#116 Sales Forecasting in the Age of AI – with Janis Zech & Philipp Stelzer (WeFlow)

[MUSIC] Welcome to the Rehtup Slap, a podcast exploring the art and science of revenue operations. To find more episodes and resources on scaling your revenue engine, visit getweflow.com/rehtup. [MUSIC] We know, we know, there are plenty of AI note takers out there. But there's only one purposely build for Salesforce. And that is Weflow. Weflow doesn't just record, transcribe, and summarize your meetings. It also suggests Salesforce field updates, thematic, spies, next steps, or any other custom framework that you want to implement. This works across any of your standard or custom objects, and across all field types, like multi-pick list, text, number, and currency fields. Weflow also writes AI-based follow-up emails, saving your reps even more time. And the AI coach analyzes recordings to help your reps run better sales meetings. And the best part, we've lost real time, two-way Salesforce integration works out of the box. No manual field, my pain, no complex setup. You'll be up and running in 30 minutes, and you benefit from our create support. It helps you customize the prompts based on your business requirements. If you want to see what hundreds of your bread-rops peers use Weflow to get better sales for stata and rep productivity, just go to getmeflow.com to get your free trial today. [MUSIC] Hello, and welcome to another edition of the Repups Lab podcast. My name is Philip, and I'm here together with Janus. Hello Janus. >> Hey, for the pause of going. >> Yeah, it's going well. It's going well. It's just the two of us today. So special episode where we just want to use the opportunity and reflect a little bit on forecasting. And more specifically on forecasting in the age of AI, has AI fundamentally changed in the last three to 24 months, I guess you could say. How you do forecasting, what forecasting means for sales. Or is it all still like a big fad? No, I don't think so. But what has been the impact of AI? And what are some of the lessons learned based on many of the conversations that we had with customers, prospects that are using Weflow or not using Weflow? I think we spend a lot of time talking to sales leaders, operators, revenue operation teams, CROs that are all haunted by that same question of how do I get to a reliable, repeatable and accurate forecast number for the end of the quarter, for the end of the year, that they need to present to the board. And I think what we're trying to do here in this episode is to sum up these conversations and add our own thoughts to that. And yeah, just have like a good, nice discussion about it. Does that sound good to you? That sounds awesome. So you're very excited about diving into the topic. I mean, I think it's fair to say that we've been building a forecasting tool now for three years. And I think that's the first time I've been building a forecast tool, I think it's a very good thing to have a forecast tool. 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We had a variety of shows on this topic operating cadence, we actually have a master deck on that, so if anyone is interested to sit us up on LinkedIn, I'm happy to share that. But that's something that almost everyone runs, and then the question is really like, what do you need to do to build a system that allows you to forecast the number accurately? And so let's maybe like dive a bit into these different foundations that are needed to actually make that operating cadence really fruitful. So let's start with maybe one that is a bit unintuitive, but it's very, very important, the data foundation. So Philip, what do we mean with data foundation? Yeah, I think, I mean, one of the key things, so if you talk about this operating cadence, one of the things you mentioned is these weekly meetings or just like cadence as of meetings that you have spread throughout the quarter, throughout the week, throughout the month, where you just discuss your sales pipeline. And in order to discuss your sales pipeline, you need to kind of like anchor those conversations on specific data points, because otherwise like it's all super subjective. And that's very hard to do to have like fully like to have only subjective conversations about deals. Yes, there is something like a gut feeling that you can have around the deal, but like realistically, if you run like a sales organizations with thousands or hundreds of sales reps, like you don't want to rely on gut feeling, you want to rely on like a very steady, reliable ramp up process, where you train reps to really understand and learn, okay, you know, how do I go into a deal? What kind of questions do I ask to qualify? What kind of like, you know, signals, key events, critical events, whatever sales methodology you're using? I'd like, do I need to collect and work through in order to have like as high as the likelihood of possible to actually, you know, turn that like first conversation, like the qualification into demo from a demo, maybe into a POC, then from a POC into procurement process and then to a closed one, depending on what like stages you have, but I think realistically, this is what most of you will be going through and attached to each of these different stages that a deal can be in, right, there's different metrics that you want to take a look at. So you need to collect like information around these metrics. It can be a number, right, it can be something as simple as the number of engineers that is working in that company, but it can also be