistency through every single call? We run a weekly forecast here at Clari. On Wednesdays and Thursdays, our managers are meeting with their teams to actually understand understand what's going on. On Thursdays and Fridays, they wrap that up to their VPs. And then on Mondays, we kick off the week between the VPs and our CRO to drive focus for the week. That consistency is critical. It is expected. Everyone participates. This is a full company motion. It is not just the sales teams and the the rev ops teams. We bring in cross functional partners depending on the week so we can try focus and rigor across the company. Revenue is a full company motion and how do you engage the company consistently to so we can all move in the same direction. That is critical to that success and drives better accuracy from the forecast side because we're covering off more of the funnel. If our forecast are just focused on the last mile deals, we're gonna be missing out. Things inevitably change throughout the quarter. Captured and closed within quarter is a real is a real thing. How do we make sure we're building the predictability and the repeatability around that? We find it through this cadence motion because it gives us a coverage across the full funnel, a very intent can drive from a consistent fashion down through our entire, our entire team. Also, just to let you know, by the way, there will be a whole q and a section towards the end here. So more than happy to dive into any of this tactical. I have an open book, and excited for that as well. But that that will be coming up. So with that, how do we fundamental how do we change this enterprise AI deliver value? And this really you know, we focus on how do we drive the insights that matter. These tools should be giving us insight. If we are deploying AI tools, we need an action oriented AI. It cannot just be a so what. It can't just be a this is just these are some insights about your pipeline. Those insights have to be connected into the actions that we can drive with our teams. We do that through the cadence motion to make sure that those insights are surfaced through the through our cadences. So we're covering off on everything that we are developing internally as well as buying from a revenue go to market tech stack. That prioritization is critical. The best thing a rep can do, it might one of the best things a rep can do, in my opinion, is actually say, I shouldn't spend my time here because of x, y, and z. And if we can't succumb those values, let's not chase the deals that are not attainable just for the sake of chasing the deals. How do we make sure that every moment of their day is going to be prioritized? Because the demands on our field team are going to rise. The expectations and what AI is going to give them from a productivity boost naturally are going to, rise. The total quota assigned per reps, the management ratios, we expect those things to grow. So prioritization will become more and more critical through this, and we have to use AI to help feed that prioritization, and the automation side of the house. Like, AI should make us faster and smarter. So how do we actually do more with less, which then ties into that prioritization as well. So we boil that down with that revenue context as how do we actually orchestrate our revenue teams? From our marketing side, from our sales side, from our CS side, how do we make sure that everyone is aligned on the same page and that the insights being driven by these AI tools are at the fingertips of, of those field teams? How do we actually put that right in front of them so everyone is unified around the same data about a deal, about a structure? How do we make sure that everyone is singing off the same scorecard at the end of the day? That is where we're having these disparate tools, but how do we centralize this? We believe Clari is that place, that just in full transparency, that's why I'm a part of this company. I was a customer before I joined the company, and I joined it because I saw the impact it had on the field teams as I brought it in. And more and more so as we are ingesting different data sources, that is a critical fashion. That is a critical, critical component. Because if we can ingest more data, we can orchestrate that data for our field teams, and that orchestration layer is their critical component. If we rely on field teams to go from platform to platform and just keep swivel sharing between Windows, the adoption is never going to get there. The consistency and the rigor is never going to get there because it's always gonna take someone else to come in and say, are you paying attention to this? Are you paying attention to this? Are you paying attention to this? And you just can't ride that scale and repeatability across large sale even small sales organizations. The problem really compounds to large sales organizations. So how do we actually drive that consistency and that cadence motion? That is what we believe on the revenue orchestration front, which needs to be fueled by AI, but that AI is really put into action. So that platform provides that revenue context. The more data we can the more trusted data we can aggregate together into one place, we can give that revenue context to show, here's what you should do. Here is a recommended action because we've seen the success in other places, and we can continually build that story. So like I mentioned, this is our the paradigm shift that is coming to go to market. When you look at and these these are averages, definite