Episode Transcript
Asia Orangio (00:01.28)
Okay, so today we are going to talk about product market fit.
Kim Talarczyk (00:07.741)
It's one your favorite topics, isn't it?
Asia Orangio (00:09.62)
Yes, it is. Genuinely. The reason why is because I feel like we hear the same story, especially from like earlier stage companies that we work with of we have product market fit. And then I'm like, OK, well, how many customers do you have? And they're like, we've got like three. I'm like, I don't not saying that they don't have product market fit, but I don't think that you can say that.
Kim Talarczyk (00:30.726)
You
Asia Orangio (00:38.476)
you have product market fit with just three customers yet. Like it's one of those gray areas. Like you might, but you also might not.
Kim Talarczyk (00:46.13)
Mm-hmm.
Asia Orangio (00:47.966)
And I feel like there actually are some quantitative ways to measure this. And that was what I wanted to unpack today. But we have heard this a lot where we'll work with company, they're relatively new, maybe like a year or two in. They have a handful of customers. to me, you can probably find someone, for any product out there in the world, you could find like three or four people to pay for probably, even if it's a bad product.
Kim Talarczyk (00:55.611)
Yeah.
Asia Orangio (01:18.466)
But to me, that doesn't mean that you have product market fit.
Kim Talarczyk (01:21.787)
Yeah. So you find most people are, or a lot of people might be guessing when they have product market fit.
Asia Orangio (01:30.004)
that and the
the way that they're measuring product market fit or the way that they are quantifying it, and I'm putting that in finger quotes because it's very loose. It's more on vibes than it is on, like let's look at actual metrics to really understand product market fit, which there are. There are a few. There are a handful. We're gonna talk about some of the first OG ways, and then we're gonna talk about what I think is more of an indicator.
And it's a set of things, it's not just one thing.
Kim Talarczyk (02:06.845)
Cool, I'm excited.
Asia Orangio (02:41.55)
I just kind of want to set the stage for like, what is it? Product market fit is a term coined by someone that probably should have researched at this point, but it has been a term that has been used in the SaaS and software industry for years, possibly even decades. But when we think about product market fit, so what we're basically saying is
The product that you have created or developed for this specific market has a very strong fit or alignment, meaning the people in this market want this product and are willing to pay for it and are willing to stick around long enough for there to be a mutually beneficial relationship with that market. Now with that said, product market fit I think also has been used to describe
almost like the beginning stages of the evolution of the MVP, the minimum viable product in that market. And so I think in some ways, the definition of it has kind of stretched a bit into you're on version two or three of your MVP or whatever it is. And, and that seems to do better than your first iteration of the product. So therefore you have product market fit. But what I actually think
Kim Talarczyk (03:48.783)
Hmm.
Asia Orangio (04:10.542)
is more true probably is yeah, like your product is getting closer to it, but you won't actually know if you have true product market fit in my opinion until you have a certain number of customers with a certain type of retention rate. So the way, and I'll never forget this, and I'm actually still a proponent of this for some companies, but I actually don't think it works for all companies. And I'll share more here in a second. But the way that we used to measure product market fit,
Kim Talarczyk (04:26.417)
Mm-hmm.
Asia Orangio (04:40.31)
or two ways. And I think that this is still true. Like I think a lot of companies still do this. The first is more traditionally NPS, so Net Promoter Score. This is that infamous question of how likely are you to recommend a product to a friend? And it's like a scale of either zero to 10 or one to 10. At 10 being the most likely, anything above eight is considered a promoter. I think it's like,
six to eight or six to seven is passive, if I'm not mistaken. then anything less than, anything five or less, I think is a detractor. Could be misremembering. Yeah, 10, I think nine and 10, if I'm not mistaken are, like those are promoters. It's either seven and eight or six, seven and eight are passives. And then anything five or below, if I'm not mistaken, are detractors. I might be one or two off here, but.
Kim Talarczyk (05:18.013)
And 10 is a raving fan.
Kim Talarczyk (05:26.406)
or promoter, okay.
