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[Music] did you know that many companies forecast by multiplying the amount of reps they have times the average forecast and the product of those gives an indication of what the year's performance will be yes yes we've seen companies anywhere from 5 million to like hundreds of millions in revenue follow that exam exact same approach and it works in a way but it often leads to some vps of sales or some cro losing their job because of it inaccuracy it doesn't do well for forecasting one two three years out and in many cases we need to know because nowadays it takes three months to just get the wreck going to all start onboarding to start to recruit people it takes three months to recruit and i may say three if you have dozens and dozens people to hit it may take you a year to recruit on top of that it takes three months to onboard six months to get them up these are long-term cycles so if we need to forecast and we need to forecast on this capacity-based forecasting is going to give us all kinds of challenges so instead what we're going to talk today about is growth rate based forecasting and in growth rate we're taking historic performance not just off the sales organization but of the entire organization into account that is called growth based forecasting and i've written a paper on that well who am i my name is jacob van koy i'm the founder of winning by design and in winning by design we combine our passion for sales with that of data and scientific principles mathematics and so on and today i'm going to take you through that paper you can download this paper that you see down here you can download that from my linkedin profile and it's located at the top you click on it as one of my featured articles you click on it and then once the pdf opens up in the right upper corner you'll see the download option appear now the links are active in your pdf and so you can click so you can get access to the google sheet now what i'm going to demonstrate here today is how this particular company grew from 2015 585 000 to 25 million in in 2021 that growth that gave us a growth rate and you'll see that depicted here on the right the question now is that this customer acme asked us to do it says like look how many sales people do i need to bring on board in 2022 to hit 40 million and how many sales people do i need to hit to a bring on board to hit 60 million so on this capacity based growth forecasting i could say well if 25 million was accomplished by 12 ramped wraps then you know what then in 2022 we're going to have 40 million divided by an average 2 million per rep is 20 reps right because that's those 12 reps they did 25 that's 2 million on average per rep again this is non-recurring so this is i keep it simple right now just as simple as straightforward as possible it applies the same to recurring revenue and then what we are going to do is for 60 million in 2023 we need to have 30 reps well we had 20 reps in 2022 so i need to write higher 10 reps extra right and that means that somewhere in the middle of 2022 that you know like those rap need to be starting to be onboarded so that they can be functionally operational by the by the start of 2023 so we can hit that performance of 60 million starting to recruit them at the start of 2023 ain't gonna do as much good for 2023 ourselves and so as a revenue architect we need to validate when how many and so on and so forth and that based on this number 40 and 60. now in order to prove that this where the challenges are in order to show what growth rate currently predicts and where we should be working on we're going to step into a spreadsheet where we're going to take step by step through all the elements okay you're with me come on let's go here you see that same number exact same numbers why because they are linked to each other and so what you'll see down here this sheet is directly accessible on the research paper there's a link you can get a copy of this sheet so you can you know tinker around it with yourself and so what you'll see down here is i have in this chart i have this data set depicted insert chart and you're gonna get see that's the chart you're gonna get so you now know how i got that that's you know like that's standard operation what i'm seeing down here is i'm now gonna add year 22 and year 23 to it and so the picture below is that of all those years now look at that that looks reasonable growth right do you see that i mean if you squint and you like shake your head a little bit and you know like you'll see that line disappear and you'll see that line going up you're going yeah i can see us do that you know does this match up yeah matches up okay hire the people let's go not so fast okay let's start applying mathematics now what i'm doing is i am taking that same data set same picture i'm removing those two samples and and i need to you cannot what i'm about to do you need to remove the future years the forecast year of 2022 and 23 need to be removed now what i have is i have this data set do you see that data set what i'm going to do is i'm going to add a trendline well jaco how do you do that well i'm going to click on the data set look at that i'm going to go to the bottom and it says trend line on off boom there it is now as i now have the trend line that trend line depicts what the growth is approximately but something is off of that this is a linear trend line and what we notice is that in our rapid growth business we're not growing just linearly no we're growing exponentially double double double right