SaaS Metrics Are Lying To You (ft. Corey Haines of Swipefiles & SavvyCal)

Speaker A: Here's my first hot take and I'll come out swinging. Here is that lifetime value is a completely useless metric and concept for SaaS. Utterly useless. Completely. It makes a lot of really risky assumptions.

Speaker B: Welcome to Exploring Product where we go behind the scenes on what it really takes to bring new products to market. Too often people focus just on the success stories. Our aim is to flip the script. We try to unpack what product team actually go through when, uh, trying to bring new products to market. I'm Ryan Hatch.

Speaker C: And I'm Rob Kaminski. Every day we're trying to build products that our customers love. And we know just how messy and difficult product work can be. We don't have it all figured out and we're okay with that. Join us on our journey as we explore the world of creating new products.

Speaker B: Welcome to Exploring Product everyone. Uh, today we're excited to have Corey Haines on. Uh, Corey is a founder of Swipe Files and the marketing lead at Savvy Cow. So Corey, welcome to Exploring Product.

Speaker A: Yeah, thanks for having me. Really excited. This is uh, a topic I've been noodling on for a long time and um, one of these days I'll write a tweet thread. So I'm hoping that this conversation will be sort of like the open exploration of all my thoughts related to this because we'll have a lot to unpack.

Speaker C: That's going to be our whole marketing platform for getting people on the show. Just come battle, test your content. We'll get you nice and sharp before you take it live under your own name. Right?

Speaker A: I would love that. Yeah, I definitely need that.

Speaker C: Perfect. Great.

Speaker B: Well Corey, we're super excited to have you. And SaaS metrics are a big deal. Everyone's talking about different ways to look at it, there's tons of tools out there. But really kind of excited to have you on to talk about how your SaaS growth metrics are lying to you and really interested in why you're passionate about that topic and how that came to be. And how did you come to this realization that there's something wrong with most people. SaaS metrics and how people look at them.

Speaker A: Mhm. Yeah. Well, I think the whole backstory here is that uh, previous to what I'm doing now, I was the head of growth at ah, Barometrics, which is a SaaS metrics platform for uh, startups and for anyone with MRR, really I was looking at subscription based metrics and analytics. So I got to see all sorts of metrics, all sorts of different Startups and sort of their mix. Uh, I think over the almost two years I was there, I talked to about 10 to 20 founders and operators a week and got to sort of like dig into their barometrics instance and talk about their metrics. And everyone always had questions around, hey, is this good? Is this bad? What does this mean? What do I do with this? What does everyone else's look like? What are they doing? How do you improve this? Uh, where should this be? What's the benchmark? What does good look like? And so been in this for quite a while. Only recently though. So the other part of this story is that after I left barometrics, I did a bit of consulting and I guess sort of like coaching slash mentoring for a while. So at one point I was working with 12 different startups, uh, based on like a marketing growth capacity, um, to help them and would talk with them once a week or once every other week. And a lot of these same questions came up around metrics and how do we improve things and how do we drive growth? What are the key levers and kind of KPIs that we're going to be, uh, we're going to be focusing on. And it was through that experience afterwards, really being able to be in the weeds, that I started to kind of like piece some thoughts together on like, well, this, this thing, this thing over here is like kind of useless or like this thing is actually lying to you. You needed to actually like dig into this more. So that's kind of like the backstory of why this matters and uh, why I've kind of culminated these thoughts around why some of your SaaS metrics can be lying to you, what, what to do about it, how to dig into it, which ones are telling the truth, which ones aren't. And um, also because there's a lot of like, kind of old advice out there these days. Like SaaS used to be very new and nascent, um, but now it's like a pretty mature industry and it's well known, but we still kind of use some of the old, uh, basic ways of thinking in some ways. Um, and, or it's just like it's kind of that, that game of telephone, right, where it's like, oh, you're, you know, someone tells you that you should be at this metric or you should be focusing on this thing, but they don't tell you why. So then that person pass on to the next person, pass on to the next person, and then pretty soon it's like, why do we really measure that thing or like, what does this actually mean? And pretty soon it's kind of taken on a life of its own and it's lost its real substance. So anyways, that's kind of the backstory of.

Speaker C: So, Corey at Barometrics, it's interesting, like you said, a lot of this, like, thorough thought and insights around what they should be and could be came after barometrics. I'm curious, what was barometrics approach? Was it more of you could do anything you want? And, like, did you guys really not have a great story for these are the absolute best metrics, and these are the ones you should avoid. Um, so, you know, anyone watching who was maybe consulted by Corey years ago at Barometrics, you may not have got the best advice or what did that look like for you then?

Speaker A: Well, I tried to be as opinionated as I could be, but when I was at barometrics, it's very, um. You know, I just want to be like, uh, fair. I can't really divulge a lot about other people and sort of like, what is good look like. And it's hard to really be specific. So it was afterwards that I was like, no, no. Okay. Now that I've left, you know, I don't really have, like, any strings attached to what I can and can't say about what I think about certain metrics. And so it can be a little bit more of a loose cannon. Um, but also after hearing the same thing over and over again, it started to piece those thoughts together around, you know what, ignore this thing over here or actually, we should really dig into this part. Whereas before, I'm trying to be a little bit more consultative about how to use the product, how to get the most out of the product. Not necessarily, like, being very cut and dry around what actually matters at the end of the day. Barometrics, I would say, measured most of, you know, probably like 80% of the core metrics that you want to be, uh, they want to be measuring on a given day, week, month, or year. There's another 20% that we're missing. And I would say out of those 80%, really only about 20% of that is what I really think is important and what is interesting. Um, and that's mostly kind of what I'll be talking about. That's related to the main metrics that are lying to you in your instance of what you're likely looking at. Uh, like your growth rate, for example, or like churn, lifetime value, average item per user. Those are the types of metrics that are going to be top of mind for everyone and that there's more under the surface too.

