Stop best-guess forecasting with John Lorenc, Ph.D.
Devin Reed: Welcome to the show. You are now part of Reveal: The Revenue Intelligence podcast powered by Gong. We're your hosts, Devin Reed.
Sheena Badani: And I'm Sheena Badani. Revenue intelligence is a new way of operating based on customer reality, instead of opinions. It's an unfiltered view of your customer reality. In other words, making data- driven decisions based on facts, instead of opinions. Or guess words.
Devin Reed: And it's made up of three success pillars: people success, deal success and strategy success. You know, the things all revenue teams need and care about. Every week we interview senior revenue professionals and they share their stories and insights on how they leverage revenue intelligence to drive success and win their market.
Sheena Badani: You'll hear how modern go- to- market teams win as a team, close revenue with critical deal insight and execute their strategic initiatives, plus all the challenges that come along with it. So Devin, you've been part of a lot of different sales teams, and I think there's a lot of aspects of sales that are tough, but I think forecasting is one of the most where it's like, you can measure this. You know if you're on, if you're off, by how much, and some teams are great at it and others just aren't. So I'm curious, based on your experiences, where have you been where you maybe had a great team that supported the sales org in terms of forecasting?
Devin Reed: This answer is probably not the best. I actually know it's not the best answer, but my perception of forecasting as a rep was this is mainly something for leadership, as long as I get to quota... I'm going to be as accurate as I can, but at the end of the day, it's a nice to have metric. I know now and I even kind of knew then, that's not the best mindset, but when you have a quarterly quota, the quota is what you're striving for. Of course, you want to be as accurate as possible because you also want to know how much money you're going to make, you want to know how many deals you're going to close. Like, if I have three deals, I'll easily get there. I just need one to close, or is my back against the wall, I need all three and maybe a little bit more? So that was kind of it. I honestly couldn't even tell you what my forecast accuracy was aside from 100%. Just kidding. No, I don't even, I honestly don't even know what it was. I know more so how often I missed quota or made quota, which is the main thing. I would say though, is there's definitely some people that were really good at it, and I think there's many of the other people who were like, they didn't even really bother updating CRM. But what was funny is most of them, I feel like, I feel like the people who didn't, like almost" didn't bother", were usually ones who hit quota. They just spent time in deals, on the calls. They knew if they just stacked up pipeline and the deals would come and that they would hit quota. The challenge with that, which we'll talk about today with John is it makes planning almost impossible, because when you're a sales leader and you don't know, hey, is Randy going to come in at 100% or 180%? That makes it really challenging.
Sheena Badani: Right. And you're relying on the rep. The rep is the one who's the closest to the deal. And if they're not thinking critically about what's in their pipeline and understanding where things are going to land up, how is anybody above them going to figure it out even better than them? So it's really tough.
Devin Reed: There was no revenue intelligence. It was literally what rep said, you plugged in, and then you have your stages. Is this most likely, is this commit, is this best case? And you put a percentage and then it's the deal value you think, but at the end of the day, every rep for the most part under promised and over delivered, because that was safer than saying," Hey, I'm going to hit 80%." And then you come in at 85%, that makes those weeks leading up to the end of a quarter a little rough for you. So I can't pretend to be very accurate. I'll just say I was decent, but I was very excited to talk to John because we had the prep call. We're talking about," Hey, what should we talk about?" I was a sales ops leader. I wasn't surprised he said forecast accuracy. I actually asked him to repeat it. His current forecast accuracy is 97%. There is a 3% variance across their entire sales organization. And then immediately I started asking'how', but I had to stop myself. I was like, well, that's our topic and we're going to get into'how' on the actual interview, which is what we did today.
Sheena Badani: So we start off this conversation with learning about John and his academic background in history. And I was kind of thinking, he could actually write another history book on the early days of forecasting. It is going to be ancient history one day soon of what people are doing with spreadsheets and manually. And it's going to be gone for good.