definite averages. But when you look at sales team, we believe that the a reputainment side of the house could more than double. Typically, on average, you see reps carrying around a $1,000,000 quota. In a few years, that's gonna be $2,000,000. The productivity gain is going to be real, and it is going to flow into the sales capacity side, with as we expect more and more from our teams. The same thing from a manager coverage. As we are automating more of these risk detection, that leads to coaching, that co the risk detection needs to be tied together in the coaching so managers can help, certain not just surface those risks, but help their teams actually overcome those risks, which becomes a repeatable process. That is going to lead to higher coverage ratios. Typically, we be seeing see between six to eight reps per manager ratio. We think that's gonna be increased to around 10 to 12 reps. Again, that drives the efficiency gains, leading to more profitable companies. As you're able to, you know, decrease your management, management cost layer, that will be coming. And the same thing from a pipeline conversion perspective, are we anticipate that you will be able to close almost double the amount of pipe if you do this thoughtfully and intentfully, with AI coming into place and getting smarter and smarter and smarter, every day that we see, we've we're anticipate those conversion rates to increase as well. This really drives to an efficiency game to allow companies to do more, with the same amount of resources. It's not necessarily a cutting. It is just an increase in productivity. Now, 13,800,000,000.0. I already mentioned that was the amount of money that was spent on AI tools by revenue teams last year, estimated from our from our Clari Labs study, which link will be here in a second. 13,000,000,000 is a massive amount of investment, especially when leaders and remember that 67% of leaders don't trust their enterprise stack. They're still investing $13,800,000,000 in these AI tools. They don't trust their data layer. That is a paradigm that is not going to be sustainable. The satisfaction with this investment is not high. How we have to be focused as operations teams and as revenue teams to build that better data layer so the investment in these AI tools really will start, accelerating your business. How do you actually drive those higher conversion rates? That is the promise that is to come because you will be able to better detect risk and better be able to know the motions that really work and change the ego across your opportunities. That is critical to put this $138,000,000,000 to far more use and see those satisfaction numbers increase. That is our goal. That is what we work with clients on on a daily basis to make sure they're feeding the right data into these algorithms to predict better. We are one of our core competencies at Clari is on the forecasting and the prediction side. We believe it is critical to running your business because that consistent forecast is what enables us to drive more investment, to drive more focus on the field team, getting more better data from across the ecosystem, not just from the CRM, but again, from your FP and A systems, from your product systems, from your marketing systems. How do you make sure more and more of that is feeding in so we can actually drive the right alignment across the teams is absolutely critical. Next year, when we do this Clari our Clari Labs study, we our hope is that we see the 67% of leaders who do not trust their data drops dramatically. That is our mission because data is the fuel to driving progress in the future and to enabling the promise of AI to really come to fruition. It all comes down to the quality of data that you feed the tools. And, again, it is not the sexy work. It is hard. It is grueling. But if we can do that and be engaged in that conversation, then we really are gonna be able to drive our businesses forward, quite dramatically. So, thank you. We're gonna move into the q and a section here in a second, and I'm really excited for the question. I've seen some of them, popping up on the side here. This QR code takes you to that Clari Lab study. So if you, click get a picture of that, you can get that. And we have more and more stats in there on how we view the revenue ecosystem right now. That's, pretty exciting, and some of them are are more surprising. But with that, Jack, I'll turn it back over to you, and, let's get into some questions. Yeah. Thank you, John. That was a brilliant presentation. And, yeah, as I said, we have had quite a few questions come in, so we'd love to dive straight in there. So first question from Scott here is, revenue leaders are unhappy with data, but what is their opinion on who owns that data? In other words, who is driving the data governance strategy, and how are they driving accountability at all levels? Yep. So I think traditionally, traditionally, the IT, the role of IT or centralized analytics teams, that is that is who has traditionally been responsible for that data layer. I think that is changing. At that Gartner CSO conference that I mentioned, this was a moment I just wanted to stand up and clap. At the beginning session, one of the lead analysts from Gartner was on stage and literally said, every CRO, every CSO, every CCO that is in the room, if you think this is IT's job, you are mistaken. It is your job now. Every team has to be responsible for the quality of that data and engage in that conversation. And you saw in the room, the