Speaker B: Yeah, I remember when we're looking at, um, different platforms and then like, what platforms do you pull in? At what time do I do a fully integrated approach, One tool that can do everything, like more active campaign or more holistic, or do you piece together your own segment, IO data pipeline funnels, and you're like, best of breed, but you have to stitch all these integrations together. I'm sure you got lots of questions on, hey, what other tools am I missing? What am I measuring? What's important? What's the right time for the size of my startup, you know, based on the phase I'm at and peer stuff. I'm curious, like, how your conversations, like, it feels like you were in tons of conversations all the time. Did you already have these realizations when you were at barometrics of what was, what should and shouldn't be? You said you were already opinionated, but I'm wondering, did you have a different perspective on things when you're able to, to really look at the whole picture from a business standpoint as opposed to from a product pushing a product standpoint? Like, how did your perspective change and when was the wake up? When was the like, aha moment for you?

Speaker A: Hmm. I m think a lot of it has to do with what people are actually looking to get out of their metrics when you really kind of dig in. So I get questions all the time around. You know, you strike up a conversation, people are asking how to make the most of my account. What's good, what's bad, how should I improve this? What do I do? But then if I really kind of dug in, tried to understand, well, like, why are you asking that question? Or like, what is it about this metric in particular that piques your interest? And then you dig in and you ask why, you know, five or six times and you really get to the root of it. And then you realize that maybe they're looking at the wrong metric altogether. So I'll give you an example here. Uh, here's my, my, my first hot take. And I'll, I'll come out swinging here is that a lifetime value is a completely useless metric for sas. Utterly useless. Completely. This is one of the things that, one of the first realizations for me when I was at barometrics was people would ask all the time. And by the way, this is one of the core metrics that barometrics tracks. And, you know, profit, uh, well, and chart mogul. Utterly useless. Because what's happening is that, uh, people are asking, hey, what's, what's my left in value? And is this good and is this bad? And started answering the same question over and over and over again around, well, you want it to be around two to three times, uh, your first year's customer's revenue on average. Um, and then we start to dig into. People are asking, well, how do you calculate lifetime value? And I'm going through. And it's really just a function of, uh, your average revenue per user divided by your churn rate. And it's essentially a function of how much can I expect to collect, uh, from a customer over, on average, over the lifetime before they churn. But it makes a lot of really risky assumptions, for example, that every customer will churn over time, which is not true at all. In fact, if you go look through real accounts over time, you'll see that there's almost like layers like there is on, like the crust of the earth, where early customers, the layer might get smaller and smaller over time, but there are still customers from five, six, seven, 10 years ago. And then there's a second layer over 2013-2014-2015-2016. And not all customers churn over time. And so what we found was that if you actually measured the lifetime value of certain customers, uh, for example, at barometrics, you know, we would have a lifetime value of a, uh, customer who's been with us for a year, and it would be about a thousand dollars, for example. But then if you really look at our, our best customers and the customers who have been us with us for, uh, for years, it was 50, 60, $70,000, which is nothing, you know, it's just completely different. Right? So if we're measuring lifetime value, we're talking apples and oranges compared to how long customers been with us. Also, what is their plan these days? In SaaS, there are a lot of different, uh, price points that you can offer. You might have a freemium user which their lifetime value is going to be zero. Effectively. You might have a starting plan at 9, 19, or $49 a month. You might have sort of like a SMB plan around a couple hundred dollars a month. You might have an enterprise plan at a few thousand dollars a month or tens of thousand do hours a month. So if you're just, you know, then you can break up your lifetime value by segment, maybe, for example. But then, uh, even then, what do we really want to know at the end of the day about what lifetime value tells us? Well, when I actually asked and I actually poked and prod. People would say, well, we need to know lifetime value for acquisition purposes so that we can measure it against our customer acquisition costs. Because the startup culture and sort of the, the advice that's been m passed along through the millennia is that your CAC to LTV should be, you know, 3 to 1, 4 to 1, or 5 to 1 or something like that. Right. And that's how you know that you're, uh, that you're acquiring customers for the right price and that you can scale effectively. That's really interesting because if you actually look at that and you say, okay, my, um, lifetime value should be. Let's just say it's four to one. So CAC should be one fifth of the lifetime value of a customer. Well, the thing is that lifetime value grows over time for each customer. So at what point do you measure the lifetime value of a customer when you acquire them? Let's just say, for example, it costs a hundred dollars to, to make the math easy. Hopefully. I'm really bad at public math here, but if things don't make sense, we'll edit it out. Huh? You can change it over. Say that it costs a hundred dollars to acquire a customer and your average lifetime value is $500. You might be thinking to yourself, okay, great, it's actually less than five to one. It's more like six to one. So we're uh, it's going to be at profitable on average. But that doesn't take into account things like churn. So let's just say half of your customers churn out within the first month, which is actually pretty common, which a lot of people don't realize. Well then you effectively have to cut your lifetime value in half because you can only expect to collect a much lower amount from your customer base as a whole. Right. If you're only collecting up to half of that or, or much less. Right. Uh, even then again, when we take into account things like pricing plans, let's just say our starting plan is $9 a month. But your lifetime value because you have enter enterprise clients is skewed up. Right? It's skewed to be larger. So that $500 lifetime value, the customers that you're acquiring for $100, their lifetime value is actually like $100 or $200. Now you're kind of screwed. You're lighting money on fire essentially because you're using a metric like lifetime value to try to see if this is useful or not. So anyways, I could talk more about lifetime Value. But if you really dig into what people are actually trying to get to, what they're trying to get to is some sort of measurement of how profitable it is to acquire a customer on average. And in which case I started telling people, I started telling barometric customers, look, don't use lifetime value as our measurement against cac. Use a payback period. Use an average revenue per user. Because when you actually get down to it, you want to know how long is it going to take for me to recoup the costs of acquiring this customer with the revenue that they generate? And so much better. It's actually much better to use uh, a metric like average revenue per user and then use that as a function against cac. Right. So in this case, your CAC is a hundred dollars. Your average revenue per user is $10 per month on average. On that case it's going to take about 10 months to recoup the costs. Not accounting for churn, you can basically round up to like a year. Are you comfortable with that or not? Now you have a much more realistic, uh, example and scenario and equation to work with about your, the scaling economics of customer acquisition. Uh, so I'll pause there.