something like a critical KPI that they need to hit. It's, you know, can be, I don't know, like can be really like literally anything, it can also be like a contract end date of like a competitor or something when like a contract is ending soon. So lots of different KPIs and metrics that you are collecting. And you want to have those meetings that you're having, you want to center those around those conversations. So if you use, for example, let's use Spiced, I'd like one of the key things that you want to collect in the beginning is you want to understand their situation and how that situation, situation translates into certain pain points that need to be solved for them. And if you now have like a weekly sales meeting where the rep cannot actually like, you know, reliably and like trustworthy, like talk about the situation of that deal and the pain points that they are hitting, then that's already like a big red flag, like, you know, basically meaning, okay, the rap doesn't have like a good understanding of where that deal is coming from. What the problems are that this potential customer is facing and meaning that they also cannot kind of like work with that customer on resolving those pain points and then they cannot really actually really sell into that customer. So I would say, so cadences like before you can do like proper cadence. I mean, I think you should always like try to start having a cadence. But I think what you need to align on first is sort of like, okay, what are like the, what is sort of like the methodology, what is sort of like the framework that we're going to use within these cadences to discuss the deals. What are like the questions that we need to ask to get to sort of like the answers around the metrics that we want to collect. And how can I align with the rest of the company on a shared terminology and a shared set of like understanding on how we are working with these deals. And I think this is something where AI can be extremely impactful and helpful because you can add this objective layer to everything where if you have an AI that automatically collects emails automatically records your meetings already already. automatically captures all those touch points that you're having. It can give an objective summary of these conversations, and then help you fill in information around these metrics that then the RAP can use to really talk about the deal, but also the manager and the Bravop team can use to really evaluate whether the deal is in a good state or not. That's sort of like, I think, for me, always the starting point. Maybe just to add there, it's basically the question of, what is a sales qualified opportunity? If you have an unaligned definition of that, you basically compare epilobus peers and you just don't have a comparison. That's really the starting point. What is a sales qualified opportunity? What is the basically stage entry criteria and then what are the stage gates for each of the stages, right? So stage exit, stage entry criteria, and being really aligned on that. You then have different type of deals signals that basically give you an understanding of deal health. I think we've all been in that world where you look into deals and they're fully qualified. They are starting stages, they're being pushed, or there's no velocity in terms of communication. So basically to build a system that is less error prone and more automated, you have to automate activity capture, you have to also emails and meetings, you have to automatically capture whether you multi-thread it. So if people are being added to email threads or calendar events, you have to automatically capture those. You have to make sure that those actually are mapped to the right opportunities, which is a lot more challenging than it sounds. And then you have to make sure that you record as many conversations as possible, including all the conversations that are happening in the field on conferences. And you need to then basically tie back those conversations where you have a lot of insight for data points to essentially fill in your qualification criteria, right? Like situation, pie in identified, critical event, in a decision criteria, decision process, champions, and so on. These are all there in the conversations. So you can basically use that data. And that's really where AI shines, right? To have, you know, a serum autofill, to, you know, automatically maps, serum fields, to AI problems, so then update those either automated or with a human in the loop, right? Like that really depends. But that's obviously like table stakes these days. To then when a manager does a deal review, they can see how long is the deal being in the stage? What was the velocity of the last 30 days? What were the touch points? Has it been pushed multiple times? Like what is the real pain identification? How strong is that pain? Right? Like who's involved in the deals? Are we multi threaded? Like so these like typical questions that managers ask, they ideally need to be fully automated. And then you can start really comparing the different deals in the different deals and you can, you know, actually look at, you know, AI scoring and warnings. You can have AI summarization across all the different data. And so I think when I think of data foundation, you know, one of the biggest challenges to data foundations that the serum is supposed to be the system of truth, but more often than not, it's not. And so you have to basically unify the activity, contact, serum, conversation data into one layer that is