Name: RevOps stories: Predictable growth with AI - Lessons from Personio & Gong
Uploaded: 2025-11-19T17:00:52.909Z
Duration: 45 min 52 s
Description: RevOps stories: Predictable growth with AI - Lessons from Personio & Gong

Transcript for "RevOps stories: Predictable growth with AI - Lessons from Personio & Gong": Hello, everyone, and welcome to our webinar today. We're joined by Anya, Jerry, and Shantanu, who are gonna be talking all about predictable growth with AI, lessons from Personio and Gong. So, So, yeah, really excited to hand over to the team. So, Jerry, Shantanu, Anya, over to you. Perfect. Hello, everybody, and welcome. We will be kicking off very shortly, but, we'll just give you guys a little bit of time to settle in and join. In the meantime, we would love to learn a little bit about you, get yourself introduced in the chat, and also given that today we are going to talk a little bit about AI, I would love to know, as a bit of a warm up, if AI could take over one of your tasks day to day, what would that be? I think we know what Thomas Lopez's task is gonna be, Anya, in the chat. There you go. I'm seeing I'm seeing forecasting in there as well. Very interesting. Forecasting. Let's just automate all forecasting with AI. What do you guys think? Well, they're tuning for the right webinar then if if that's what everyone looking for. Anything else? Text us as well. Good morning. It's early for you guys. Thank you. Documenting stuff. Yes. Admin. Wouldn't that be perfect? All my admin, actually. Somebody could do all my admin. And the funny thing is I'll I'll probably go for a non non, job related pieces. What I always think about there'll be a time when AI is doing all the fun stuff and doing the boring stuff. So I would love for AI to do the boring stuff so I can do the fun stuff. So things like taking the trash out, that'd be amazing if AI could do that. Nice work, Craig. Brilliant. Exactly. I'm with Craig on that. Yeah. Especially hoovering. Oh, hate that. Alright, guys. We'll give it one or two minutes, and then we'll kick off. Just give people a little bit more time to join. We'll we'll kick it off saying about one or two minutes. Invoice workloads. Interesting. I might just start the first sign up for you on that one. There's a few good companies doing some great work now. Documenting. We all love the admin, isn't it? Keep on minding the children. Oh my. Nice. Great. Got a great, branch of people between Paris, The US, Spain. Buenos Dias. I can try practicing all my language skills. Perfect. Okay. Shall we kick it off? Sorry, Shantanu. Perfect. Do. it. First of all, everyone, welcome today, to RevUp Stories, how to drive predictable growth with AI. Before we get started, I want to go through a tiny bit of housekeeping. We have a really great session today with two wonderful speakers that I've had the luck of working with both of them. But to make sure everything runs smoothly, we're going to have the audio muted for the duration of the session, but we want it to be super interactive. So please use the chat to discuss thoughts, have a bit of banter, and keep going throughout. However, if you have some specific questions you want to ask we do not want them to get lost in the chat so please use the Q and A tab for any specific questions and we will answer them back through the chat so that everybody gets to see them. Great. So let's kick it off then. Let's start with some intros. So my name is Annie Friedrich. I look after product marketing for Mia here at Gong, and I would love to pass it over to my cohost to introduce themselves. Jerry, do you wanna start? Thanks, Anya. Hello, everyone. Jerry Cunningham here. I am the head of rev ops for Gong in EMEA. I've been at Gong for three and a half years. And before Gong, I was at LinkedIn for, nearly seven years where I had the pleasure of working with Shantanu at LinkedIn and also at Gong. Delighted to be here today. So, Shantanu, I'll hand it over to you. Thanks, Jerry. Everyone knows where next job is gonna be. Yeah. I'm Shantanu. Delighted to be here. I've been in revenue operations now for more than more than ten years. Very interesting space. I'm very excited about all the interesting stuff that companies like Gong are actually bringing to this space. So also but I I was a employee for about almost two and a half years. Worked with both Jerry and Anya here. So excited to work with you again. Before that, I was at LinkedIn for five years. Been in a couple of smaller companies like Nitro, and now currently at Personio, which is a German HR tech company. And, excited to be here and talk about forecasting, one of my favorite topics. I am so happy you both are here. But before we dive into the meaty stuff, I'd love to take a step back and just, reset on why we are all here. If you think about it, revenue teams at the moment are facing unprecedented change and it means that it is harder than ever to deliver predictable revenue. Why? There's a couple of factors that lean into this. Well, first, forecasts are built on a really shaky foundation. The number of buyer touch points has doubled in the last few years, data and tools have exploded and that leads to a lot more silos which teams are really struggling to navigate. In fact a recent survey showed that 99% of rev ops professionals struggle with data quality. And when you don't have the right data, it means that you're really left open to subjectivity and bias. In fact, 53% of deals that make it into commit or to late stage actually never close. It's not really