Speaker C: Yeah, the other thing, because I had a question coming in. Like my assumption when you talked about for acquisition, it's all about positioning for funding, right. Like the lifetime value is really about showing predictable growth of like give me money. But it lacks, in your articulation of it, it lacks the time horizon. Okay, five months, five years, lifetime value. What you talk about in terms of having a value per, would you call it revenue per user and knowing when it's paid back, if it's still for acquisition and investment, it makes a ton of sense because it positions the conversation to be like, when might I have to raise? Or if I raise, when can my investors start to see a return come back to it where lifetime value just doesn't quite capture it because it's missing that variable of time.

Speaker A: Yeah. And in fact I would say, um, that CAC to ARPU kind of ratio or factor is actually a really good way to create a marketing budget because one, you need a pile of cash to work with if you have uh, a payback period of, let's just say three to six months, which is like pretty normal and acceptable from m a marketing perspective to scale pretty effectively. But that means that you need about a three to six month buffer to work with before the costs start to recoup themselves, um, through revenue. Right. And so you need to have essentially, let's just Say your average earning per user or say your, your customer acquisition cost is uh, 100. $100. And you want to start acquiring 100 customers a month. Again, trying to do public math here. Make myself, make it easy for myself. Um, that means that you're going to be spending about $10,000 a month. And if you're, if your payback period is about six months, that means you need to have about $60,000 in cash set aside just for customer acquisition costs before that starts to sort of replenish itself in theory. Right? Again, you have to also account for things like churn, ah, account growth or account, um, uh, uh, expansion or contraction. Um, but that tells you, okay, now I can actually back into a number here. We need $60,000 set aside to work with in order to scale this effectively. What a lot of companies do is they start just lighting money on fire and they start, you know, pouring money into Facebook ads and the LinkedIn ads into sponsorships. And they don't know their payback period. And so they're waiting for the cost to be replenished and then it's like, oh, we have to raise six months earlier than we thought. Right. Or we have to turn it off right now, otherwise we're going to run out of cash because their costs aren't being replenished as quickly as they thought originally.

Speaker C: Are there metrics that. It's interesting when you say that rather than having a metric that kind of tells the whole picture because you're getting into cash flow. Uh, my assumption is that when they're just asking about cac, are they separately monitoring cash flow and then having to do this math in their heads of

Speaker A: like, my cash flow is this and

Speaker C: my CAC and LTV average is this. Um, what have you seen?

Speaker A: Yeah, if, if they're a really finance savvy founder, maybe if they have a cfo, probably because they're going to feel a little bit more accountable to, you know, the CFO's job is to not run out of money. Right. Or to basically tell the founder if they're going to run out of money. So they don't want to be responsible for that, so they're going to be modeling that out. In fact, I actually created a, um, a, ah, summit template. It's called usummit.com by a founder named Matt Wensing. It's basically like a Excel on a whiteboard kind of idea. Um, but those things are really useful to model it because you actually, again, you do want to know, okay, if this is the cash that I Need how quickly is this going to be replenished if things slightly change? For example, if you want to account for like a 5% monthly churn rate, right. That means that you have to multiply everything by 1.05 in order to account for the uh, effect of churn in the future. Right. Um, but to answer your question, I mean, no, a lot of people aren't thinking about this at all, which is why I like to talk about it because this was one of the recurring things that comes up with founders and when people are asking questions. But there are for sure ways to model that out and to effectively kind of run the numbers.

Speaker C: Corey, for context in the discussions that you have, and I'm sure it runs a spectrum, um, my assumption is that it's like B2C like early stage, like I don't know if maybe you can like level set the conversation where this most often comes up, um, by type of model and where they're at in their.