Devin Reed: The Old Testament of forecasting will start with, I don't know, a notepad or spreadsheets, one of the two. It depends on how far back we go. If you are a revenue leader, or an aspiring revenue leader, you're going to want to listen to this because it is a truly in- depth breakdown of how to become more accurate. And what I really liked about this is there's kind of three stages that John takes us through, which is the early days, which is just a VP doing it. Phase two, which is relying on reps and having a formula and a more programmatic approach. And then three, to where he's at today. And I won't spoil the'how' there. I'll let you unpack that because that's where all the gold is today. So if you're interested in forecasting it more accurately, this is 100% to you, and if you're like me and you're like," I don't know, as long as I hit quota." I promise it's worthwhile, especially if you want to get into leadership. So with that, let's go hang out with our new friend, John. John, I appreciate you hanging out with us. Welcome to Reveal. I'm really excited to talk about today's topic because it's forecasting. Some people love forecasting, some try to avoid forecasting or have their own unique under promise over deliver strategy. But today we're going to talk about some secrets you have around forecasting with exceptional accuracy, but they won't be secrets for long. Before we get into all of that though, you're the VP of sales operations at PointClickCare, but you're also a PhD. What is your PhD in and how does that relate to the work that you do today?
John: I might be setting you up for disappointment because there's absolutely no connection between what I studied in school and what I do now. Some things crossed over in terms of skills, but in terms of subject matter, absolutely no connection. So what I studied in school, I did a PhD on medieval economic history. So what I was studying are the laws around lending money in the Middle Ages. So in the Middle Ages, there was a religious prohibition against lending money at interest. As you know, no one will lend money unless they lend money at interest, so the book I wrote was basically how people try to get around this law. So what I had done is I edited a confessor's manual, which was intended to instruct priests how to elicit confessions from people who actually did this, lent money at interest, and tried to detect it. Kind of a little bit interesting economic sort of historical thing mixed in with a little bit of religion. Like I say, in terms of the subject matter, could not be farther away from what I do today.
Devin Reed: Okay. Well, I'm curious what was the most common, or maybe the most interesting way that you learned people would try to swerve around the no interest law?
John: There were a lot of ways. One of them was in the dowry, so they would kind of bake an interest based loan into the dowry when you got married. Another one was they did speculation on futures for grain. So they would do loans that way, where they would give you money up front when you were about to plant the crops, and then they would collect more money when you actually harvested them. So they would kind of hide those usurious loans that way, but there's a million different ways they did it. They're all interesting, but I don't want to use up the whole interview talking about medieval history, because trust me, I could.
Sheena Badani: We could do an entire episode on that. We really could.
Devin Reed: We, we, we definitely could. It's good to know people have been essentially evading taxes or banking rules for centuries, not just today. How would you take some of those skills? Surely you have some analytical skills from that experience. How has that parlayed over to the world of sales and sales ops?
John: So when I was doing all of that work, it was a lot of really heavy duty analytical work and, especially when you, when you edit a manuscript, you have to learn the discipline of paleography and the discipline of paleography involves something called collation. So you're trying to go from versions of the manuscript that exist today to reconstruct how it was originally written, because in the Middle Ages, everything was written and copied by hand. So you can imagine, in the modern world where everything can be typed and copied and pasted, how many times do you get broken telephone? You still get quite a bit of broken telephone. Now imagine where there's no emails and everyone is literally writing everything by hand. Imagine how much more broken telephone you get. So you have to reconstruct it in original text. It's comparing so many things left to right to find out, okay, what are the differences, what are the similarities and how do I get back to some original insights from that? That was what I think really got me started as an analyst, so the beginning of an operations career as an analyst is being able to pour through thousands of rows of mind numbingly, boring data to find that one little discrepancy, or that one little insight that you can really extract from all that. And it's a patience thing. It's an attention to detail kind of thing. It's an endurance thing, being able to just keep going and going and going and find those things. And I think, really, that's one of the things I took away from it, as well as an interest and curiosity to sort through problems like that. I think in operations, you have a lot of problems that other people find boring, or maybe smash your head against the wall trying to sort them out. You have to have that mentality saying, I want to go find the answer in this massive chaotic data.
Devin Reed: Well, you said endurance, which sales is definitely an endurance sport. You said attention to detail, which definitely links us into our topic for today, which is forecasting. So forecasting is something that I will say, every sales team I've been on has either been bad to okay at. And that's not a knock on the team. I think it's something that's just genuinely very hard to get right. Why do you think that is?