and a lot of like startup scale up you know rapid growth businesses have that so i go like hey there are there other types yes there are there's exponential oh that's better oh that looks cool right and what else oh i got logarithmic oh that's odd how does that work okay so i need to now learn which particular line is going to be the most accurate which trend line is the most accurate now in order to do that i'm going to remove the trend line for a second and the first thing that i'm going to do is i'm going to change the skill now the way how i do that look i'm going i'm going to do it on this set this gives me the data set i just double click on the blue and then i'm going to click where is it where is it where are you where are you set up uh i want to get the logarithmic data set customize scale where is the axis always challenging that right chart title no i just want the axis follow me think along with me horizontal axis vertical axis that's it and then i'm going to click unlock skill boom yes yes google is not always as simple and sometimes the best way to do it i'm going to show you is simply click on that thing and that gives you like like automatically you go there so instead of me like find out where do i go i just double click that ah now i'm there click log scale and you'll see how it now reflects that okay so now i'm discharged this chart has a logarithmic skill look that i have the trend line engaged trend line and i now need to know on this logarithmic scale which trend line i'm going to use now in order to do that i'm going to depict down here i'm going to depict different very different regression lines types okay and for that i need to have what we call r value at the top our value is how far are you off the mark imagine that you're throwing darts at the dartboard our value tells you how far it is if you're one you're right in the middle of the dartboard and the further you go out the further that value goes down to zero okay so if you're totally out no points for you no putting what strong is that another brick in the wall no pudding okay so what we see down here i need to know which of these various trend lines is the most accurately tracking my current growth rate and so what i'm going to do i'm going to look at how that r value changes at exponential it's 996. so you see down here i'm going to write down sorry what is that i just click on trendline i'm going to make sure oops i'm going to make sure that that trend line again polynomial i'm going to go to power series and it zero says six one i'm gonna go like okay close logarithmic seven three four okay well it looks already odd right authoritic and you'll see it's further off you see up it's not good okay uh i'll skip exponential because that's the one we're going to pick in a second oh hey exponential why is that a straight line why is exponential straight line well because we have a logarithmic skill if you put an exponential line growth curve a trendline in this case on a logarithmic scale they cancel each other out and there you have the straight line and so exponential shows zero nine seven nine note that down let me take linear linear on a logarithmic scale does not become a straight line it curves and as a result what do i see a 2 6 oh sorry i typed it wrong that should be 8 6 2. okay now what you'll see now is the right line is polynomial look what i do if i click on that line and i make it polynomial so i say it's not linear it's polynomial and i use 2 degrees i'll explain that in a second polynomial oh it's look at that 9 9 6 okay that is close to 1. that's the line i want to forecast what is happening in year 22 and year 23 that's it the closest look at the values here polynomial comes to closest okay we now know now what is that second degree and third degree means it the simplest way of thinking about it is that it uses the two previous years or the three previous years to make that number more accurate and as you will see and if you'll tinker around with it in this case two and three does not make a significant difference what i'm now doing is as i now have that trend line i use the same chart but i simply drag and drop and say like hey no longer want i want this to be the data set i want that to be the data set so if you now see i added that 40 and 60 million dollar forecast that the company told me to verify i added that as a second set remember that was the capacity based forecast that was great now if i insert chart based on that you're not going to see the trend line just a heads up i'm going to insert chart where is it chart you're going to see that sample you see that now it's not a logarithmic so i'm going to click on that i click on log skill ah this starts to look alike you with me you with me yes chuckle we're with you okay and so you get this chart and then the remember that blue line has the associated trend line with it like remove the trend line back trend line polynomial second degree i'm close to one zero nine nine six now you'll see this visualizes down there this visualizes how far we off doesn't look like a lot right it goes like yeah we can do that well that actually could be as much as five ten million dollar off that's a lot if you miss your quota by ten million dollars on a 60 million dollar number that is 15 that is significant what can i do jocko what can i do to figure out how if this all matches so i don't want to see if this matches i actually want to calculate what the accurate dollar amount is that means i need to know what that line does okay now there is a way to get that line going there is a way