Speaker A: Whether it's revenue or happens, it happens across all of them. B2B enterprise all the way down to like um, B2C really really low arpu to prosumer, everywhere in between. Um, again with SaaS these days there isn't like a, usually like a strictly B2C price point. Uh, maybe like a, like a comm app for example. It's like what, like a hundred bucks a year or something. Um, they're a little bit more of like the exception than the rule. And also there's a lot of like big enterprise startups who are charging you know, seven figure contracts, uh, you know, paid up front. Right. And then there are three year commitments. Most startups are somewhere in between. We're going to have like a blend of freemium, low price point, mid price point, high price point. Um, and in that case you have to account for all of those and you know, you're effectively B2C, B2B2C and B2B. Um, and so it happens for everyone. You have to run the numbers whether you're charging a lot or a little. I would say that there are some nuances in there. For example, for a higher price point, usually it's going to take, it's going to be more expensive to acquire the customer. So your CAC is going to be larger. Um, and usually there's going to be a longer sales cycle. So you know, it really starts to get messy when you start thinking about payback period to the sales cycle to close to first revenue and that can be stretched out. Whereas if you're working with like a B2C product, like a savvy Cal for example, I can run an ad, get a sign up and get revenue right away. Right. And so there's the sales cycle is effectively zero days or whatever the trial length is and that makes it a lot easier. But again the numbers can be a lot smaller. So our are, you know, we're running Google Ads right now for example and our CAC is around 50 bucks. But the, the average first year revenue, average contract value because I, you know I'm not going to use lifetime value is somewhere around $150. Right. So we're working with about a 3 uh to 1 ratio ish right now around CAC to average contract value for Savvy Cal. But the revenue is, is right away. Right. So we start recouping those costs when we, we do it on time within three months. Whereas if you have a really high ticket revenue, uh, model and if there's a longer sales cycle and if there's even like a, a long cycle from like um, you know, from like close to revenue collected because of procurement or whatever it is, that can also add months and months, months on there. So also have to account for those things. Right. Account for those buffers, account for those delays. But at the end of the day they're all effectively the same.

Speaker B: Yeah. And you're really coming out swimming swinging here. Right. LTV isn't, isn't worth tracking.

Speaker A: LTV is worth.

Speaker B: Yeah, yeah. Um, I know when I was studying, you know, David Scott's work and looking at like SAS metrics, 2.0 type calculations and he really has like two main guidelines to abide by. One is LTV to CAC ratio. Um, and another one is really the CAC payback period, which is really what we're talking about here. It's all about cash flow management, right. It's the J curve. How fast can I pay back? Is it three months, six months or a year? And he really talked about when you run the financials, um, you're always going to be, you look like you're losing money unless you're really analyzing from a unit economic level. If you're just doing the whole company, it looks like you're always going to be losing money. So how do you get these early indicator metrics to know that you're not lighting your money on fire? And I think payback M period. He talks about if you're at more than 12 months, like you're sunk, the cash flow is just too heavy. And so he really encourages to get under 12. And so I think about, okay, payback period to me makes the most sense to measure. If I can get paid back in three months, boom, I m can turn that over and I can raise money on that and I got something going here. I think then the interesting thing is what you've done is you've just paid for the marketing department or the sales group, but then you haven't paid for operations or anything else. Um, are you thinking about a couple questions? Are you thinking about the financials in that sense? How do we pay for operations in a certain way once you've paid for the marketing and sales group? Another question I have is how are you measuring arpu? How are you measuring average revenue per user? It's got to be in cohorts, but it's got to be, uh. Talk about that. What's arpu in your mind you're doing this cac. ARPU ratio.

Speaker A: Yeah. So for that first one, I'm measuring operational costs and R and D and engineering and all that kind of jazz. Um, for the purposes of uh, effectively what we're talking about is cacta ltv or essentially your unit economics of scaling customer acquisition. The operational costs, R and D engineering is effectively not important because it's an entirely different equation. Essentially what happens is, for example, if you have a, uh, let's just say that you have a six month payback period. The payback period is for the marketing costs. And if you're, especially if you're blending in, into your cac, uh, basically like labor and salaries and marketing technology and anything else associated with marketing and sales, then like you said, that part of the company is accounted for. So then anything after six months goes to the rest of the company effectively.

Speaker C: Right.

Speaker A: To operations, to R and D, to engineering. And that's essentially what you use to uh, to model out and grow the rest of the headcount. Right. And how you scale up the company. Um, but you know, when we're talking about things like gross margin, um, and things like that, man, that gets really, really complicated very fast. I don't know if it's very helpful in, in many ways. Sure. Um, later on, I think when you're like series B and later and you're thinking about, you know, getting ready for exit ipo, things like that, you, you want to start thinking about gross margin. You actually want to. Once you have like a very predictable scalable acquisition model, then yeah, you want to think about, okay, let's get our gross margin to you know, 70%, 75%, 80%. You want to like keep bumping that up, keeping making it more profitable, more effective. But for really early stage startups and for even like just getting this going in the first place, I would say don't worry about it completely forget about gross margin. Just focus on those core unit economics of CAC to, uh, ARPU or what's effectively the payback period now on arpu. Uh, to answer your question there, it gets complicated. Um, but I would say that the, the more price points that you have, the more you want to separate your ARPU and kind of create these cohorts for arpu. Uh, if they're, if you only have one plan or if the plans are really close together, then it doesn't really make a difference. You can blend those together and average them out and that will still help you with figuring out that whole equation of CAC to ARPU. Uh, so for example, if you have a $9 plan, a $99 a month plan, and a $990 a month plan, it makes sense to create an ARPU effectively for each one of those cohorts and then to measure CAC against each one of those, you know, using some sort of attribution model, which I can also get to a little bit later if we have time. Um, because the way that you acquire them is probably going to be different and you want to make sure that you're not overpaying for the smaller cohorts and you're not underpaying for the larger cohorts and missing out on, uh, basically the opportunity cost of revenue that you didn't pay for because you could have acquired a larger customer that way. Um, but if you have a $49 a month plan, $99 a month plan, and $200 a month plan, it makes sense to average those out into one ARPU. And then that's kind of the main one that you use altogether.