credible and that you can use to basically extract those insights, whether these are in a, you know, like the pipeline view, right? Where you just go through the deals 101 or whether it's in AI shared based system that allows you to query all the data and, you know, ask, hey, which is a risk of flag deal risk and, you know, act against those. So I think that's all, you know, things that weren't possible three years ago. That's very much possible is basically, you know, something everyone uses in WeFlow these days and it's pretty much table stakes. And at the same time, we also meet a lot of companies that are not doing it that way just yet. So this is essentially, I'd say like the first, the data foundation leads into the deal intelligence and deal hygiene that is kind of the first layer of this forecasting motion because it's like, ideally, the forecast should be based on deals that are qualified and comparable and that then feeds your pipeline metrics and your forecast metrics, right? So, so yeah, I'd say those are like the foundations we typically see and work towards anything to add from your side. No, I think the comparable, I think really like it's a mark there. I think that's super, that's super critical. Right, that's also what I meant with like, I think the problem is like the problem we're talking about here is not like you have like a sales team of like two, three people or five people and then you align on it internally, right? Like what is like a good deal or not? I think there's a different motion. I think we're talking about like, you have like 50 reps working in different territories. You know, spread a cluster clope or even if they're sitting all in the same team, it's about giving them a consistent ramp experience and making them successful. I think and I think this is very critical. Like everything we talk about here, forecasting is just the end result. Like I've said this many times before, yeah, forecasting is just the end result, but I think it's really something super important to constantly think about because like, what it basically means is if you become very good at doing accurate forecasting, that means that you've become good as an organization at building a good team and helping them become good sellers with the product that you're trying to sell. That's the end result of an accurate forecast in a nutshell. So the forecasting is always just like this, you know, ideal stay and end state like that you want to work against, right? I think if you're like an 80% accurate forecast, great, right? Then try to work against like 85% accurate forecast, 90% and so on and so on and so on. 100% will always be tough. But I think if you become like 95 to 98% accurate, that means you're like working in a world class sales organization. Hey, Philip here. Are you enjoying this episode? Well, good news. Because you can find more free red ops and go to market resources on get me flow.com/redops access over 20 cheat sheets, reports and guides that will help you become a better revenue operator or join over 2000 subscribers who already get the latest resources right into the inboxes without a free newsletter. Just go to get me flow.com/redops that's so so important to mention right? Because the board is looking at you and forecasting is obviously not a number. It's a process and that process should also enable you to execute better. So there's the action ability, right? Like let's say you have full visibility on all the deal risks. That means you can take action. You can mitigate those risks. You can win more. So you can execute against your forecast better. So that has basically the action ability and then there's the predictability piece, right? Which is another process that is then lay out on top of the kind of deal reviews and deal intelligence, which is typically what we typically recommend is to break it out into multiple forecasts. So what we see lot happening in friction softwares, you have forecast for new logo business, your forecast for expansion business, and you have a forecast for your renewal business. And that then summarizes to your revenue forecast. You know, you look at there's obviously a separation between bookings forecast and consumption forecast, right? So that is very different. There's different challenges with consumption forecasting because you often have a bigger challenge in knowing the deal size. And you also don't know how it spread throughout the period, right? So if you buy workloads from snowflake, you don't know how fast you spend those within a year, right? Even if you have a clear number for the year. But like these are all separations, but typically you set up these multiple forecasts. And then you run a forecasting motion against those forecasts, which often combine multiple numbers to forecast the quarter or the month. And you know, what I am a big fan of is that combining three main metrics to keep it simple, a dynamically weighted forecast. So this is basically just a very simple model to look at last six months, last 12 months of your kind of stage conversion rates and just apply those and say, hey, you know, we have like three numbers last three months, at least last six months, last 12 months. And we just look at the number as an input factor. The second is a bottom up forecast, which is typically a roll up forecast. And we'll talk more about this in a second. And the third one is basically an AI prediction that is based on the data foundation, the deal foundation, which towards like reflow or clarity, they all have those right to