John: You can draw an analogy to the SDR function. So why was the SDR function created? I'm talking about sales developer representatives. I'm sure everyone who listens to this probably knows the history of it, but I'll recap it in five seconds in case they don't. So originally, account executives did all their work. They prospected, they got their own clients and they tried to convert them and close them. Then a genius who worked at Salesforce, this guy named Aaron Ross, he comes by and he says," They're not as effective as they could be. They really suck at finding opportunities." Account executives are closers. They love to work deals to completion. They're not good at finding the deals. So he says," Hey, guess what? We're going to create a team of people that all they do is prospect. And they're going to prospect and then handoff opportunities to account executives to advance and close. So he did that because account executives are not built by nature to do that well. And I think you can apply the same thing to forecasting, so sales reps and sales directors are not built by nature to do that exercise well. That's the part of the job that most of them like the least. They want to be working deals. They want to be advancing opportunities. They want to be generating new opportunities. That's where they want to spend all day, every day. And if we could leave them to do that all day, every day, God bless them and they would be all successful. But the forecast, unfortunately, has to be done one way or another. So that's why I think, honestly, they struggle with it is because they don't want to invest time in it. And it's not a knock against them. It's like asking someone who's an expert at playing football to go be a ballerina. It's just not something they're built to do. So I think that's why they struggle with it quite, quite often.
Sheena Badani: And now you've been at PointClickCare for, I think over three years. Paint a little bit of a picture of what did forecasting look like when you first joined the organization.
John: So when I joined PointClickCare, they really didn't have a sales operations function, so sales operations was part of business operations and they kind of served the entire company. So what they found was sales was getting short trip. And the forecast... We had a senior vice president of sales at the time who really took on a ton on his own back and too much, frankly, on his own back. He was handling the whole forecasting exercise on his own. So he was literally spending hours on hours every month, preparing the forecast for every single sales director, for every single sales rep. This was kind of crazy. So we had to go from that to something better than that, and I think that's a key theme I have definitely noticed in my career is when operations is successful, yes, as much as we want to get to a crystal palace in the end where everything is perfect, it takes so long to get there. That really, success is how did we make it a little bit better than yesterday, but we changed it like the next day. So first step from there was going to, listen, SVP, you shouldn't have this all on your back. Let's go to a standard, at least a standard bottoms up forecast methodology, where the sales reps forecasted the directors, the directors forecasted their VPs and the VPs forecasted the SVP, and that way you're not having to manage a forecast, well, let's say 60 plus field reps. You're having to manage a forecast for three VPs that report to you. And that was really the first step is just getting a very basic bottoms up weekly, monthly, quarterly forecast cadence going. That was really the start of all of it at PointClickCare.
Devin Reed: I like the first... To me, this sounds like phase one. I have a feeling more phases are coming and it reminds me of something you mentioned earlier, like I'm going through handwritten transcripts, one by one, playing the game of telephone. You kind of just described a game of telephone. Rep to manager, manager to director, and I know things sometimes get lost in translation. After you finished phase one, what were some of the challenges that sprouted out from that and how did you go to build phase two?
John: When you're working with a bottoms up human- generated forecast from people, I think... And again, it's not a knock on people, it's just a natural inclination. So one thing you'll hear a lot of the in sales, which is a good saying, but also can contribute to bad behaviors in forecasting is," You never forecast less than the quota." So if you're a sales director and you're forecasting less than the quota, it's a bad mark. However, there are times, legitimately, when you're going to be under the quota. And I think that's what we saw a lot when we were doing the bottoms up human- derived forecast, is people were massaging the numbers to get, if not to the quota or past the quota, at least as close to it as possible. And there wasn't a lot of oversight on that time and the underlying data behind it. There wasn't a lot of the ability to check it at the very beginning, because the operations function was so inaudible. So you rely on a lot of people's subjective call of the sales forecast and that subjective call includes their... We used to call it the aspirational forecast, which is not supported by data. It's like," I'm going to get to the quota." And then," How are you going to do it?"" Well, the deals don't exist today, but I'm going to find them." If the forecast is off it, a lot of the stuff downstream doesn't work, and that's what we were finding is we didn't have accurate data to do future planning. So I think that's what we identified. Okay, we have a need to move beyond this human- based bottoms up forecast process. And again, nothing against the people that do it. It's just, again, you're asking people to do something that really, maybe it's not the best thing to ask them to do.