Speaker C: You brought up attribution. Right. Uh, and I go into the why, right. Of where this matters. And you brought it up in your example of where do I double down? Where do I just completely ignore with your. My overpaying for the 999 user or am I underpaying? And when you say underpay, I want to know that because that's where I should be dumping my money. Um, what are your thoughts on attribution? Like does that get into tooling? Or you brought up models. I'm really curious on how you coach approach that.

Speaker A: Yeah, so here's my experience and why I bring this up is because attribution was by Far the biggest pain and blocker when I was at Bare Metrics. Because what happens is that you start to look at, uh, okay, where do we invest money? Where do we have to put more time and resources and budget into? And if you don't have any clear sense of attribution, then it effectively becomes a ginormous question mark and you're sort of stuck until you get an answer or you just start throwing money at things and seeing what changes. And you need to have a big budget.

Speaker C: Attribution by experimentation, right?

Speaker A: Yeah, you need to have a really high risk tolerance. So what we found was I started really looking into it and initially it felt like, oh, we have some pretty like clear ideas of what's working, um, because I can look in Google Analytics and see where the conversions are coming from. And then I really started to dig into the world of attribution. I learned about different attribution models. Um, there's like five or six kind of main ones, but really they kind of boil down to first touch, last Touch, and multitouch. And multitouch can be anywhere between like, uh, there's sort of like, you can give more weight to the first and last ones and then like blend out the rest of them. You can have every touch point kind of give equal weight to the attribution, uh, and everything in between. There can be linear, there can be decay. It kind of gets complicated, um, pretty quickly. But, um, what I learned was that Google Analytics by default, and you can't change this is a last touch attribution tracking platform. So effectively what they tell you in attribution is that when you look at Google Analytics and you see where the conversions are coming from, it's only from the last known source from whatever page they converted from or whatever was the last known refer. Effectively I'm like really, really simplifying this. Um, but that was questionable because most of the marketing that we do is not Last Touch. It's not remarketing, it's not, um, some sort of like conversion campaign. It's not even like sales focused where I'm working leads and I'm trying to get them to start a trial or to close from a trial. Most of the marketing that we do is First Touch, it's top of funnel. It's how did you discover us? How do we get in front of more people? And so it became, uh, you know, it was horrible to learn that it was Last Touch because then it was like, oh, frick. Well, this doesn't tell me anything. This is not helpful at all. I don't know where to put our marking dollars because I can't tell you what the First Touch was that got someone who eventually converted into the funnel in the first place. And so this is one of the other things of your SAS metrics are lying to you. Because if you just look at conversions from the last known source from Last Touch, it's going to paint a completely different picture. If you were, uh, measuring from a First Touch, which First Touch is probably the most useful attribution model that you should be using day to day. Um, multitouch is more of like a realistic. But if you're actually thinking about, again, what is the purpose of measuring attribution? It's where do we invest our marketing dollars, how do we get in front of more people? How do we know that something is going to result in revenue later on? And First Touch is like the most pure form of measurement there. But First Touch is the hardest one to measure. And it's also not the default in the platform that most people use, which is Google Analytics.

Speaker B: How do you, how do you solve for that? Like, do you, um, the best way I can think of to actually solve for that isn't actually data at all, isn't analytics at all. It'd be like randomly survey people on this plan versus this plan per cohort and figure out, hey, how'd you hear about us? Free form text. Not drafted, free form text. Like, I'm trying to figure out where the energy is coming from or at least where they felt like they became convinced or the biggest impression was made. Right. Otherwise we're just looking at. Talk to me about that.

Speaker A: Yeah. So, uh, I tell everyone to do that, and this is one of the reasons why, is because you don't know what you don't know. And the only re. The only way to really start to uncover that is to ask people, hey, how'd you find us? How'd you discover us? Where'd you first hear about us? It's one of those things that sounds too simple. You're like, nah, we gotta have a better way. But like, no, really, that's one of the things that you need. It's not the source of truth, but it's one of the things you need to start to piece things together for your attribution model to know what's working, what's not working. So in your onboarding form, in your demo, uh, form, in whatever it is that sort of gets, uh, people into the product, ask a simple question, don't make it, uh, like multiple select or like One of the like, you know, just click on this and select them, have it be an open text box, a little comment box and have people type in uh, how they heard about you. You can make it optional, you can make it required. I don't really have like a strong opinion there. Obviously if you make it required you're going to get more responses but it's going to be lower quality. If you make it optional you're going to get less responses, probably higher quality. So kind of pick your poison. But um, that's one of the big ones. Number two is you can do some like custom attribution tracking yourself uh, by using some really simple like kind of cookie in the browser, native uh, tracking. If you have like server side control of your website and if you are able to basically make some customizations to track uh, for every, every visitor that comes to the door for the first time you grab the, the known refer and the page they're landed on and then later on you can hopefully track that back to uh, when they converted. Now you can you know, pipe that into your segment, into your bare metrics, into your intercom, into whatever else, into a custom database. I don't really care, it doesn't really matter, it's up to you. But if you can basically attach a cookie on the first known visitor and say what was their first known refer and first known page that landed on, that'll help you a ton. Um, and this is more for like the technical folks out there. There's some like threads I can link to and ways that this can be done but I couldn't, I couldn't really explain it to you. Uh, and then the third way is through some other tracking analytics software. Um, personally I don't like Google Analytics. I don't use it. I think it's useless, I think it's outdated and uh, it's, you know, it might even be misleading a lot of ways. In fact it's funny because Google Analytics now through all the like privacy and data loss stuff, they don't track personally identifiable information and that is what you need for attribution unfortunately. And so by default they're becoming less and less useful over time. Now there's a whole other conversation around data and privacy and all that jazz, but I'm not super sensitive to that stuff. I think that it's useful and you should be tracking as much as you need to. So I like platforms like Split Bee personally that's what we use for Savvycal. Uh, you can use Fathom and you can send Custom events. It's like a privacy first if that's what you really care about. Um, there are some other really fancy platforms out there like ah, Dream Data and Attribution App and all sorts of other ones like that where you have to like spin up a custom kind of data warehouse and track everything in house. You can go that route. I don't know if it's going to get you that much more than a split B plus custom attribution, plus the onboarding, uh, form where it's asking you, how'd you hear about us? So those are like the three ways that I would say you're never going to have like a perfect attribution picture. It's never going to be solved, it's never going to have all the answers, but you're going to basically try to triangulate through those three sources. All right, where do we spend our marketing dollars? Where is like the, the, the most known places that people are finding us from and how can we do more of those things to get top of funnel?

