In the below video I discusses cohort modeling for SaaS (Software as a Service) businesses and its impact on financial forecasting. Cohort modeling involves analyzing a group of customers who join in a specific month and tracking their performance over time. I explain the configuration and assumptions used in the model, such as contract value, retention rate, and churn.
Check out all SaaS financial models here.
SaaS Video Tutorial
The video demonstrates how to calculate the value of a cohort over time based on customer retention and contract terms. It also explores the concept of negative churn, where existing customers generate more revenue through upsells and add-ons. I tried to put an emphasis on the importance of retention in SaaS businesses and how it can impact monthly recurring revenue (MRR) without heavy reliance on sales and marketing efforts.
The video showcases various visual representations of the model's output, including graphs and charts. If you are interested in all sorts of financial templates and models feel free to explore more on this website. SaaS financial modeling is only one example of the many industries that cohort modeling is useful.
The video provides insights into cohort modeling and its significance in analyzing and forecasting the financial performance of SaaS businesses.
Here is the video transcript:
Hey, what's happening? Everybody. We're doing a quick little video on a SaaS cohort modeling today. I've got many models on the site for doing such analysis. And what cohort modeling is, it's basically you're looking at something for how people analyze SaaS businesses and then taking that and driving the way the model works.
So when I say cohort, I'm just simply saying a group from a given month and we want to see how they perform over time. Now, in terms of the modeling, we're gonna drive how they perform over time and that drives the financials and I'm just gonna look so it's really easy to see. We're just gonna look at a single cohort of one month and we're gonna do one month terms, month to month contracts and $50,000 CPA average contract value 350,000.
Well, let's make this a bit smaller or just do $100. We're gonna say this does not change over time. Oh, before I forget, subscribe like the video also, if you want to see the model I'm doing right now. Is this one B to B SAS. This is the template I'm using, but you can buy all the Sass models I've ever built and gain access to everything in the future for $289.
That's a lifetime license all kind of models here for Sass. This is the one I'm gonna do right. Or I'm looking at right now to do this little tutorial. But Sass modeling has been tough. It's, it's one of the hardest things I've had to get. I've had a research and try to understand in my career and it's been, you know, obviously very lucrative and it's, it's tough, but people need it done and it's really a valuable skill, so I'm happy to do it.
OK. So here's our configuration. Where was I were doing $100 contract value? We're gonna assume that the, the every time a customer renews, we're just gonna do a 0% change in the value. So it stays at $100. If you were to put this at say 10% that means every time a person in the cohort one renews they actually renew at a higher price. And that's what this is to account for it, be like 100 and $10 and then 10% more on that, et cetera.
So it's a way you can kind of model negative churn potential. We're gonna put a 0% there. Retention we definitely want that 80% month to month, I mean, a more you probably want something like 95 that means every month you retain 95% of the previous month and we're gonna 0% off this. So it's just really easy to see. We're also let's clear all of this out. So all we're gonna see is the contract cohort data and clear this.
And the idea is for Sass, obviously, retention is king. If you have really good retention rates, your Mr is valued really high, you are, you have a really, you can get a really high MRR without having to spend a ton and ton and ton on sales and marketing because you're retaining everybody that you, you get. All right, zero, all this out, zero and everything out.
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OK? I should have did this before the video, but we're jumping right in here. OK? So we have our cohort. Let's look at the actual matrix I do with this. So we've got the count joined per month and let's just look at one month. So we go to the monthly detail. We're only gonna look at this customer's added to organic and we'll also look at ad spend. But the whole cohort, it doesn't matter how you acquire them, the the configuration is gonna be the same, we clear out the rest of this.
So it's just one cohort the first month we added three total customers. What are the economics of that happening. Well, if we see our in our matrix here, we've added three in month one. Now, the way I like to do modeling each month is just a cohort. So whatever however many customers join in month one, this is month one cohort, there's three, we want to see how old is this cohort.
So it start counter starts here, go 123, start counting in the age. This would be dynamic. If we were to add more over time, you would dynamically get the age of each cohort. And then that is very important. What you do is measure the age of the cohort. How many people are in it, how many customers are in it? And then you, you multiply those the number by your revenue per month average, whatever that may be.
And there's more complicated things. I've gotten this model where you can, you can account for collecting all your cash upfront or over time evenly. If you have a a, you know, a contract period of like 12 months or six months or something right now, we just set it to one. So it'd be, you know, month one, we collect the revenue per total contract value, which is because it's just one month.
So it's 100 bucks per customer. And what we do is look up this age in our validation and we say, OK, well, what's the renewal account? First of all? Because you gotta know how if you're compounding the value of the contract, which we put it at 0%. So it's just gonna be the same every time. And then we also have potentially different, the ability to improve on the cohort retention over time, but we're not looking at that, we just care about year one.
So what we know we sit at 95. So this shows you how many customers are left over time. Now, if you wanted to make it even, you know, like maybe a curve where it's like 100% 60% 40 35 30 like a little bit of a a different type of curve rather than linear. That's fine. You can completely configure that in each of these here manually for tier 12 and three.
But the key is we know at each renewal point, how many customers are left. And then by that logic, we can figure out how that cohort is gonna perform over time. So the one, the the only the people you signed up in month one customer and you signed up in month one, this is how much is left in month, two month, three, month four, et cetera. Al always you start with 100% for the first period.
And then what we can do here go to our matrix where you see this math happening. So you're joined, this is just some dynamic references, the percentage of cohort remaining So, even though it's three, it's not a whole number, but that's fine. We could say, well, every month we're losing 95% it means you don't really lose 0.1 of a person, but you have that percentage probability to have 0.1% less.