Devin Reed: I've been there. I mean, it's hard if your forecast says 88%, maybe just nudge it up to 98%, maybe some good fortune happens. Maybe a deal gets a little bit larger, but it's kind of natural. Like, of course you're going to want to project a little bit more and it also makes things smoother for you.
Sheena Badani: The thing that I was thinking about is you are building this forecast on this foundation, which as we talked about is based on manual processes and their own subjectivity. What kind of guidance did you provide the reps, in terms of them creating their own forecast bottoms up? Because everything gets built on top of that now.
John: Yeah, so I think once we... It's always an iterative process, so we introduce the bottoms up forecast process. We start looking at forecast accuracy. Forecast accuracy is a little bit all over the place because of that human element. So the first from there then was to get them focused on the metrics that should be driving their forecast. So for example, obviously your forecast should be pipeline- derived. If you don't have pipeline to support your forecast, why are you forecasting that you're going to post$10, 000 in recurring revenue when you have$ 8, 000 worth of pipeline? Even if you had$ 10,000 worth of pipeline, I wouldn't be forecasting$ 10,000 worth of bookings because you don't win every single deal. So we're starting to get them focused on those key metrics. And as much as we like to do fancy things with the data, I found in my career, constantly going back to the basics is really effective. So talking to reps about their win rate. So for example, win rate and pipeline multiple, those are two basic, really basic concepts. So if you go to a rep and you say," You have a 50% win rate," which is industry standards, it's actually pretty good. That means you need at least 2X pipeline. You need at least$ 2 for every$1 worth of bookings to come in. And then that clicks for them and they start saying," Okay, that's what I can forecast. So if I have only one to one coverage, I'm not going to meet my quota, I'm going to be at half of my quota." And then they start to gut check it themselves and, obviously, we're gut checking it for them in operations when the forecast starts coming to and you get some improvements to forecast accuracy there as well. I think where you start then hitting the limitation is there's only so much time and interest that a rep has to go deep into their pipeline metrics, their historical deal metrics, to produce a really accurate forecast. And to be honest with you, if they mess up their forecast on the positive side, where they crush the number they gave to us, it doesn't help us with planning, but we're less merciless to punish them, because you're doing a good job. You're just terrible at forecasting because you don't like it and don't spend all your time doing it.
Devin Reed: It there's that point in a rep's head where I have the time I'm now spending on the forecast, I could just spend generating pipeline or making that forecast better, and so it is hard to maintain that. I want to go back for a second John, and tell me, maybe you have the number, maybe you don't. So it's understandable if you don't, but we started this with SVP doing everything manually. Do you know about, even if it's a bracket, about what the accuracy was, like 5%, 10%, 20% accurate?
John: Because it was one person handling so much data, it was kind of like a plus or minus 20% swing. There's only so much one person can do with that much data.
Devin Reed: Honestly, I'm pretty impressed it was only 20%. If it was me, it would've been 50%. I don't know, flip a coin. So now we went to bottom up, so now we're talking about manual entry, reps are doing what you just described. Do you remember what... How did the variance increase or decrease from that point?
John: So there wasn't actually, to be honest with you, a tremendous improvement there. I think he actually, he really took a lot of time preparing the forecast. He took it very seriously. So he took it as far as one person can take it, so we saw maybe some incremental improvement, maybe about 5%. So it was, you're still kind of off by 15% on either side.