surprising then that 79%, yes, almost 80 of organizations miss their revenue forecasts. So the reason we are here today is just to talk a little bit through how teams are managing that change and how rev ops leaders can win more with AI. So flicking through the agenda quite quickly, we'll start looking at the evolution of operating rhythms and how teams are evolving to meet those challenges in the different ecosystems. Then, Shantanu and Jerry will lead us through a little bit about Personia and Gong's operating rhythms work. And at the end, we'll look at how both companies are using AI in their forecasting cadence. So before we go into the evolution of operating rhythms, we're just gonna have a look at what is actually happening with Teams on the ground and with the ecosystem and why all this change is necessary. If you think about it, most revenue organizations still run on systems that were built for a different era. Oops. Sorry. That double jumped. One that depends on manual inputs, static and siloed systems, and constant change management. This is too slow, too rigid, and too reactive for how revenue teams operate today. The best teams don't just manage change, they're actually engineering it. They're connecting systems for visibility, using intelligence to guide their actions, and using automation so that everyone moves together. That's essentially what a revenue AI operating system makes possible. It drives predictability so that people can see what's coming and avoid those end of quarter surprises I know you all love. Drives productivity through less manual work and clearer priorities with stronger routines, which our gentlemen will also be talking about. And at the end, drives revenue growth by freeing up time for people to focus their energy where it matters most and not get stuck with all that admin that we said at the beginning we would love to automate. So thinking about that, I'd love to pass it on to now to Jerry and just talk about how these operating rhythms are evolving and why the old world of forecasting was littered with inefficiencies. So, Jerry, over to you. Thanks, Anya. Yeah. We're gonna talk about operating rhythms in the four in the the context of forecast operating rhythms right now. And I'm gonna flash something on screen now that might trigger some people. If you if you want if anybody has ever seen a spreadsheet like this, please light up the chat. This is an old type spreadsheet that we used to use in forecasting in days gone by. You have the CRO entering their forecast, reps entering their forecast, managers entering their forecast. You had to track buffers between layers. You had to make sure that the person in enterprise didn't sort and filter all of the the sales because, that would have broken the the entire spreadsheet. When I got into TechFirst about ten years ago, we we used the spreadsheet like this. Shantanu, you will remember, at a company you we used to work at. I I was responsible for managing a file like this. It was a beast. I think it took me, like, nearly, like, two hours to press f nine and refresh the whole Excel model, and that was how we built our our forecast from a top down perspective, and that's how we ran our forecast on a day to day basis. Do you remember, Shantanu? Oh, I absolute and I also remember the time it used to break every few few weeks or so. Also, because, you know, someone like you mentioned, someone filtered something. Some data was static. You're like, wait. Was that actually last year's data which came in, or did did people in the wrong data point? It was just very manual, very, very prone to error. Spot on. Indeed. There is, like, a there's an evolution of how our forecasting operator operating rhythms have have played out. So most of you are are are, according to the chat, a lot of you are still in that manual space where you're using spreadsheets and you're using CRM. The downside of that is you're you're relying on manual entry from reps around, like, what are the next steps. You're trying to track contact activity at the the the task level in Salesforce, and you're you're kind of relying on manual entry from everyone across the organization. Yeah. We were we were still there at a company we used to work for. Then the next step of that evolution is going a little digital where you would have your CRM and your spreadsheet and potentially a piece of forecast software. And that forecast software could manage stuff like, sales process and stages and trying to tie back stage progression to potential output in forecast, and then tie tie a score to that like a a here's where we could land in terms of revenue, and it might give you a range of where where your revenue would be. And then the next step on top of that would be using intelligence, using insights captured from actual buyer interactions, customer interactions, such as conversation intelligence. This is where Gong started out, and this is where when I came into Gong as as a business, I was kind of blown away by the level of depth and contextual data I had available to me to figure out what is actually happening at the cold face of sales. And it took away a little bit of the not a little bit. It took away a lot of the happy ears that I used to get from my sales reps telling me what would happen. I could actually do this this myself. Shantanu, does that resonate with you as well? I think it resonates. So and I think the the thought I have on this, Jerry, is I think there's almost two parts. If I if I look at the evolution from point one to