Asia Orangio (05:35.535)
That's like the general idea and then what you do is to calculate your score you you send that survey out to all of your customers and Everyone who's a passive you ignore their scores completely you take promoters minus detractors and That is what gives you the score and hopefully you have a positive number
And yeah, I'm pretty sure you totally omit passives. I don't even think that you look at passives. But it is helpful to know what percentage of passives do you have in correlation or relative to the other folks. There's a lot of criticism for NPS because it's sent at specific times and also people's feelings do change short term versus long term. And also NPS gets a lot of criticism because...
referring a product to someone isn't necessarily the best indicator of fit all the time in certain contexts. So NPS is a good indicator of like how happy is someone generally with your product, but that doesn't necessarily always indicate product market fit. And we're going to unpack why, but it's one of the gaps here. And I've actually talked to a few customer success professionals and consultants who
are very critical of NPS because it does kind of feel like a vanity metric. It's kind of like a feel good metric that might not actually be grounded in reality because how someone feels about something is very different than how someone, like what they actually do in real life. And this is why NPS can be a little bit controversial depending on who you're talking to and also depending on how you want to use the metric. But generally speaking, NPS is one of the like old school ways of measuring product market fit.
Kim Talarczyk (07:03.366)
Hmm.
Kim Talarczyk (07:27.655)
Got it.
Asia Orangio (07:30.786)
Sorry. So the second way is the, what we now call the product market fit survey. And this was started by which company? was it Buffer or Basecamp? Now I'm not gonna remember.
So, I feel like it was Buffer. I remember the company that championed it was Superhuman. So Superhuman was the email client. I cannot remember if it was Buffer or Basecamp though that created, like that was the originator of the survey. But basically, there's the product market fit survey. It's a set of three questions, possibly four questions depending on like how you wanna structure it. But the main question is,
how disappointed would you be if you couldn't use the product anymore? And there's three answers. There's very disappointed, somewhat disappointed, and not disappointed at all. You have product market fit if you have, what is it, more than 40 or even 60 % of people who say very disappointed who are in your specific target market. So I mentioned that there are actually four questions in the survey. If I recall correctly, one of the questions is going to be, how would you best describe
people who get the most benefit out of this product. And then there's a third question that you'd add, would be, what do you get? What is the most benefit that you do get out of it currently? And I think the fourth is, what would you add to this product, if anything? Or what would you want to see us do next, or what have you? And the whole premise is to filter by people who describe your ICP, and then...
measure the amount of people or the percentage of people who say very disappointed. The somewhat disappointed is interesting because they're kind of passive and there are some cases where like you'd say, yep, include those people or don't, but you definitely want to understand how many people say not disappointed at all and does that fit like your ideal target market? Now I do think that this is a good survey to run, but again, it's similar to NPS where it doesn't actually, it's not actually grounded in a real behavior. It's grounded in
Asia Orangio (09:40.791)
what people believe about themselves and like their, I don't want to say like their imaginations, but it's more the, what they're like willing to kind of make up or believe, whether or not that's actually true in real life. So Bob Mouesta, go ahead.
Kim Talarczyk (09:59.036)
Mm-hmm.
No, so you're asking them to essentially describe the market. So you're assessing if they understand who this tool is for. And is that assuming that they're included in that?
Asia Orangio (10:20.044)
Yeah, so there's that assumption. There are some schools of thought that say to have the person instead describe themselves rather than ask who do you think this product would be most suited for. So like some people have adjusted this like four or three question survey to kind of help you assess product market fit. But yeah, like I think the original version was who do you think that this product is best suited for?
Kim Talarczyk (10:29.041)
Mm-hmm.
Asia Orangio (10:47.086)
And then you'd ask them the question of if you couldn't use it any longer, how would you feel? And again, like the goal is to filter by that first question of everyone who kind of describes your ICP. Now again, I think this is kind of problematic sometimes because, and this is a survey that I've used a lot. Like I've used this, we've used this survey a ton. We still will actually incorporate it into some surveys, but not to assess product market fit, really more to understand
Kim Talarczyk (11:06.844)
Yeah.
Asia Orangio (11:16.158)
It's really more about, I think, how someone perceives the product and how they feel about it more than their actual behavior of if they stick around or not. And I do think that, so there are some leading indicators, but again, those leading indicators like NPS, like the product market fit score or the survey, I think that they're leading indicators, but they're not necessarily grounded in reality all the time because someone could be very disappointed
Kim Talarczyk (11:26.023)
Mm-hmm.