Sheena Badani: Creating a forecast can feel a bit like a loss scale among all the other activities that sales teams are focused on today. Yet it leads to success in your role and for the business. Stats we found were shocking when it comes to the pain companies experienced related to accurate forecasting. According to Miller Heiman Group, fewer than 20% of sales orgs have forecasting accuracy of 75% or greater. Another stat from Gartner is that less than 50% of sales leaders and sellers have high confidence in their organization's forecasting accuracy, yet sales teams that leverage a formal and a structured forecasting review process increase their win rates by 25% versus those that take a less formal approach. Thanks again to Miller Heiman for that stat. With that backdrop, let's get back to the conversation with John and I'd suggest grabbing a pen and some paper for this next bit, as he shares quite a bit about what forecasting looks like today for PointClickCare.
John: So we still run bottoms up, because people who have good ideas, they deserve to have their good ideas replicated. So a really good idea that they use at Salesforce is they have a machine- based forecast that constantly gut checks the bottoms up forecast. They always run them in parallel. And the reason it's important to run them in parallel is because, especially at the sales director and VP level, the accuracy of your bottoms up forecast reflects your understanding of the business. So we run both and the leaders have access to both sources of information. So when you're talking about what's the process like now, we still, on a monthly basis only, pull in a bottoms up forecast to get all those intangible aspects of the forecast, things changes in month, things that the computer doesn't catch because they're happening in the moment. But in addition to that, what we layer on is a machine based forecast. So at the beginning of every month, we project the forecast for the next 90 days. So what does that mean? There's a pretty extensive business intelligence framework of reports I've developed that can extract pipeline and process it, as well as project pipeline generation from the past into the future. So on a very basic level to inaudible forecast, you're going to start with what you know. So what I know is the existing pipeline. So you extract the existing pipeline, we slice it on a number of metrics, like our forecast is pretty crazy detailed, which is another reason why it was very difficult for people to do, because you're slicing it by market, by geography, by size segment within that geography. And they're asking, for example, one of the products we sell is practitioner engagement. So how much practitioner engagement will you sell in Tennessee to skilled nursing facilities in the next 90 days? That's the question that has to be answered. You can imagine for a person that gets pretty difficult to get down to that level of granularity. So another issue we had is maybe their forecast starts getting close to accurate on the aggregate, but their allocation of it to geography market and size segment, they're guessing. There's just no way a person can do this. I remember... I won't name the person, but the VP I was working with just said," We're just throwing darts at the board." That's it, right? That's all he said and that's the most he could do. So when you're working with the actual historical data and the pipeline data, all those dimensions exist. So in the open pipeline, they're there already, I extract them. You extract the open pipeline, you weight it, so we are waiting it according to stage and age. So that gives you kind of a percentage conversion of that open pipeline. The other side of it is you take how much pipeline is going to be created in that period that doesn't exist today. And of that pipeline, how much of it will convert to bookings within that 90 day period? So you can use your historical background. We vary the range. It depends. If big events happen, if we have a sales restructure, I change the historical range. If the market shifts, I change the historical range. For example, if we see different sales drivers coming into play. We had a large surge of new business at the beginning of the year, that started to peter off towards the end of the year a and then the wallet share business was increasing. So you pick your range based on... that element has some human judgment, so a human being looking at the data, but you pick your historical range, extract it, that will tell you how much pipeline will be generated in the future based on what's been done in the past. You take your deal metrics from the past, your average length of sale and your win rate for every product, slice down all those dimensions. So I can tell you, what is the win rate for practitioner engagement in Arizona for skilled nursing for two to five facilities. We have enough data that I can actually tell you that. You take that win rate. You apply it to the pipeline generation, so basically, you take the create date, you add the average length of sell, you take the MRR projected, you multiply it by the win rate, that gets you booking at a specific period in time. Add that together with your open pipeline, that produces your forecast. So machine- based forecast. That doesn't have that level of subjectivity. It doesn't have that temptation to say," I'm going to call the quota." It says," No, this is reality." So this is based on what you already have sitting out in the pipeline, plus what you are likely to create and convert within that time period. Add that all up and you get your forecast.
Devin Reed: If you're listening to this and you don't have a pen, pause, go find a pen and go back about two or three minutes and jot down those notes, because I feel like John, you just gave a crash course and exactly all the things to be looking for. Because what I just learned also, amongst many things, is it's not just about the, where are we going to end the numb right now? But it's all of the data you just described will help you be a better strategic planner, so you know how many people to, if you're raising quotas or changing quotas, how and why? Head count, joint, all those things. You kind of take forecasting away from just the'Where will we land on our number' and really elevated it to the next level.