point three, there's the actual business intelligence part, but there's actually productivity. And I think if I think about you mentioned spending two hours. I think most companies I know who use the Google Sheet or Excel based modeling today, you literally have one person's entire work they spend on doing pipeline and forecasting and all of that in that entails. I think that's such a lost loss of time for and I'm talking about one rev ops person. Add add back the amount of time a salesperson has to spend, leaders have to spend, you're stitching sheets together, you're sending an update, you're manually sending notes. There's a question you do more analysis to send back answers. Just the amount of time that's spent there, that's huge. So that's productivity. And then second, just in terms of human you you hit the nail on the head, just the level of depth and the context driven forecast, it just completely changes the game in terms of the actual quality and accuracy of the forecast. Yeah. And it's like, how do you pivot between the the bottoms up forecasting and the tops down forecasting? So a lot of us in rev ops, we our natural gravitation is towards that tops down piece where we we like looking at the data. We like looking at historical analysis. We like seeing how history will will determine what happens in the future. But I think for me, the biggest moment when I joined Gong was I was able to get to the depth of what's actually happening with customers that I could never really do before, and that's where this intelligence layer comes in. But there is a there is another layer, and that's the the AI powered layer, which is how conversation intelligence plus other data streams and data triggers or signals can feed into an overall and, like, holistic view of where we could land in terms of our revenue. And for like, an example there would be, we don't need to just take conversations. We can take intense signals from different places. We can take, triggers that come in through our Marketo or or our inbound routing, or we can take funding information from from outside. And that can all be layered into one engine that can tell us using AI and using an insights layer as to how we could actually land. Shantanu, are you what kind of interesting stuff are you doing around this. So I think persona? and and at Persona, we wanna I think we wanna, Gong's first European customers for forecast, I remember, a couple of years ago. And right now, it's very if I think with the AI powered part, the place where it's adding the most value to us is you you mentioned the top down and bottom up view of forecasting and how there's often a few deals which make up a significant part of the forecast. You're looking at pipeline. I'm sure most of our listeners here spend time weekly looking at here's my most likely pipeline amount. Here's the amount left to go. Here's specific conversion rates you could look at. But I actually start within persona, we start looking at what's the Gong view on AI powered score. Again, you mentioned there's so many signals feed into it. We use different pieces on intent. We use different pieces on customer health. All of that, if you bring together, we're actually looking at if your most likely pipe in is a €100, of those €100, how many are actually high priority or or high likelihood based on Gong? How many medium, how many low? So long as high is very close to most likely, we're safe. If not, we're far. And we use that methodology, and we found actually, Gong's forecast was within 2% error rate of the previous quarter, which is fantastic. So I'm really, really, really loving the AI powered intelligence layer here. Nice, Shantanu. Thank you for that plug. So, Anya, you're gonna talk about an interesting what we wanna do? Yes. I am. Thank you guys so much for talking through that. I'm sorry. The the slides are getting ahead of me. You have seen all of these different operating rhythms, but we know that every organization is different. So I would love to know from you all, how do you power your operating rhythm? What is it that what stage are you currently at so that we get a little idea of that? And I'm just going to leave up this slide while we run the poll so we get an idea of how you guys are running it because I know some of the terminology might be new to you. So I'll just take a second for that. Poll coming up. Perfect. I think we're seeing that most people are kind of having digital. So a lot of you are in that second column of currently using CRM and forecasting. There we go. So really interesting. Okay. So we see strong gravitation towards that digital that CRM system. Interesting. Not seeing the poll. Interesting. Don't worry about it. We'll move on anyway. But yes. Okay. So we're seeing a lot of that kind of CRM and forecasting software. Maybe you're thinking about intelligence. Maybe you're thinking about how to move into that AI powered layer. And one question we often see is, why can't we just power AI with the insights from conversation intelligence? We have our CRM. We'll get some kind of recording tool, and things will come together. And it's true that a lot of the context is locked in conversations, and those become available when you record calls. But it takes that context to be connected to your CRM and deal insights, for those real gains to be unlocked. So, Jerry, I'm gonna put you on the spot and have you talk through a little bit about how at Gong we pull all of those insights together and how we are helping to generate that AI powered