blood drain out of people's faces of, wow. I that is not what I'm used to hearing. And I was talking with the CRO after a session, that I hosted at that conference who said, I know I need to get involved in this. I am so scared because I don't know how to. This I'm a seller. I don't know anything about data, and how to control that, and I'm hearing consistently that this is now part of my job, and I'm scared. And I think that, I think for everyone in a revenue operations role on the call, this is our job now. This may not have been our job historically, but it is our job now. And for every CRO that is in that that's, c that every CSO, every CCO that's like, I don't know how to do this. Our job is to guide them. What I said to that CRO was, I promise if you say I am interested in this at the executive level, you are going to see hands raised across your organization and say, I am here to help. This is I someone's actually listening. So I think that paradigm is shifting. The responsibility now relies on the business teams. If we are not actively engaging in that conversation, we are missing the mark. Because quite frankly, our expertise and the context that we can provide on that data is critical to the ability to use that data and to get make make sure that people are focusing on the right areas. So, historically, IT analytics teams. Now I would say business teams, we have to be engaged in the conversation and guiding the conversation and cannot just let data teams run the show. If we're not there, it is our it is everyone in this, everyone in this, webinar's job, it is our job. It's everyone across the company's job now, and that is scary and daunting. But it is critical to making sure that the right data points are trusted and being used. Great answer. The next question here is, what automation and AI tools have you seen your team as well as clients use most often or adopt most quickly? Sorry. Jack, can you repeat that? You just broke up there for a little bit. One second. Yeah. I have to do. The question was, what automation and AI tools have you seen your team as well as clients use most often or adopt most quickly? I mean, I am completely biased here just because Clari is an AI tool at the end of the day. Our, forecasting processes, our pulse tools, and our components are AI based. Then we're releasing more and more to the stack. So, you know, we drink our own champagne at at the company. So everyone we use this to run our business. We use the cadence moments, the cadence capabilities, and the platform to drive our sales teams in a consistent repeatable fashion. And it's also one of the messages as I talk with clients. Like, I I'm I need some I'm so excited for this because I need something to help drive this consistency. That is really the key part of the problem. So that is Clari, we we focus on seeing that adoption with our client base. So that is absolutely one of the tools. Outside of that, I buy a ton of other go to market tech stack, for our sales team. When I buy that though, I I we integrate that into the Cadence experience in Clari. A prime example is we, put a heavy investment into our account health scores, and really did that based on telemetry data. We partnered with a company called QuadScribe that actually sucks up all of our raw every time someone is clicking, hovering over in the Clari platform, we are gathering all that data like any other software company does. How do we put that to more use? CloudSci helped us actually take all that data, merge it with our customer support data, and more merge it with our account subscription data to identify trends of where the risk signals in the business, what are the risk signals on a per client basis, or what are the gross signals on a per client basis. And we boil all that down to an our account health score now, 70% of that score is actually based on usage. That is the most important factor as how people are using the product, which is a similar pattern to every other, every company that I I speak with as well. So how they are using that product is so critical. We boil that down. That is a tactical AI solution, that then we indirectly ingest into the Clari platform. So our reps are actually seeing that customer health scoring, the top risk signals for the customer health scoring, all on a top line layer in their cadences moments. And every day, they can wake up and actually see where my customer base shifting, where is the risk that I didn't know was there before, Where is there opportunity that I didn't know was there before? And I am am I aligned to that? That motion and that being able to bring it together in one place is critical. They don't need to go into another platform. They just need to go into one place. And that is the motion that I, am driving across my entire tech stack is to say, how do I deploy this in a central place so I have one front end for the fields? The moment I need those fields to go into another tool, I know I'm gonna lose an option. So I consistently say drive to this one place. So that's a it's just a really highly tactical example, that has helped us do more for clients, to get in front of clients, to make sure that they are using the tools and getting value from those tools. That is so important for any retention motion and just any customer motion across the board. So how do you do that for one consistent place? That that is our goal, and so we're using other tools but driving it in a very consistent fashion. Gonna keep the questions coming on recommendations as well. The