John: I mean, the whole reason we do this is to enable planning, to enable strategic or tactical pivots. Strategic being long term, tactical being short term. So for example, with COVID, we were very quickly able to isolate which products are working well in the market and which products are not working well and shift our plans to focus on, we actually create a new category form called digital doorway products, so products that mean that a physician doesn't have to actually go into a skilled nursing facility to deliver care. Obviously those things intuitively you think, okay, those would be attractive, but we had data to prove that those were actually starting to sell much more frequently than the others. We took that data and then we say, let's apply focus to those products. So they were naturally selling better than other products. Now, let's apply focus to those products and create actual campaigns and structured programs around that. As a result, we were able to get significantly larger bookings from those digital doorway solutions. And those were a key contributor to our success this year. So it enables insights like that. It also, on a very simple level, anyone who works in sales know you have an army. You're trying to deploy your troops where it makes sense to deploy them. If you see, for example, gaps in a certain region and surpluses and in region, you know that your army can be deployed to, ultimately, achieve the goal. You're not worried that a gap here doesn't have a surplus over there to cover it. But if you see gaps all around now, you know where to go to fill those gaps. If there's a gap, for example, in enterprise sales, are we seeing any goodness in, for example, the Western region sales that we can accelerate to close that gap? And it just enables that kind of decision making, so you can see a little bit into the future and make decisions about it.
Devin Reed: I got to ask the question because I asked for percentage for phase one, 20% variance. Phase two was 15%. You just walked us through human- in- the- loop, machine- based as well. What variance are you at, at that point, or if that's today, what's that forecasting accuracy?
John: So we have a symbol for it. 3%.
Devin Reed: Wow. For folks on not watching the video, 3% variance. Well, now I feel dumb because my next question was going to be what's in the future, like if you had... the magic wand question, what would you solve now? But now it's almost like, do you even bother trying to get closer than, than 3%, because I feel like that's pretty much the gold standard, if not, whatever's above that, the platinum standard.
John: So when I say 3%, the metric I use is the standard, so that's a quarterly forecast accuracy. So on day one of the quarter, we predicted how that quarter would end with the 97% accuracy. I think the next step that would be really helpful to us is to be able to drill down into how each month within the quarter plays out. That, we are still not as close on, so we're about 88%. So 12% off on how each individual month ends up. That's when we're predicting each month's close, 90 days before that month happens. That's how we measure that, so 90 days before, like if we're talking about we're sitting in January, we're trying to predict how April's going to finish there. There is still room for improvement. So on the aggregate in the quarter, we're very strong and you're right, that we are within the range on best in class. It's just getting down to that monthly. It's also going down to really slice and deep, how did we hit each product? How did we hit each geography? We're not there yet. We haven't done that level of onion peeling yet to see if every single metric we have is actually being met with really high forecast accuracy. We just know the aggregate number's being delivered accurately.
Sheena Badani: I think a lot of the folks who are listening are going to be in various phases of their journey along that forecasting route, where some are more manual and some are trying to automate and maybe some are close to where you are today. What recommendations would you have for folks who are thinking about automating their forecasting and getting to that more precise, accurate number?
John: So what I would recommend is don't roll it yourself. I had to roll it myself, because I wasn't able to secure budget to invest with a partner. The reason I say that is because it's very difficult and I'm not patting myself in the back. I'm just saying it takes up a ton of your time, as someone who leads operations, it takes up a ton of your time working with your operations analyst, that you could probably better deploy in other ways. It's like when I'm talking with my boss, he says," Is it taking up too much of your time?" I say,"No, I'm just working way too long." Because the time you take to spend doing this during the day, you've got to make up with other things in the evenings and the weekends, sometimes like that to make sure you're back on track with all the other key priorities. So secure budget for it. It's just something new, just the quirks of working. I did a business case to prove out the ROI, competing priorities in the business, budget goes somewhere else. Make the case and keep making the case to in technology because other people have solved this problem. There are vendors out there who have revenue operations platforms that do something similar. So if you can avoid rolling your own, try to avoid rolling your own. If you can't avoid rolling your own, work with a data scientist. I'm not a data scientist, I'm an amateur data experimenter. I don't know what you would call that. Work with a data scientist who can do this in a much more sustainable way. So you can contract with a data scientists, whatever, if it's a cheaper buy than investing in the revenue operations platform. But really, although my forecast accuracy is very good, the process to get there, it still requires a lot of maintenance of the code, running it each month, et cetera. The process doesn't take very long. It takes a half hour, but it's a half hour every month where I would wish I would have a revenue operations platform, I could just click every single day, what does the next 90 days look like? That's something I can't do today. It's still a cut in time. So I'm happy with what we've accomplished, but definitely investing in technology would be my next move and I would suggest that be the first move for someone looking at this.