forecasting. Yeah. Thanks, Anya. So at Gong, we have this, this idea of a the the Gong revenue graph, which is essentially us pulling together contextual data from many different streams and having it as a as as the name would suggest, a revenue graph that ties together all of the revenue facing interactions you have with your customers. And that could come from the conversation intelligence data that you you you generate. That could come from activities that you have done in the past with that customer, such as old tasks from your CRM, or it could be information or data you're getting from third party applications such, as I mentioned before, like, intent signals coming from from, any of your intent data, demand base, etcetera. Or it could be funding data that you get from your your whatever pitch book or whatever similar tool you use for understanding what funding would be, and it's layering all of that together into one tapestry of information that you have about your customers. And it's then the next stage of that is is tying all that together to make sense of all of that data in an intelligence layer. So finding themes within that data, what the trends are, what signals are are coming from the data you you layer together with those customers, what risks are you seeing from those customers. So it's that middle intelligence layer that makes sense of that revenue graph that you that you've generated. And then, like, what Gong does is once you have generated that data, made sense of that data, it allows you then to automate and orchestrate from from those, in intelligent signals. Some examples of that are, like, you could potentially run a workflow based on signals that you're getting or some some triggers that you're getting that that a customer might be flagging on their renewal because they have mentioned, that they're not happy with with this service for three or four times. And Gong will will have a new tool called Orchestrate that will be able to make all of those workflows and orchestrate them so that you can drive actions and workflows based on the signals that you're getting from your customer layer. That is that's the that's the dream or division that our CEO, Amit, has for for Gong is that you have data capture, make sense of that data, and then drive, like, really strong actions from that data. I hope I've answered your question, Anya. Shantanu, do does that resonate with you as well from what you're doing at at Personio? I think totally resonates. And I think the the part which I love about, bringing all these different pieces together because I think there's also a chat or there's a there's a message in the chat which say which talk about how there's an element of what the machine layer tells us and there's an element of of human touch in in order. I think that's spot on. There's a bit of both. A a good forecast is almost looking at both and trying to find, one word I love is triangulate. Triangulate all these different pieces together. And and the part which I love about the new revenue graph and orchestration that you've set up, to a large extent, you're already triangulating multiple data points and giving me a very, sense check number already. So that's a fantastic start. And then on top of that, obviously, applying the human layer helps a lot. Perfect. And, as you're talking about applying that human layer and we spoke about operating rhythms, I think what I where we'll move on to next is really understanding how you then use those systems, use that data, use that intelligence to help drive your own operating rhythms. So, Jerry, I'm gonna pass it over to you first to talk through a little bit about how Gong's monthly operating system, rhythm looks like, and then Jerry or Shantanu will pass over to you to get an idea of how that works with Prezonia. Hopefully, Great. our our rhythms are the same, Shantanu, because you taught you taught me everything I know about, operating rhythms. It's a, fine matter. okay. So, like, what what we do at Gong is and I'm gonna put a visual up here, but imagine that our week kind of starts on a Thursday here at Gong. And the reason I say that is because our reps will enter their forecast into Gong on a Thursday, and that sets sets then a, a series of events where that number will then roll up to our executive team the following Thursday. So it's a it's a week long process if you if you wanna imagine it like that. Reps submit submit their forecast on Thursday. I will sit down with the reps on fry Friday and their their managers, and I will go through a fairly rigorous, forecast review where I will inspect everyone's forecast from a bottoms up perspective. And then we will go with the VP level on a Monday. I will do forecast and pipeline pipeline reviews with VPs on Monday. Tuesday, we have our CRO CRO call. Wednesday, we don't have any forecasting, thankfully. And then Thursday, it's back to the exec level, and also the reps start again. So we have a singular process, here at Gong. But what I have also, like, outlined here on the the operating rhythm calendar is we have, regular touch points every every week for pipeline, for deal reviews, and it is like it is set in stone. We do do not deviate away from our operating rhythm. It's the rhythm of our business. We like, ROB is is a fairly common acronym here we have here rhythm of businesses, everything, and we we are tied to that rhythm. Shanton, you probably have something similar at Personio. Yeah. I think it's very, very similar, I think. And I and I and I think I love the