for me to get that formula in now i'm going to click down here in this chart i'm going to find out what that formula is because lo and behold show r squared i cannot only show or scared i go to custom and use use equation and what i get here 874 210 143 are a few of the key variables i am going to calculate my forecast with these things now notice there's an x i know we're squinting jacob it's going to you know what why the heck i mean it's my screen i can do whatever i want let's zoom it in okay so you see down here eight seven four two ten one point four three to the power of six folks that all that is six means a million so that's one point four three million that is eight hundred to seventy four thousand dollars but that's 1.4 million times x and that's 893 279 dollars times x to the power of two okay remember that those numbers i'm going to show you i've already done the homework and already put those numbers in here so you got the 1 897 which was the x squared we got the 143 which was x and we got the 874 which was you know like 210. all i now need to do is i now need to create a formula that says i need to multiply that first number times x squared that second number times x and the third number um times keep it normal i just point out the formula this is called a quadratic formula so that formula is i'm going to use i write it down so we see it i'm going to use i'm going to start at the bottom 874 210 plus one four million thirty thousand one two three i should put a comma to make it clear which i think you get the id and i need to do that times x oh i forgot that to do that times x i'm just writing it out this formula is not going to calculate times uh what's at the top 893 000 270 times times x to the power of 2. that's the formula okay now i need to make that mathematically coherent and the question you have is what is x x is the amount of periods that i've started to measure now my data sample started in 2015 that means that in 2015 period is zero not one it's the delta of the period so it's zero zero when i start i start with zero the next period is one the next period is two i've omitted year the earlier years so 2017 equates to period 2. so if i want to calculate on average what 2001 ing this mathematic formula should be like i should put 874 times nothing plus that times 2 plus that times x to the power of 2. that's what's depicted here in this formula i'll let you i'm going to zoom in can i zoom in of course i can zoom in it's my day-to-day i can do whatever i want there we go you may want to take a screenshot you may want to re you know like think that through a little bit see what you're seeing use the colors of the letters to associate with the boxes digest it see it get it it's it looks more complicated and when you you know when you're going about it it takes but once you get it you go like is that simple it is that simple what i'm now propagating in this case i'm propagating the value moving forward year by year by year and i'm recalculating as this as the signal goes up i'm recalculating what i would look like for year two year three and so on so now i'm saying ah that was what i predicted that was the accuracy i'm 7.3 percent off then the next year i am a predicted 4.6 3.9 ouch four you know like three point fifteen percent off but what you'll see is that the the model is adjusting itself i go nine percent plus nine percent minus four point eight percent and then i come down to one perc 1.3 accuracy the model is adjusting itself okay there's a little bit more depth to it and for those who are mathematics i get it i could have joined the sample and created in this and stuff like that and multiply it too complex for today it works this works and so now what i have is i have down here the ability to come 1.3 accurate forecasting for cycle 6 and i can use that exact same sample all i do is i drag and drop that to the right and guess what i got in 2022 the forecast is 34.6 million in 2023 the forecast is 46 million and in 24 the forecast is 60 million folks we're gonna hit 60 million in 2024 not in 2023 a year later here's what's going to happen in practice this 34 million may become slightly closer because we got a big whopping deal in 2022 so we're going to be at 36 37 38 million dollars close enough to the original 40 that we forecasted remember we said it was 40 in 2022 and 16 year two and so you know like we'll be frowned upon missing the forecast but it's within error of margin of error as they say okay and then i'm royally halfway during the year 2023 i see this is not happening i come to the conclusion as a as a ceo as the board i go like oh we're gonna miss it vp of sales fired or you know that could be one hire a new vp of sales often at these kind of numbers the vp of sales actually has good relationship with accounts so we cannot really let go of the vp of sales so why don't we hire a cro on top of the vp of sales to manage it so the sierra owner comes in and guess what the next year we do 60 million dollars finally it must have been the cross everything works better forecasting is better may i ask what are you different what do you do different not a lot why it was just a natural outcome now why is this let me let me let me take you through what has happened here when you got to the 24 million dollars in 2021 it was not just based on the amount of sales people you hired because the amount of sales people you hired also needed to have job descriptions that needed to be written by the hr organization we need the tools to start recruiting because if we start recruiting 10 to 12 people we may