Speaker C: That's awesome. So Corey, we have about 10 minutes left. I'm not sure if you want to throw another uh, hot take out there that you had in mind, um, with metrics or.

Speaker A: Yeah, well, here's one more interesting one is that this was another one that I realized when I was at Baremetrics was, um, the higher your growth, and especially the higher growth rate, the higher you can expect your churn to be. And this freaks people out because what happens is that it starts to work and you start to grow and you're adding revenue every month and more and more customers are coming through the door. And then all of a sudden, seemingly out of nowhere, you have a churn problem. And then people freak out and they stop the acquisition, they stop the growth and try to figure out their churn problem when in reality that's not what they should be doing. You should actually be, you should be either one, working concurrently to fix, return, uh, as your growth. But also two, you should actually kind of ignore it because by definition, essentially what happens is when you grow, you're adding more people top of funnel, more people are discovering you, learning about you, more people are becoming more interested in you. More people are coming through the door through the form of trials, through signups, through demo requests. And more people are coming through the door into your product who are paying, who actually might not be a great fit at the end of the day just because they're excited about the product or maybe the product isn't quite at that Product market fit for that particular part of the market. But that's okay. You can expect higher churn when you have higher growth. So at barometrics, we're growing, right? I'm like, woo, things are working, I'm getting my bonuses, uh, you know, things are going well. And then all of a sudden Churn, uh, almost doubles, right? And so we stop everything. We're like, we have to figure this out, otherwise it's just not going to scale. And so we tried to do that. Growth completely stalled. And then once we ramped growth back up again, the exact same thing happened again. I was like, wait a second, I thought that we fixed Churn. No, no, no, no, no. It, it sounds very counterintuitive, but you have to ignore it in some ways because you can expect for more people who are not good fits to come through the door. And you just have to do a good job of filtering them out earlier rather than later. And you have to be okay with that. In fact, if you look at, um, well, you can't. But again, I got the sort of inside scoop into a lot of SaaS metrics and in particular companies. And what I noticed over time, again, one of these things was the high growth startups are worried about Churn and the low growth startups are worried about getting more growth. And it turns out that that's, that's not a coincidence. Those two things are, are related because you find that a company is growing very, very steadily, or they're even basically plateauing, but their churn was immaculate. It was like, you know, 0.5% or you know, 2%, which is like basically, um, nothing. And then the super high growth startups, their churn was like 8%, 10%, 15% sometimes. And they're freaking out. It's like, well, you got to pick your poison, right? Do you want high growth or do you want really low churn? It's really hard to have both of those, um, unless you have a really, really strong market, really, really strong product, and you're able to counteract those things at the same time, which a lot of startups can't do those at the exact same time. And then as a part of that as well, speaking about Churn, a lot of people freak out about Churn because they think that it's way too high. And if you just run the numbers, uh, for example, like a 5% churn rate, I think that the math works out to where you would churn through about 40% of your customer base every year. But that's actually not True. And your churn might not actually be as bad as you think, because you need to account for reactivation. Reactivation is essentially when a churned customer comes back. And it happens more often than you might think. In barometrics, one of the. So we have that, we split it out into, into five different parts of your mrr. There's new MRR expansion mrr, existing mrr, contracting mrr and then lost mrr. Right. Or churned mrr. Uh, oh, and then there's. And then there's reactivation mrr. Right. Uh, and that's the one that everyone always forgets about because I see that little thing there at the bottom. They're like, oh, interesting reactivation. Like, these customers came back. So reactivation essentially eats into the lost mrr and it cancels out some of that lost MRR because they turned last month, but now they're back. So is it really churn? Now, you don't want to make your own formula and equation for churn, but basically, let's just say that your churn rate is 5%, but your reactivation rate is 1%. That effectively gives you a churn rate of 4% on average, because you can expect some of those to come back. So again, metrics, you know, we're growing, then churn goes up when we're freaking out. And then, uh, and then we stop the growth and we try to figure out the churn at the same time. The reactivation went down after we stopped growing. Why is that? Well, again, because people are coming through the door. They churn, but then they actually come back later. And so I found that our churn wasn't as bad as we thought. We can just completely ignore that we need to account for the reactivation. Um, and in fact, you can have pretty high churn if you also have a high reactivation rate because it just means that your customers are finicky. Um, for example, you see this a lot in B2C or the B2B 2C. If you're working with like consultants or side projects or uh, anything to do with like the creator economy, honestly is, uh, people are finicky. They're like, oh, I have a website, I don't have a website. Or I pay for this tool. I don't pay for this tool, or I picked up this gig, this contract, this consulting gig, whatever it was, and then just use it for that period. In fact, one of my favorite startups is, uh, SparkToro. Ah, founder was MA founder, uh, of Moz Ran Fishkin. Um, and he talks about this because they basically talk about how they know they have a higher churn rate than normal and they don't care because they expect customers to come back and to only pay for it when they need it. And so again, your turn might be lying to you. You need to account for reactivation.