the point you mentioned about rhythm, and and Jerry is a musician for those of you who don't know him well. I think rhythm is really crucial obviously for for any upcoming musician, but also in the forecasting game. And in fact, the overall revenue game, just the rhythm of repeatability of getting us a cadence done is so so important to actually drive success. It's all about the process, and I think it's very similar from but I'm also liking on your monthly calendar, the very clean view on not just what's happening from a forecasting at a deal level, but also looking at enablement, looking at reviews. I think that in bring our entire cadence together is is super crucial. And and thanks for that, Shanyu. And and one more, like, salient point here is that we run everything from Gong internally in Gong. We we tend to not, and not that we tend to. We we actively discourage people using, spreadsheets for forecasting, do doing their own offline models. Everything we do has to be within Gong, and we are now in a place where a 100 a hon 100% of our operating rhythm is is through Gong, which, we're very proud of, and we we drink our own champagne. So, I I just had to plug that one. That is fantastic. That's great to see. So an an example of what my forecast calls with my reps and my managers would look like. So this is this isn't an old world without using Gong forecasting. I would have had to ask the so on the a side, they would have had to inform me about the deal status, what happened in the deal, what's the when's the next call, did you speak to the decision maker, when's your last call with them, etcetera. It would have taken me a lot of time to do that level of depth. And in a company I I worked at before, it took me hours and hours and hours per week to do this. So a lot of my time was back and forth trying to understand deal house so I could then filter up and report up on on that information. But the new world is where I have essentially cut out that entire step. My forecast calls now look a lot differently. I'm trying to identify risks and and, like, challenges within overall forecast rather than getting to the depth of the deals. Gong tells me that now. I can get to that within two clicks within Gong. I can get from rep forecast to individual deal and understanding the activity would within that deal within two clicks. Shantanu, it was probably an eye opener for you as well when you started using it. Oh, absolutely. In fact, I was talking to a rev ops leader, I think, just a month ago, actually, and and they are still in the without AI forecasting tool world. And he was talking about how we talk about this concept of swing deals, again, the big deals which make a big difference. They do at at the end of the forecast call, they actually go in and there's a spreadsheet which needs to be updated every week with the top top 15 deals. Again, the deals move. Some of them are no longer relevant. You're like, where did that deal go go? So it's they spend so much time trying to really get that list right. I think it's it's somewhere in your on your, bottom of that, box there. And when I when I I actually opened my laptop, this is how we do it, persona. Look at deals on this. It shows us all the analytics you need and so on. His eyes literally jumped out of his socks saying, oh, we can actually do that. So, yeah, there's a it's it's almost like it's literally it's like playing Xbox, you know, versus the old, I don't know what the gaming console you'd call it, the old Nintendo, the first version. So it's huge. I don't remember Nintendo. I don't remember Nintendo Shantanu. yeah. Sorry. You you're too young for that, Jerry. No. I do. I remember it well. So here's just a, like, a visual example of the different layers within the business and how they can use Gong within their operating rhythm. So, like, a CRO, they they might interpret this the exact same analytics table when they when they open their Gong every morning. It it will be the same thing that the AE looks at. It's what like, what's my what's my bookings? What's my coverage? What's my pipeline created? What are the different stages my pipeline is at. We can facilitate all of that within Gong, in the in the in the deal board, in the analytics analytics views we have out of the box. Shantanu, over to you. What's your rhythm look like? Thanks, Same. as mine. If if you go to the next page, it it looks like it's literally the same. And, again, apologies for those who think it looks it looks very, very different. It doesn't. The only thing I'd say, I've literally zoomed in. Jerry had the full month. I've literally zoomed in, to to show you what it looks like for a week, at a very at a very large level. Again, I would say for us as well, I'll I'll go to Jerry. Well, let's say we start on Thursday because, Tuesday and Wednesday, we're using we're going through deals. We're having one on ones. A lot of you might have gone deep into this. In fact, we could probably do an entire webinar on what an effective one on one between, a rep and a frontline manager should be. But let's say you have all your pipe reviews. You have all those numbers in Thursdays when basically, all of our reps and let's just let's take a new business number, AEs. Right? AEs submit their forecast in Gong. We then have bunch of forecast meetings where frontline leaders would would work would would would go with their with through their AEs. Eventually, at the end of the day on Thursday, the frontline sales manager or sales leader would submit their forecast. Then