essentially basically do AI prediction. And then it's not about like, okay, there's like one number, but it's like you have basically different corridors at the AI prediction and reflow. It doesn't, it's not one number is actually like mid-high and like base case forecast. So you have basically a corridor. Because the reality is like it is a corridor and a lot of things can happen, right? So I'd say, you know, like set up multiple forecast motions for the different right by new logo expansion renewal and this can be different in different businesses. I think about not just one methodology, but multiple forecast methodologies to get to the, to get to the, you know, different numbers and to get to the corridor. And then let's maybe talk a little bit more about roll-up forecast. I think that's a process that is often run in spreadsheets and it's really bad to do it in spreadsheets. But yeah, maybe we can jump into that. Or maybe you have something to add to what I just said for the panel. No, no, I think, I think let's move on. I think it's clear. I think roll-up is such an interesting topic because again, it requires rigor. Right. Like we sometimes have these conversations and this is something I shared before in other episodes. But we sometimes have these conversations with customers or prospects. But basically they want to move into a roll-up, but just in terms of like the current cadence, they're actually not ready for it. Because they're not like in a position where they could, you know, trust their reps to make these like objective estimates on their forecast calls. That being said, I think it's good to start with like a roll-up motion gradually early in the process of your of your company. Because that's like a good vehicle to basically train that muscle of getting to an accurate forecast. So the best companies that we work with the way that they currently do forecasting with a roll-up motion is they basically have something that they typically call the baseline forecast, which is what you could translate into like a worst case. And then they have like a best case forecast. Right. So sort of like the best case outcome in that in that given month quarter or whatever they are forecasting. So they have two forecast calls. And today basically let the reps forecast both of these numbers for each month. And they do it basically on a weekly basis. So every week all the reps and the company need to forecast on the current month and on the next upcoming two months on how, you know, what they think they will achieve based on the current pipeline that they have. And the results of that could be, you know, not that great, right? Like you could find out, OK, actually I don't have enough pipeline in like two months to actually reach my quota. Like that could be an outcome of that, right? Like you could have not enough pipeline based on your current coverage to actually reliably get to like a good forecasting number in the sense of like being close to your your quota target or your to your target that is defined. But that also is an important outcome. So the best companies do it on a weekly basis and they forecast for the current month in the next upcoming months. And they both let their reps make a forecast call and then they let their managers make an independent forecast call. The forecast call that is not just the roll up of the, you know, the reps that they that report into them, but a separate one where the managers also required to just make an independent estimate of what they think their team can achieve. And that can be lower, that can be higher than the, you know, some of the the roll up of the reps that report into them, right, but they need to, you know, give up their own number or give out their own number and add their own justification to it. And then you have all these numbers, right, next to each other, you see the number from reps, you see the number from the from the managers and then a good tool like reflow. Sorry, I have to have to have to have to watch that in here like a good tool like reflow, but then also let you select individual deals. So not just like a forecasting number, but also like not just like an amount, right, but also like, okay, I'm going to hand pick those 10 deals, you know, and those 10 deals are basically what that number is made up of. And then you have that number, you have those deals and then you have it on in one table view, basically, where you look at it next to the overall value that you currently have in your different forecast categories or for example, commits best case pipeline. Those are the most common ones that everyone is using. And and that in itself will already give you like a very good idea of where you're currently standing with your, with your like, you know, kind of like roll up overall. How likely it is that you're going to hit your targets, your quota and where the gap is. So this is the most basic roll up, I think we typically see. And one that already really enables you to have really good conversations. And then you can become like, you know, more crazy and you can then, you know, add more metrics to it. You know, okay, what is the average deal size? What is the historical win rate of that wrap? How many deals are actually included in here? You can add like a deal score to each individual deal in there. So there's a bunch of different things that you can add in there that just help you quickly evaluate from a leadership perspective that is maybe a bit more detached. You know, from the day to day operations. So they, when