Speaker B: Your SaaS growth methods are lying to you. Your LTV is not worth tracking, Your churn rate is lying to you, your attribution is lying to you. And this is all coming together. This is some great stuff, but there

Speaker A: are ways to counteract it, right? I hope it wasn't just like, uh, all these things are wrong. But really LTV is a sham. But instead you should use payback, period. You should use ARPU to compare against CAC instead. Um, higher growth equals higher churn. But also if you have a higher reactivation rate, then you don't have to worry about churn as much. Attribution is going to be really tough, but try to get to those first touch attribution rather than relying on last touch attribution. Um, all this stuff is really hard, right? This is me speaking from like four years of experience now and just talking to a whole bunch of startups. Um, personally I'm not even like a really big analytics guy or like data guy. I, I look at my own metrics like once a month pretty much. Um, because a lot of them are lagging indicators. There are very little leading indicators. Um, and so even if you look at things like sign up rates, uh, you know, number of new customers, there's really only like 8 to 10 you need to be really tracking at any given time. And I would say you really only need to look at them pretty much once a month. Otherwise you're kind of wasting your time just staring at a screen and looking at a graph and you know, pondering, well, what do we, what do I make of this? Right. Um, but if you really understand the core mechanics of what's happening and you understand what's okay, what's not okay, what are you doing to improve those things? The metrics will take care of themselves.

Speaker B: Yeah, and again, it's different for every business. I know we're talking about general SaaS here. These are generally applicable to subscription type companies. But like these are all lagging indicators and they don't matter until all the leading indicators are solved and, and all the other micro customer behaviors. Right. It's really what we want to be looking at. Are we delivering on value? Are they getting value time to value all these other things that are going to be different for every single company. What do you think about this? Like, you kind of talked about higher growth, higher churn. Um, and is there kind of like, is there a parabola, Is there like a sand timer where it's like you have high churn in the beginning because you don't have product market fit? Maybe you close that gap and then churn goes down. Your growth is really accelerating because you've, you've hone in this target market segment and you're able to raise a ton of money. But then you need to grow, you need to grow faster. So you kind of like, you loot, you expand the top of the funnel really fast again. And then, and then you kind of like the same time inverts again where, because you're chasing higher growth, churn goes down again. Is that kind of like, is there a sweet spot in there where you think like. Because you have to know, when should I pour fuel, when should I pour more gas on the fire?

Speaker C: Right.

Speaker B: And when not to do that. Like, product market fit seems to be like this sweet spot in the sand timer model, uh, in my mind. But is that kind of resonating with you or is that not how you look at it?

Speaker A: Yeah, no. 100% growth normally comes in S curves, not in these linear progressions going up and to the right in a perfect kind of algebraic formula. Right. Uh, it comes when, like I said, you focus on one thing in particular. So maybe for a while you're really focused on retention. You're trying to find product market fit, and you're working on kind of the core mechanics. And then you start to scale things up and things start, you know, that's where you get kind of like that first curve of the S. But then churn catches up to you. You need to, uh, you know, maybe some incumbents come by and they copy you. Some competitors pop up. Maybe. There is a really fundamental issue with, with churn, the way that people are using it or even pricing. And so then sort of it starts to, starts to even out again. Right? And then you get kind of the top of that S curve again. But then you want to repeat that S again. You need to be able to figure it out and then create another curve up again. Hypermetrics, uh, it was never linear. I think another one of those, like, outdated models or sayings in SaaS is, uh, the long, slow SaaS ramp of death. Because in the early days things were pretty slow moving and it was just sort of like, yeah, you have your SEO going and you have like your word of mouth. Going and just kind of chugs along going up and up and up and up to the right. But it's hard to really make things go parabolic like you said, or to really flatten these days that is not the case. Um, it's usually flat or it's going up a lot. It's hard to really have a very consistent slow growth. Um, not to say, I mean savvy cows experienced something like that, but we still had some plateaus, uh, barometrics had plateaus but then there were some, you know, parabolic growth, uh, periods. Definitely more common than not to have like an S curve rather than just like a straight line graph up and to the right.

Speaker B: Right. I love that growth comes in S curves. I think that's, that's super true. You think of like um, you know, whether you want to even corporate innovation, you think about how do we get to the next level. Is it gross through acquisition? Like not CAC in this case, but actually acquiring a company versus starting a new company. Kind of like same thing with a series B kind of startup. It's like do I expand to a different customer segment? Do I go up, up into enterprise? Do I go down into B2C and do this SMB play? Do I come up with product 2? Do I expand inside my current clients? Like there's a lot of different ways to get that next, get to the next curve, right?