on Friday, the same the same thing gets repeated with the team leads. The frontline leaders with the with the heads of those segments, those folks then also work with rev ops partners. The other thing which which you might be very interested in for those of our listeners in rev ops, at this stage, really, our frontline leaders don't even need rev ops support anymore in the front in the first conversation because you have a very clean model giving you an output. You have everything on the on the platform. So you don't you you might have a question, hey. This is a field I want to also look at or something. Can you add this into my deal board? But other than that, they don't really spend that much time. And that's a big time saving for them and for us. By end of day, Friday, essentially, the VPs of the different segments, let's say, the new business VP goes into the number and they forecast it. We actually have a very interesting model we've started using at Personio. We now do we do biweekly forecast and pipeline calls on Mondays. So, and we're also going we're actually going in by each each of the CROs direct. So we actually go into new business, existing business, we're trying to go by by segment as well. And what that depth gets us to do is on once every two weeks, we're literally looking at the forecast for the month and for the quarter. But once every two weeks, we're actually looking deep into pipeline and everything around pipeline generation, pipeline coverage, pipeline hygiene, and, again, all of that, we're actually using Gong. Even the all the pipeline views, using Gong dashboards, Gong analytics to actually go deep into that. And that's giving us a very clean view of both the sense of what we need just in terms of forecast, but also looking ahead for the pipeline that we need for future quarters. So super helpful. Super interesting. I love how you guys went through it and also, Shantanu, that you spoke a lot about the repeatability and a bill and accountability. And I know that next, we're gonna talk a little bit about kind of those metrics that you are tracking with AI and how you're looking at it. But before we do, I'd love to ask our audience, like, what are the metrics and and please pop it in the chat. But what are the key metrics oh, sorry, that you are tracking. Oh, I don't know what happened here. There is a yeah. What, key metrics are you tracking? If you can let us know in the chat in the chat, and what are you looking at when you're pulling in your forecasts, and what are those key indicators that you are looking at at the moment? Shy. Ready to answer? I think this is one of those things where everybody is a little bit I know. It's like, we don't wanna say the wrong thing. Don't worry. We'll just oh, this this is a nice. We have a few brave souls. Hypothesis and win rate. Yeah. Well, I'm not in RevOps, so I don't know what MPI or GA or ADR means. But, Shantanu, I'll pass over to you to run us through a little bit about what you look at. Perfect. Great. Let me go on. So if we let me just push this ahead. So I'll I'll just clear this slide because it's quite a busy slide, but this is almost a view of, how I think about forecasting to a large extent. Jerry mentioned top down and bottom up as well earlier. So if I take you to the top down and the on on on really the the top half, very, very specifically, you'll see here I've split this into acquisition, expansion, retention, and I'm hoping a lot of our customers here are looking not just at new business but existing as well, which is huge. And if I even go back to two, three years ago, most of the forecasting solutions, including Gong at that point three years ago, was really focused on how do we look at the acquisition forecast. The existing business world was a little bit shaky. We don't know whether to go into that or not. So, bunch of different metrics you can look at, but on the acquisition side, top down, really, for me, the number one thing and I think Jerry called that out as well as a couple of others in the on the chat, pacing how we're doing this quarter. At the same time, at the same week, so let's say you're looking at quarter of thirteen weeks, week five of last year, same quarter, where were you? Or the same or or where were you in q three versus q four? We're in q four right now for those for those in the same quarter. So that's something which is which just gives a very quick predictive view on where the where the coverage is, where the forecast is. I won't worry other others within the acquisition, but, again, you do blend a lot of them together. Pacing alone won't give you the story. It it's clearly part of the story. When you go to the retention side, again, similar metric. The one which I would I would skip a little bit on retention versus pacing, it's also looking at if you're in a SaaS, again, this is a SaaS specific forecast number, which is if you have a 100 a $100 of renewal coming up in the quarter, at this point, if you've if you've closed or renewed 30 of those $100, where are you in terms of your overall forecasting number? So this gives again a sense of retention percentage versus pace line of the pacing of the baseline, not necessarily as a percentage of time spent. So just just a small nuance there. Again, multiple things you can look at including how you're doing versus product adoption, slip deals, all of those things, but, again, I'm not going into that. The so that's all, let's say, the top down. And the reason I if, again, it's very difficult to say here's