Asia Orangio (11:45.443)
But actually, that's only in that one time slice. You fast forward six months or 12 months later, and they might feel very differently. And I think that that is actually where product market fit can kind of rear its ugly head is long-term, not necessarily always short-term. Although of course, there's initial product market fit in that short-term period. But I think true product market fit is not revealed until long-term.
Like that's because people's context, minds and emotions do change. They just do. That's just how we are as human beings. So those are the two OG ways. There's a third OG way that I think is actually much more successful. And then we'll talk about the fourth that we use a lot. yeah, we'll discuss some of the others that come up, I'm sure, because there are other ways, I think. But the one that I like the most,
Kim Talarczyk (12:13.777)
Mm-hmm.
Asia Orangio (12:42.56)
is actually not, I don't talk about this whole lot, but this is an OG way. It's called GRR, which is gross, gosh, okay, so it's actually GLR technically or GCR, but I guess you could say GRR technically as well. But we're looking at gross customer retention. Another way to think about this is gross logo retention. So if you have an account with many users in it, you'd look at the account and not all the users.
Kim Talarczyk (13:00.721)
Mm-hmm.
Asia Orangio (13:12.354)
But what we're looking at is out of all of the customers you've ever had, because it's gross, not net, out of all the customers you've ever had ever, what percentage of them do you still have today? And the number we wanna aspire for is more than 60%. Now, this is an old school way of doing it. I actually think that this is far more like a better indicator because it's not based on vibes and it's not based on
how someone feels about something or like what they're imagining to feel in that moment. It's looking at out of all the customers you've ever had paying customers, what percentage of them do you still have today over X timeframe? Now X timeframe is an ever increasing number for as long as you're in business, you will have a gross customer retention rate. And
Kim Talarczyk (14:10.705)
Mm-hmm.
Asia Orangio (14:12.126)
it will increase or decrease over time. Hopefully it's increasing over time. So if you're just starting, and if you think about everyone who's ever paid you money, how many of those do you still have today? Now if you look at the first six months of your business or the first 12 months, the number might be well below 60%. It might actually be kind of sad looking. But then once you get to the first year or two years, you might see a much healthier number, hopefully over 60%.
But the point of that is in those first six months, you might not have product market fit. And that's kind of the point is if it's less than that 60%, you might not be there yet and that's okay. But I think it would be unfair to say that you have product market fit, but your gross customer retention is well below 60%. That's what I think is unfair. Because what that says to me is if it's much less, then it's probably, we probably targeted the wrong people.
Kim Talarczyk (15:00.369)
Mm-hmm.
Asia Orangio (15:08.17)
Maybe we had the right people, but the wrong product, or we didn't have enough features in the product. So I think it's fairer to say we're working towards product market fit rather than we have it, we're there. I don't think that you're ever really there to be honest, but I think you have stronger product market fit when it's more than that 60 % gross customer retention, which is different than the next KPI that we'll talk about here in a second.
Kim Talarczyk (15:20.079)
Yeah.
Kim Talarczyk (15:34.628)
Yeah, but I like that one because then over time, the idea being even if people are dropping off early on, but like you said, you're improving your activation rates and your turn rates over time, that is compounding. And then you may then lose, say, you your five years in business and you may start losing those early, you know, people from three years ago. But the idea being you have picked up more and more business.
that is staying with you for multiple years. Yeah.
Asia Orangio (16:05.634)
longer. Exactly. And, and it's not based purely on vibes, which is what I like about it. Like this is based off of like to me, product market fit says, again, you, you have customers who are willing to pay for the product and stay for a time that makes it mutually beneficial for you both. And traditionally speaking, that's going to be more than a year. So anything less than a year and
Kim Talarczyk (16:13.904)
Right.
Asia Orangio (16:34.522)
It is already super expensive to acquire customers. Most marketing cycles take three to six months. So anything less than a year is not going to necessarily be profitable for you unless you were just charging bukkus of dollars. And the cost for acquisition is pretty low. But ideally, you've got customers that are sticking around for longer than 12 months. And most of the customers that you have stick around, and not most of them are churning, because that creates a very unsustainable business model in general.
Kim Talarczyk (17:02.077)
Mm-hmm.
Asia Orangio (17:03.096)
But yes, that's also why I like GCR. The next one, you and I have talked about a bunch and we talk about with founders all the time and often they don't know what this number is, but we are showing it to them for the first time and that's net revenue retention rates. So NRR, we've talked a lot about NRR. Do we have a whole podcast on NRR?