Speaker A: Mhm. Oh yeah, yeah, 100% I would say too. You know one of the ones that is largely overlooked is expansion revenue. I don't know very many companies that intentionally try to grow their expansion revenue where that's like a key KPI of we want to, we want X percentage of our MRR growth to be from expansion revenue. And here's how we're going to go and do that. But that's actually, I mean it's one of the huge advantages of having a SaaS product. Running a SaaS business is you can have expansion where a customer starts paying you, you know, twice as much as they were before. Overnight they just clicked a button. All of a sudden your RPO doubles from that one customer. Right. And ah, more companies aren't. More companies should be focused on how to do that, how to move their customers up the pricing tiers of their own structure rather than just focusing on bringing more customers in through the door. This is actually one of the things that I focused on a lot when I was at Barometrics was uh, upselling and cross selling. Barometrics had created sort of like a suite of products between analytics, which was like the flagship. And then there was Recover, which was a dunning tool. And then there was Cancellation Insights which was like a churn prevention tool. And then there was Messaging which was, I don't know where it's at today, but it was sort of a intercom competitor in some ways like a messaging tool. Um, and for about a good year I focused pretty much exclusively on increasing the adoption of Recover and Cancellation Insights from users who are already using Analytics. And that's where about half of the revenue growth came from that year was just from uh, running campaigns, doing sort of like some inside sales, uh, creating some like product led growth upsells and pop ups and ways to expose the product within there where you know there's a, you know a micro ad in there or there's hey, how do we improve this metric? Or what does this mean? And then there's takes you to a page where it sells you on the, on the product. You can try it out, um, campaigns around, hey, we'll give you your first thousand dollars recovered for free if you want to try it out. Or hey, I'll consult with you for three months on how to improve your retention if you also use Cancellation Insights. And that'll be like the core research, uh, sort of data set that we use in order to make product suggestions and fix uh, onboarding, all sorts of things like that. Right. But not a lot of people think about expansion, um, or contraction either. If you, if you have a lot of contraction revenue, man that is a horrible sign you have to fix right away. But more um, important thing about expansion.

Speaker B: Yeah, it's always easier to um, tell someone who's already getting value. Right. Than start from scratch. Right. And I was reading an article on these, these you know, top companies that raised VC versus top companies that bootstrapped. It's just comparing the two. It's really interesting takeaway that you, you know, the top companies in both actually had negative churn. Right. Like it was like expansion inside existing contract expansion was huge. And the interesting takeaway is that the bootstrap, the bootstrapping companies had outperformed on the expansion side, um, which is really, really interesting. And I think there's so many implications on the expansion side of even you talked about, hey, I had to work with the existing product team with the existing product to put an ad inside the product for this other product and it becomes this really interesting like how do you organize teams to work together to you know, you know what I mean? Because you're crossing product experiences at that point.

Speaker A: Mhm. Yeah. That's a lot of fun. Yeah. And net revenue retention is probably another one of those like core metrics that I would say you have to pay attention to. That's, that's the dream really. I mean if you have uh, if you have negative churn quote unquote or essentially positive net revenue retention year over year, um, that is an amazing business model where basically cancels out every one of your churned customers because your current customers are outpacing the revenue that you've lost from lost customers. Um, that's, that's a dream. I mean there's nothing, there's no, no other like business that can do that. Right. Um, that's what kind of brings me to the SaaS. Why I've kind of fell in love with the business model and tested products. That's really magical.

Speaker B: Great stuff. Corey. This has been so awesome. We talked about payback period. We talked about first touch being really important. It's so interesting that there's not a lot of um, you know, even help on the first touch side especially. That's. I think it's going to be an interesting thing going forward with the privacy plays coming and how do you capture that? Um, but you covered a lot of great stuff today and I'm so excited because this is stuff that people obviously don't talk a lot about. I think it's really easy for us to get, you know, as product people, you know, to get tied into tooling. Right. Isn't that always it? Uh oh, if I just hook this up to this and look at these. Cool. Look what I could do and intercom this or that. And it's actually less about the tool, it's more about what method and the process and your mental model that you're bringing into the business. Right. And let you get. Use whatever tools you have but view them the right way, measure the right things. Mhm.

Speaker C: Corey, where can people hear more about the stuff that you post and share and talking about metrics, um, in your community or what's best for people to get at you?

Speaker A: I'm on Twitter all day long Orhansko sharing things mostly about SaaS marketing. Um, also if you just go to swifehouse.com you can sign up for my newsletter. It's uh, cutting edge, sometimes crazy marketing ideas straight to your inbox. I share a lot of case studies, thoughts like this, um, tactical stories and things that I've experienced, frameworks, strategies, um, that I've noticed. There's also tiny marketing ideas which is like a drip of I think maybe close to a hundred, uh, sort of bite sized marketing tactics that you can implement for your business. And really it's just like get your brain going. That's just that. Spyfails.com Tiny I believe. Um, and check out Savvycal as well if you're tired of calendly. If you don't like the power dynamics there, uh, we've got some fun stuff. Derek, the founder is an amazing product builder. It's a fantastic product. I was one of the first customers actually. That's how I got connected with him in the first place. But, um, mainly Twitter. Just connect with him on Twitter and you'll find links to everything else.

Speaker C: Awesome.

Speaker B: Thanks for joining us today. We hope this episode gave you some fresh perspectives and even some inspiration to help you on your product journey.

Speaker C: You can access notes, links and resources from this episode@exploringproduct.um com if you enjoyed it, please be sure to share it with us on Twitter so that we can chat about it together. Until next time, keep exploring.