next question here is, what tools do you recommend to ingest, clean, and format data that is consistent and ready to be used in AI supported applications? So I'm not gonna lie. This one's gonna be at the the depths of my understanding. So someone out there, I'm sure, is gonna say, hold up here. But the I believe in the database layer. I I believe that I have individuals on my team as well as an analytics team, that focus on how do we actually make sure that data is clean and ready to go in our we use Snowflake in in the Snowflake layer. I will not try to talk to the tools that they are using to do that. I know DBT is a critical tool from a workflow perspective that they're using. And if but I don't think the tooling really matters. The strategy is what matters far more. What is the actual strategy and the intent and how you're doing that consistently? Are you setting up the workflow so that as things change upstream, everything starts flowing downstream? That is the critical component. If you are doing everything in one off tools I'll give a prime example. When I joined the rev ops world, joined, there were all these Tableau dashboards, out there. And I was like, oh my gosh. In every single one of these dashboards, they're independent calculated fields that sometimes contradict each other when you're looking at all these different things. That's insane. There's no way to drive consistency. So it is really about how do we move that out of visualization layer and how do we move that to the data layer. And so that as analyst teams across the company are interacting with that data, they know they can go to one place. That data is solid. It is good. As things change, it's gonna flow downstream and hit that, but they don't have to worry about that. They don't have to worry about changing that because that is being handled a data layer. That strategy is where I would focus. The tools are subsidiary. It's a strategy that matters far more. Awesome. Next question here from Pete is, having sat through years of forecasting discussions like this, the last four to five years indicate that most CROs and CEOs don't trust the data. It doesn't look like this issue is being resolved either. It also doesn't look like it is trending positively either. Why would this be not sure if it's something we can ignore? So, I mean, I think this comes down to there's there's dependencies. There's dependencies. It comes to the intent. And that's why I say the focus on the data. It is our job to make sure and to convince people that this data is trustworthy, that we have put the time, the rigor, and the effort to make sure that the analysis that we are putting in front of our CROs, in front of our sales teams is trustworthy and it is it is ready. That is the job of a revenue operations team. But for rev ops team is saying that isn't their job, I would highly question that clearly. I take a very data centric approach to rev ops because it is how we are going to fuel this growth moving forward. We have to be responsible for that AI path. So the best way out of that is to drive, is to drive that predictability. If we can if CROs continually say, hey. This is what the forecast was, and the number was completely way off, and you can't add that up. It wasn't a whale deal that whale deal that came in, which was always gonna be the case. If we can't describe it and describe the predictability behind it, that will continue to that'll continue to exist. That is our focus at Clari is to break those barriers down, to make sure that our teams have the right data. And the right data is feeding those forecast algorithms so we can actually drive consistent forecasting, both from a structured and an unstructured perspective. That is our goal of rev ops. So if that is the case, if that is a feelings from, that you are hearing from your leaders about that process, then my challenge to you is go participate. Go make the case for change. That is our job as operations teams is to make that case for change and to convince people this data is trustworthy because we can drive the right action and we're surfacing the risk. And you can start small. Don't try to boil the ocean if that's if that's the vision. Start small. Start with one thing. Start with a, hey. We see this risk on deals. Let's go identify when this comes up so we can surface it and action it. If you start small, then you can compound and compound and build that trust. If you try to boil the ocean and say, hey. We're just gonna go drive for, you know, a 100% accuracy, no one's gonna trust you. You've gotta build that good will. The next question here from, Alex is, do you have any strategies or best practices for how AI forecasting can be used for consumption revenue models? We are not a traditional SaaS company, and our consumption based meaning, is often challenging the forecast effectively as our market is extremely dynamic. Absolutely. This is going to be more and more the case. So the short answer is yes. I would have a rec like, you we've had the tooling as critical here. The consumption data is changing so rapidly. The tooling is critical, and the ability to ingest that data directly into, a forecasting tool, that is paramount to be able to predict the future. We just released, I think, three or four weeks ago at this point, are enhancing that specific use case internally. So feel free to reach out to myself or someone. But it's the ability to bring together the real time consumption data with the other client data that actually say what's gonna