So they might leave, they might. Now the chances are they don't. But to get a smooth percentage, this is how you do it with a finish modeling forecast. Now you get down to two after eight months, you get down to a little bit less than one after 15 months and this is starting with three and then you finally are going to zero after 20 months based on that retention schedule we have.
So, so to figure out the value of this cohort, all we're doing is saying the you you had three people join here their pain $300 next month, we have a little bit of less customers that are paying 2 85 a little bit less customers. Now they're paying 2 70 but these are only, this is only activity from people joining in month to month. This is not people that joined in month, 2345, etcetera.
We have, we zeroed all that out. So just looking at a single cohorts financial performance. Now, what if you wanted to say? Well, I think actually I'm gonna churn customers. What? But the ones that stay will end up paying more because of add-ons and up sales. Well, you could try to model that by saying, well, what if we think on our configuration here that we get 15% more money from the customers that stay.
Now, if we go over to this matrix, look here, we actually go from $300 a month, 1 to 328 month two. That's actually a negative churn. We have negative because churn is a bad thing. So you lose money. But if you have a negative churn, it technically means you actually have more money with less customers and you can see this 10% going up, you're still above the 300 mark even as because in its monthly here.
So I mean, getting 15% per month is pretty like that's a lot. So it's not realistic. But you can see the financial effect usually, I mean, 10 or 15% for 12 month contracts is probably or a a and again, it depends on the business. It is completely arbitrary in this case, but it just depends on the business. So here you can see finally we're going down below $300 after month 20 of that initial cohort that joined in month one and then zero.
So that's, that's pretty much it for cohort modeling. And when you look at this, when you're trying to analyze existing actual data, this is how you do it. You figure out well, how many people joined in in whenever you start the forecast in this month. How much did they pay in month one? How much did they pay in month two? How much did they pay? Month?
Three, et cetera. And you, you model that out, you can create retention off of that based on how many customers are existing after each month. And all of that can inform numbers to model going forward or to just report on existing data and then you would go to like, OK, how many people joined in month two? What did they pay? Well, month two is over here.
And if you're looking at the actual model time period, so these people talking about two, what was their retention and, and money? And how much did they earn the company et cetera? So that's how you report actual data and that's why this model is so great because you're looking at real, you're you're taking the reporting style and then integrating it into the configuration style so that it matches so that it makes sense to people when I go to try to forecast, they can forecast with
the same concepts and methodologies that are used to analyze. And then here's your just your churn count each period, here's just a straight line. So you can see over time, we add up to three after you add them all up, three total customers, here's the dollar value churn and again, we're going up so we're actually negative. This is where the negative churn comes in when I put in, you know, they're actually paying 15% more every month that they renew.
So there's that and then I, I this is logic for collections. If you, now if you're collecting customer seats up front and you can see what would this, what would happen if this is 12 here, the contract length? Well, watch how things change. Now you've got, you've got revenue over the full five years. But look at this, the total contract value did not change.
So it's still $100 but now it's spread over 12 months instead of $100 per month. Now, if I were to, let's just extrapolate and say, OK, it's actually 1200 maybe you give a little discount, maybe it's $1000 and these people sign up for 12 months. Well, now, yeah, you see a significant difference because they're paying each pay pe person is paying that much and they're staying for 95% are staying after 12 months rather than after one month.
So it's a huge difference. Now, if we go over here, you can see this is where the actual cash is collected because I have it up front. So that's just for cash flow modeling. But for the, the value of the contract over time, you see that's here and it's not changing again until the renewal period. So here's renewal month 13 is now 10% more for everybody that stays and you got 95% of the customers left, which is this percentage of cohort remaining.
So here's your three, all three, all, all of them stay for 12 months because that's the contract term now and it's a completely different style but that's, that's the beauty of these models. You can do all kinds of different things. And then if we look at some visuals, let's see. Why is there? I'm just see if there's a spent, let's zero that out.
OK. OK. Yes. Now we just have the economics of what we just did. So if we look at the visuals is your month end customers monthly revenue actually going up, there's that negative churn. So you're getting more from less and now if we look at the turn rate, it should be going down where it this is a term percentage they value OK. Average monthly churn negative.
So that negative is you actually gaining because normally turn is saying, OK, how much did you lose? Well, we lost it, we lost negative eight, meaning you actually increased and these are in every renewal period since it's 12 months, those are kind of far apart. But you get the idea now it'll look way more smoother, way more smooth when you add in assumption for for different tiers as well as I I just had one month's worth of data.
So you guys can kind of understand the concept here, of what is happening. Now, look at the, these numbers changed because I took away the ad spend. So you're only getting customers through. Well, it's just the one single customer rather. so that's what you're getting $1000 in the 1st 12 months because $1000 contract. Now, if we zero this out and you put 100% retention, well, you're just making $1000 every 12 months.
So that's kind of a way to check it too to kind of understand what you're seeing here. But all right, that's all I got for you. Just a little bit of insight into a modeling, very valuable stuff here. If you're in the industry or if you're trying to understand how, how this stuff works. If you want more, you can always check out smart helping dot com.
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Alrighty. Well, I'll see you guys on the next one. I think the next model I'm actually gonna build on Monday of next week would be a cost segregation model which is in the real estate sector. mainly it's really for just depreciation and I mean, it's, it could apply to anybody that owns buildings and appreciates them, but we'll get into that later and I'll see you guys here next week.