three things only. But if you're adding three things only, once you've had those two pacing indication indicated numbers, the the bottom part actually goes back to that human element. There are those swing deals, then depending on the on the type of company, whether you're volume business or not. But even in a volume business within those small smaller deals, you might have a a select few which have a large outsized impact on your forecast. So how do you go in and look at the outcome of those large swing deals? That's the third metric to look at. So if you go to the or I'll I'll go to the next page. This is how we look at it with use and, again, using Gong, we've been able to do this quite well. For some of you, probably familiar with this view, but, essentially, what Gong does, it shows you literally the the the week on week pacing of pipeline build by different stages, by different categories. This one is showing is the forecast category of best case most likely. I'll I love to use it based on even stages, which is whether it is discovery, stage two, stage three, stage four, or pipeline, really helpful. You can go back and look at it for the year, for the quarter, for the month. Second, I mentioned renewal pacing in a similar way. Just literally looking at renewals that you have, how many going in. And and within within renewals, the part which is very handy is being able to map. I think, Jerry, you talked about the revenue graph. Right? All those different integration and data points you're bringing into this and being able to really have a view on view on the renewal pacing. And then finally, swing deals, and this is where, the AI part comes in. I think there's a question from Robin about how do we really use this for this. So if it again, this is these are all fake deals or or or or whatever on on the sheet here. But let's say, if I look at the first two deals, you see those numbers nineteen and twenty three. Looking at those swing deals, I'm like, why is this deal in most likely if the if the Gong score is anything less than 60, for example? So you could actually go in and see that. And with with, with the AI agents now that Gong has, you can literally go in and ask based on the conversations, is this deal likely to close this month? We literally go into that level of detail and helps us get a quick quick check of the forecast and and really come in come into that. So with that, I will push it back to, I think, Anya. Yeah. No worries. And then thank you so much for running through that, Shantanu. I love your how you're building that repeatability and accountability into the process. We do have a very quick poll because I know we're running short on time. But before we go into Gong, how Gong leverages AI forecasting, I'd love to know from you guys, do you currently use any kind of AI tool to, support your forecasting? And, hopefully, we'll see that come up. No. There's a good mix in here. I think that's not quite surprising because every single organization is different and looking at where we saw at the beginning, the different stages that you're at. And to be honest, it can also be really hard to think about what are the tools we should be using, how should we be using them, especially with so much out there? So, leaning into that, Jerry, I'd love to pass it on to you just to tell a little bit more about what we do at Gong, how we use that data and information to fuel our AI processes, and we'll talk through a few of the key elements that we have in there. Thanks, Anya. And mentioned something earlier that I think is really important, and and somebody mentioned it in the chat also. The human element of of forecasting will never go away, and it's my opinion that as as much as we optimize our AI models, etcetera, you will always need a little bit of that gut feel on forecasting. It's it's an art and not a science. It's probably it's getting closer to 60% science and 40% art. It used to be, a 100% art and no science, but it's getting there. But I I will always lean towards using my gut sense to determine what where we're gonna land on a number. But I will heavily lean on some of the AI functionality within Gong to to to get me there. So we we have a new this is another shameless plug, but we have a new tool called configurable forecast boards, which essentially is a new look and feel to how we we set up our forecasting forecast module. It's drag and drop functionality based on our our metrics module in the back end. Won't get into too much depth, but, essentially, what it is is it allows you to drag and drop and create your own view and your own look and feel to how you want Gong forecast to to work for you. Somebody won't, explain it. It's like a a really, really fancy spreadsheet. It is without all of the downside. That's I will leave that there. Okay? So to answer Anya's question, how how do we leverage how do I leverage AI and forecasting here at Gong? There's probably three main ways I leverage it right now. That's being, like, really, really aggressively prepped before I enter forecast calls. And there's two things I use to to prep in those cases. One is the AI deal predictor and AI briefs. I will get to those in a minute. The second is how I pressure test the forecast call live when I'm with my reps and my managers. And then the third one is how do I find paths to win? How how do I spot certain themes within my my, my reps forecast that will allow me to get get around some risk that maybe comes up in in my business? So the first one I wanna touch on is, like, the