Sheena Badani: It's always great to get that feedback and recommendations from someone who's been through this entire process. It's almost like guidance you're giving yourself a couple years into it, like what do you wish you knew back then? So that's great recommendations there.
Devin Reed: So John, what final advice would you have for our revenue leaders who are listening?
John: I think the only thing I would add is for anyone who's an operations leader or for working in operations, never work on five- year plans. I think that's what a lot of people get killed on is they're trying to do something truly amazing that's going to take five years. Yes, you have to have your five- year plan. You have to have this is what excellent looks like and that's what I'm working towards. Every day, try to make an improvement, even if you have to hack in that improvement in the short- term, make sure you're constantly delivering some kind of incremental value to the business. Because I know a lot of colleagues work in operations, they start getting in trouble because just the length of time it's taking for them to deliver the value. It's stretching out a bit too long for the sales leaders who want to consume that value. So whatever you can do to return time back to your sales leaders, back to your sales reps, with the exception of probably rolling completely from scratch your own forecast model. Invest in technology, if you can. But even investing technology, that's a shorter journey. That's a shorter journey than saying," I'm going to get a whole data science department together and we're going to have our own model that's just as good as what a revenue operations platform does." Try to find those quick wins. Try to find ways to make the process better as quickly as possible, while always working towards that ultimate goal.
Devin Reed: John, you have made it to the final round. It's not going to be as hard as writing that book on medieval... I want to say tax evasion, I know that's quite what it was, but that's where my brain went. But we have the same question we ask all of our guests, which is how would you describe sales in one word?
Devin Reed: I would agree.
Sheena Badani: That is true. That is very true.
Devin Reed: Especially as you talk through all the phases, phases of forecasting and all the things that went into it, but John, I want to you. I feel like this was a great crash course in forecasting accuracy and I truly enjoyed hearing your story and the phasing that you went through, because I know everyone listening surely identifies with one of those phases, probably one of the first two. So I can only imagine how many notes our listeners wrote down. So want to thank you again for sharing your time and expertise with us on Reveal.
John: Thank you for having me.
Sheena Badani: Every week we bring you a micro action, something to think about or an action you can put into play today. John basically gave us a crash course in forecasting accuracy. One thing to remember as you start to put this conversation into action is that building the ideal forecasting process takes time and it's a journey. So answer these questions in order to better define where you are today and where you want to go in your own journey. What aspects of forecasting are working for you and the team? Next, where can you be doing better? And finally, what will it take to get to your destination? These could be changes in processes, people or technology. And remember, you're not alone on this journey. You have peers, vendors and thought leaders who have all been down this path before.
Devin Reed: Did you like today's episode? Subscribe now so next week's episode will be waiting for you on Monday.
Sheena Badani: And if you really like the podcasts, please leave a review. Five- star reviews go a long way to help get the word out there.
Devin Reed: And if you're not ready to give a five, check out another episode and see if we've won you over by then.
Accurate forecasts are core to running a successful business. Yet more often than not, forecasting is rarely that... accurate.
When John Lorenc, Vice President of Sales Operations, joined Point Click Care they didn’t really have a sales operations function. Today, the company operates a data-fueled forecasting approach putting them within 3% of accuracy. Hear how they said good-bye to a “best-guess” model and get a crash course on how to set up a sales operations function that has massive impacts on the health of your organization.