they look at that roll up also quickly understand, hey, is this realistic or rather unrealistic. Yeah. Yeah. Maybe just a few things to add here. Like so for everyone who doesn't know what a roll up is, it's basically that you have basically like values are being rolled up throughout the hierarchy. So you can set up your own hierarchy. So VPE has five AEs reports in the zero VP West and international. And so you see basically all these roll up numbers. If the base case, best case. And then typically what you also put in there is the the goals, right? So the targets, which could be the quarter goals of the reps could be manager goals. Sometimes the reps should not see the manager goal because it's lower than the you know, roll up values of the quarter targets. You can look at you know, you can also add the budget number into this. So you can compare basically, okay, this is my base case, best case. This is the manager view. These are the deals that are included in driving this. This is basically our goals. And this is the gap to the goals, gap to quarter, gap to manager tie, gap to budget. Then you typically look at what is already closed one. What is in open pipeline. This could be forecast categories. This could be stages. And then you look at the pipeline coverage. Because then you really have a good tool to get a very quick understanding of what's going on. And you will see that you know, there's specific, you know, teams that have a too low pipeline coverage. There's specific teams that have really good pipeline coverage. And then you can add more like these analytic metrics. So for example, you know, kind of deal size. And then you might see, oh, pipeline coverage is 1.5. Deal sizes, you know, only 30 K and all the other teams have by 2.5. And the deal size actually, you know, 60 K, right. And so it's a very good tool to quickly understand what's going on. And then, you know, all these numbers basically reconcil into what we call like snapshot pacing or pulse, right. Like basically views that you can then look at those numbers together. And you see the pipeline progression of what time and it predicts out with the AI prediction, the team forecast, manager forecast, the way the forecast being all in one. So you basically see a key, what are the differences and what you want to report back to the to the port. But like going back to that, right. Like what we see a lot is like this being done in a spreadsheet. And so what you then misses three main things. Number one, typically reps don't go in a spreadsheet and you know, report their numbers. That's almost never happening. So it's only only a manager forecast. The second you cannot do it deal by deals forecast call because it's very, very complicated. Yeah. If you have like 50 reps, you know, each reps has like 10 to 50 deals a quarter, right. Like it's it's just to complicated. And then the third one is you can also use it in tours like we floor like others, right. Like you can basically just inspect those deals right from any pipeline analytics or roll up view. So you can basically dive into that and you can see which deals are slipping or stalling, whereas risk, right. So that is often something that is very hard to see when you're in a spreadsheet. Because a lot of context is missing. So basically what you do in a roll up is you basically click on OK, these are deals are driving these deals at risk. You dive into those and then you see OK, these are basically the risk deals and these this is what the is summarizes. And then you can take action again. Right. So it's basically closes the loop of like the data foundation, the deal foundation to the forecast foundation, which is I think really, really important. Yes, 100% 100% yeah, if you haven't if you haven't seen a roll up just go into just going to Google search or just ask plots to mock up a roll up for you. Maybe yeah, that that would also that would also think I think we also have some cheat sheets and documentation on that 100% yeah, I think the other like super interesting topic. I mean, there's like the AI like sort of like projection forecast, basically is like hyped up machine learning model, right. That just takes like your seasonality, all you're existing deal data. And and then and then basically makes a projection to the future where I would say like the main question here is just how does it work, right. Like does it just look at the aggregate does it look at each individual deal. Does it score each deal the foil takes it into account, right, the good model models to that the bad models just look at the aggregate. So that's that's like a question you should all always ask when you are in a conversation about like a tool like that. But that's just like in most cases, you know, like a black box machine learning model. And then the more the other like really interesting one I find that also is like very tangible and something that is always worth looking at is the dynamically weighted forecast that you mentioned earlier where basically you look at the probability rates attached to the different stages that you have for the deals in your pipeline. And like in tools like Salesforce, right, like you attach like a hard code at probability to each stage. I mean, you can change it, but it's not going to dynamically kind of change. So what you would have to do with a tool like Salesforce is you would have to go in on a regular basis and you would have to recalculate like the probability rates for the different stages. You would have to repeat that process every couple of