the prep side. The deal predictor Shantanu, you already kind of touched on this, but, the the Gong like the deal likely had good scores that we have in Gong is based on all of the contextual data that we've collected. So it tells us, like, what are the reductors in the elevators? Is a champion highly engaged? When have they met mentioned pricing in the deal? And that allows me, at the deal level, to have a, like, use my own gut sense to validate whether a deal is gonna come in or not. I won't I will not use this score as, like, this is going to happen, but I will definitely look at this, and I will lean to towards this to to help me come come up my own gut sense of of where we're gonna land. The AI briefer, this is probably my favorite tool in the entire Gong, universe. AI Brief essentially gives me structured, account summaries or account briefs that tells me what's happening at the deal level or at the account level. If I'm going into a a call with my CRO tomorrow, I will do a a deal briefer if if I know he's gonna ask me about certain deals. So, like, Acme Incorporated is a 600 k deal. I need to know everything about Acme. I will do a deal our AI briefer before that call, so I am, like, aggressively prepped for that for that call. I use the word aggressively prepped because I I like to be aggressively prepped before these forecast calls. The second thing is how do I pressure test the reps' calls? An example there is we we have we use Medic as a sales methodology or sales framework here. We can tie Medic back to, our our contextual data within the calls. So if if Gong doesn't notice that a compelling event has been mentioned in any of the the the, interactions we've had with that customer, it will tell me, and that's where I will focus in the forecast call. I am very maniacal about compelling events. And if a rep can't tell me a compelling event, it means that that deal isn't gonna close when the rep says that it's gonna close. So Gong can tell me that based on the context it it gets from all of the interactions we've had with that customer. That's just another example how how we use this internally. And then the third one is, how do I find pathways to win? This one is a relatively new tool that we have or a new, a new feature and agent we have. It's called the AI theme spotter. It allows us to, query many different deals or many different accounts at scale to pop out what the main themes are are coming up. So it it can give us, like, common objections. It can give us pricing objections. It's a really, really interesting use case that has helped me to understand more about certain regions, certain account pockets, certain reps, certain managers. And then if if I find out certain certain information within that, I can then pivot to enablement or pivot to, performance management or whatever that might be. So, yeah, that's that's in a nutshell how I use it. Prep, pressure testing, and then finding pathways to win or pivot. Anya, did I wrap that up in time? I think I just just on the mark. actually bang on the money. It's like we have about two minutes left to go, and we would, have one feedback slide and one takeaway slide. So you guys I I couldn't have timed it better if we tried. So, yes, before we all finish up with a couple of key takeaways, we know that everybody loves a good survey and so do we. So we're going to pop up a quick survey for you guys, to please give us your feedback. Let us know what you liked, what we can do better. We always wanna make sure your time is well served, so please do send us that back. I'll give it a couple of seconds because I can't see if it's popped up or not. But I hope you see it. Let me know if you are not seeing the survey, and we'll hop on to the key takeaways now in a sec. Perfect. Thank you, Larry. I'm a little bit blind on this side of the screen, so it's great that you're looking out for me. Right. So while you guys are filling out that survey, I'm just gonna go to those last key takeaways because I'm conscious that we were out of time, and I know you all have a lot to do knowing these two gentlemen. I never wanna take you away from your forecast, your meetings, and anything else in driving revenue. So just to finish up, there are three core things that we went through today on how you can help to build a more reliable, forecast and also adapt to all of that change that's happening right now. First is to think about evolving your operating rhythm, to go just beyond those manual spreadsheets, have a think about how you can bring in more data. And then when you come to that stage of AI using real time CRM data and deal insights analyzed by AI to improve predictability. But as Jerry had mentioned so accurately, never forget that gut feel. Second is build your revenue graph, which really will underpin everything. That just means looking at your data, what you have, where is it, how are ways in which you can connect it better so that you get a holistic view of what is happening across your business. And last but not least, as mentioned by Shantanu, is fewer, better metrics. Think about what are those key things that help you build out that forecast and forecast accuracy, what you need to track, and align all of those key stakeholders around those metrics so you can drive growth for your organization together. And that is it for us today. Thank you all very much for your time. Thank you for spending the forty five minutes with us. We really appreciate it, and we hope you enjoyed the session. So have a great day. Thank you.