happen. We are focusing on that as a company because it is a critical use case. But the ability to ingest that data, that is the linchpin to getting these tools to work to help you predict where you're gonna land. Awesome. Next question here from Daniel is, could you talk a little more about the AI engine powering pulse? It looks like, it's just using historical weighted averages, which is perfectly reasonable way to forecast. What other sophistication is built into the model? I'm not gonna lie to you. All I know is they exist. I I am not an act I am not a data scientist. I trust our data science team implicitly, and they are far smarter than I am. So, yes, a lot of companies start with weighted averages. You can just do that in Excel. The algorithms that we are using to do that are far more advanced. I'm not gonna tell you I'm an expert there because I am absolutely not. I just know when our head of data science tells me this is good and can prove it. I don't question what models and algorithms he's using. I just trust that. So, I've I've I'm not I cannot answer that question with any, with with any degree of confidence. But more than happy to introduce you to people who are far smarter in that area than I am. We resolved a question from Madeline here who asked, how much historical data is needed to make accurate AI predictability? So we at Clari typically use two years. That is our goal, is to get two years of data to start measuring those trends. At the end of the day though, what you have is what you can work with, and the goal is how do you start building that historical dataset as soon as possible. Because the earlier you do it, the more data that you're gonna have to do it. We are predict yeah. There's there we are predicting that, you know, if you don't start now, you will be behind your competition because the rest of the competition is going to start now. And the adoption curves and the accuracy curves increase pretty dramatically over time as these models get more and more data. So if you one year out, that curve starts cramp going up quite dramatically. Two years out, it starts becoming exponential. So the earlier you start and you will run into walls, you're going the things will not work. That is just the data world. But you can learn from every one of those things. The earlier you start, the better off you're gonna be is really the the best advice I can give you is go now. Start building that dataset now. Use whatever you have historically, but then start compounding it with the dish additional data insights as soon as possible. And that's a bit of the whole the beauty of get a system that starts taking those historical snapshots. That is really where the power of Clari comes into play is because we are snapshotting that data to build that repository and measure those changes over time. That's why we can drive higher degrees of accuracy is because we're measuring the history, and weighing what is today against the history. Moving into the final few questions then. In fact, the last question I can see, is from Scott is, I've seen a few companies now providing data quality as a service. Have you ever used this, and what do you think of it? So I'm not I have not used I have not bought anything personally around data quality as a service. I think it is gonna be more and more of a of a focus for companies. But here's what I would say, strategy still rules supreme. I don't think there will be any tool that can come in and just say, this is your good data and this is your bad data. I look across I've seen a lot of CRMs in my day, and most CRMs, I will boil it down to say probably 60% 60 to 70% of the data in there is not good. It's just old data. People haven't deleted fields. It just sort of sits and exists. But how do we actually what's a strategy? Like, that is the most important part. And before you jump to a tool that's gonna tell you, hey. Let's look at the data quality, You have to understand what is a data strategy I'm deploying and then leverage tools to come in and start measuring that. If you just point a tool at your CRM and say even ask the basic question, what's yeah. For the those in the software industry, tell me the ACV of this deal. If you look at any deal that I typically see, there are five to 15 ACV fields on that deal. How on earth is a tool gonna say this is a good one and this is a bad one for this question without that context? When we go through our deployments here, we're very intentional with our clients to say, what are the fields that we need to pay attention to that matter, that trace to your renewals, to your new deals, to your expansion deals? Let's understand that flow. It's the understanding which is critical. Then tools can come on and tell you, are you missing things or you're not doing things, but have the strategy. Invest the time in there, and then more good things will come from that. Awesome. Well, that is in fact all of the questions, John. So thank you so much for taking the time to answer all of them. If anyone does have any more questions for John, any comments, or just wanna catch up about all things forecasting, please do connect with John over on LinkedIn. I'm sure he'll be more than happy to answer any more questions there and really just keep the conversation going. So that said, John, thank you again very much for taking the time to present and answer the questions. And to everyone that tuned in, thank you very much. And we'll see you all very, very soon. Thank you all. Great to have you with the app. See you.