months, at least like once a quarter. But then you would still have the same probability across all of the stages or like like follow the opportunities. And I think where it then becomes a lot more interesting is if you do that on a rap by rap basis or at least on the team by team basis, right? It always depends sort of like on then like how many deals do you have? Obviously, if you have like a low velocity like sales motion, each rap has like like 10 deals. That's very hard to do to get a reliable number on a rap by rap basis, but at least you could do it on team basis. If you have enough people in there. And then it becomes a lot more interesting, right? So because then you see, okay, like I have like a rap like let's say after three months, like the probability for the different stages is you know, 20% in the beginning and then like for more ramped up rap that has like 10 like two years under the hood, selling that product. Maybe it's like 40% or 30%. Right? Like in looking at that, on that granular level can be really valuable. If you're not able to do that, then at least it's worth like defining like a cutoff point. We're saying, hey, I'm going to look at like the probability rates for reps, you know, who have been with us less than six months. And I'm going to look at those who have been with us more than six months or like 12 months. So whatever makes sense for you, right? I think that's sort of like a like a deep dive that you have to do. It very much depends on your sales motion, the size of the organization, etc. But this is also great point. Yeah. And but this is also where a tool can help you, right? And again, when I plug beef low, but like, and this is also where a tool can help you. And just to just to just to automate these calculations because that's super tedious and time consuming. So that's something where I would say like a tool can be really helpful. And yeah, just automate that for you. That's what a good tool should be doing. I mean, you obviously need these like kind of opportunity snapshot data, right? If you want to do it yourself, it's very crucial. I think most of you are probably familiar with that concept. And then, you know, I think you're actually stressing a really good point. I think there's like a variety of folks we met in the past that, you know, have not just the dynamically weighted and the AI forecast and the roll-up forecast, but they actually create their own reflux model. And then they basically, you know, run that against it. And I think in the end, it doesn't like what you should use as essentially a combination, but you should also learn about time. What's the best model? How can you tweak it further, right? Like to become more accurate. And obviously, there's a lot of dimensions, right? Like the segment market, like SMB market enterprise has huge differences. The runtime of the team has huge differences. The Geos have huge differences. Obviously, the motion you lower expansion, you will have huge differences in conversion rates. You need to take those into account. But like whatever you're going to do, right? And I think maybe just one comment on this like AI prediction topic, right? You know, like the best model is obviously they are based on the data foundation, right? So like if you don't have a, you know, if you don't automate that process of like good data quality on an opportunity basis, like it will be very, very hard to have like accurate like prediction models. And this is something that we run into. We actually launched the prediction forecast before we had automated Salesforce data capture and all the cables we have today. And we found that it wasn't that accurate. So it's often something that like made us realize, okay, you actually have to solve it and to end, right? Like you need to go from automated data capture to the roll up forecasting capabilities where flexibility and adjustment to your processes is crucial and it's very, very important that you can adhere to to what you want to do. But it's essentially all one, you know, overarching operating cadence that you run and that every business is running. And then the question is, how good is that and how good is the quality of all these different sub processes you're running? And that's essentially what we wanted to like, you know, spark here today. So with that said, for what's the book you would recommend? Oh no. Oh no. Okay. I did try the next course. Okay, damn. But putting me, putting me in the hot seat. No, no, no. Look, I just posted this on LinkedIn actually like nine books that I would recommend. So just follow me on LinkedIn and then you will see my recommendation. That's what I'm going to do now. That's what that's a great, that's a great answer actually. Okay. Good. All right. Thank you. This has been great. And hopefully it's useful for you as well as mentioned, right? Like at gbflo.com/revox. We have a lot of resources that help you become better at building out cadences, defining different steel signals, defining your forecasting motion. We put a lot of effort into it and we continuously bullet out. So definitely take a look at that. And with that said, thank you for listening. And yeah, see you all soon. Thank you. See you soon. Thank you so much. Thank you for listening to the Reveils Lab podcast. If you enjoyed this episode and would like to support us, share it with a Reveils friend or try us five star rating right now. And if you have feedback, questions or guest ideas, just send a message to Janice or me on LinkedIn. Thank you and see you next time.