need an applicant tracking system of sorts and so we brought in the tools well the tool by itself and the job descriptions didn't do we needed the process to do that then now we have tools we have process we have uh content hey we need to teach also our own hr department to do that right not only do i need sales people i need this for the sdrs i need you know like sales support people i need marketing campaigns all this all this growth was based this number down here how we grew from one point five or one point five eight seven how we get to four point six into nine point five to all these numbers all that growth on how we actually grew was based on how the entire company performed because this is the outcome of the entire company including inventorium products and whatnot and therefore it is way more accurate it is a superset it uses a superset of all these variables to determine that and that's the reason why this is more accurate now what we notice is that our at first glance it looked like that linear growth actually was accurate but it wasn't we saw these small changes in that number actually led to these big changes that is what i want you to start thinking about that is when i call we're using mathematics we now you're starting to use formulas and so on and so forth what you'll see down here is as i now start propagating that and i plug those numbers in is hey how if i plug that number in 34634 and i plug that in how accurate is my r squared how accurate am i now to the target and naturally i'll hit a one mathematically this is the most likely thing to happen there's always exceptions if you close a 10 million dollar deal that you never close before of course that is going to cause a disruption in it if you acquired a company with 20 sales people and 20 million revenue of course because historically that never had these are conditions that never happened before and as a result it could have not been predicted by this growth rate based forecast that's the trick that i want you to think about it's not a trick that's a technique that i want you to think about that is what is all written in this particular research paper that i shared with you to which i hope you enjoy i've verified it with many of you and i want to thank you for all of those of you who have done the growth forecasting okay it has been a fantastic way in order for me to help you this is not just it i just want to let you know there's more of these papers coming but what i want to make sure is that yeah like that you get a good idea of what what what what you're doing down here okay um that's it for me i just made sure that all the papers are online everything is activated it is right there for you for for for looking at it to summarize my key finding that i wanted to pick down here is that historically when we look at a capacity based approach that capacity based approach is based on us thinking that people solely are the key to what we're doing and that the systems are subservient to the people in a in a people-based approach when things go great we are hitting target first thing we want to do is let's hire more people and if things go awry the first thing we do is we say is let's fire people let's fire the non-performance and therefore in a people approach we blame the people and the systems are there to do this so first we hire the people then we bring in the systems first we look at the people the skill sets the enablement and the org structure do we do all right that's where we start to tinker with first in a systems approach we reverse it and a system approach is based on research that has repetitively shown that actually what we are seeing is that people act very consistently very consistently when you have a group of people there will be an additional flare out but most people operate very consistently and therefore it is not the people who are determining factor it is actually the systems the tools the process uh accuracy and response the quick responding to data which is the determining factor and organizations that are based themselves not on just capacity based forecasting which is based on people but combine that with a systems approach combine that with data and science-based methodologies are going to be far more accurate in forecasting in this case with potentially dire consequences for all kinds of human beings that are hired too soon or too late and who are being blamed for the non-performance which rarely has anything to do with one particular person or group that i want to say to you i highly recommend a paper that was written on this topic by professor james reason you can google it there's a pdf it's free don't don't fall for for those who are trying to sell you this and it says this particular quote that i have taken to heart when i started to look at sales after being you know years and years and decades of people of a person who performed just against quota you know like i came to the conclusion that that actually science can be applied uh james clear you know very famous because of of of his book about habits you know you know he recently updated that a quote a famous quote from a greek poet to the point that you see down here i'll let you read it yourself but that was essential for me like that that was so good i got it okay well if that said i certainly enjoyed sharing this with you i hope you enjoyed it as much that i gave this to you with that i want to say thank you very much for today's session i'm looking forward to uh one of these in the future um yeah let's do this okay see you later thank you bye-bye [Music] you