Kim Talarczyk (17:23.377)
We do. I think, or yes, we'll have to dig that one up and link it.
Asia Orangio (17:25.166)
feel like we do, right?
Asia Orangio (17:30.518)
Yeah, so NRR, if you're new to me and my content, then this might be the first time you're hearing about it, but we have tons of content about this. NRR is one of the most critical KPIs for understanding the health of your business. So if you have a recurring subscription model, so you're charging recurring revenue at...
12 months, you need to have at least 80 % net revenue retention rate. And what that means is all of the revenue that you acquired 12 months ago, you need to have retained from that specific cohort, the same, like you need to have at least 80 % of that revenue that you acquired. it's October, 2025. In October of 2024, how much of that revenue
do you still have today from that specific cohort? And the reason why that number is so important is because it tells you how much of that revenue do you still have today. If it's more than 80 % average 12 months, you're gonna have a much healthier business. It's gonna be a little bit easier for you to grow than it will be if you were less than 80%. 60 % is like your...
crawling, like growth is crawling. If it's like 60 % average 12 months. If you use ProfitWell, which most bootstrappers I know are gonna be using ProfitWell, if you don't use ProfitWell but you use Stripe, Stripe has this number as well. And it can show you, you can pull up this chart right now and you can see what your 12 month net revenue retention is. If you have an annual, like if you're primarily annual deals and annual subscriptions, then
you're still going to look at NRR. You're just going to look at the 13th month. So 12 months will probably say like 100 % or whatever, but your 13th month is what's going to show you the real number. Now, if you're primarily annual deals and annual plans, you're still beholden to NRR. It's just your timeframes are going to look a little different. And it's going to be slower for you to know what net revenue retention is based on tax law and state. So I learned recently, for example, that
Kim Talarczyk (19:24.209)
Mm-hmm.
Asia Orangio (19:47.875)
just because you sell an annual plan, you might only charge monthly. You might not pay all of that money upfront. There might be some laws locally that restrict you from doing that. But for the most part, you won't know what your NRR is until the 13th month. So that's gonna be what you use instead. But even still, you're still beholden to it. Like it doesn't make sense to sell an annual plan and then most customers churn from the annual plan at the 12 month mark. So you're still beholden to it even if you're annual.
And also there are some businesses that they sell annual plans by every two years, and same thing. So you're gonna be 25 months, rather than 24 months. But that's what we're looking for at that mark. Anything less than 80 % is gonna feel like a struggle bus. If you're less than 50%, you might see declining or contracting growth, meaning like,
Kim Talarczyk (20:29.191)
Mm-hmm.
Asia Orangio (20:45.23)
MR is going down, like you're losing money. So the goal here is to, we've talked about what causes this before, but the goal here is to look at product strategy, to look at go-to-market strategy, to look at pricing, because it's probably one or all of those things. Similarly, activation could also be a thing here, but chances are if you're less than 60 % at 12 months,
it's probably something more fundamental and foundational like pricing, product, or go to market. But that to me, NRR is one of the best indicators of product market fit because customers who aren't good fits, strategy that doesn't align product wise or go to market wise, like that's quite literally what product market fit means. So if you have poor NRR, probably, like there's something wrong with product and market in some kind of way.
Kim Talarczyk (21:42.878)
So how would the GLR number, which you said 60 % and above you're shooting for, And NRR, 80%, above 80 % when you're specifically looking at product market fit, like are those two numbers generally if one's bad, the other one's also bad?
Asia Orangio (21:49.038)
Mm-hmm.
Asia Orangio (22:02.166)
it's very possible to have one that is healthier than the other, depending on the stage of the business. So net revenue retention, is looking at, that's looking at a cohorted sample of revenue over some period of time. So you might have, for example, gross customer retention is looking at all customers you've ever had ever, right? And that number,
is slower to change than your net revenue retention. So net revenue retention, let's say you launch new pricing and it's like super aligned with your product, your market, the model, all the things. Your net revenue retention, because you can look at it, it's, it's cohorted by nature. You can look at any timeframe and any time slice. So let's say you only look at the last three months and you track net revenue retention month over month until you, until it's a year in the future. That could actually be far better.
and way better performing than gross customer retention because gross is looking at everything, it's the gross. Whereas net is looking at slices. Could the opposite be true where you have poor net revenue retention, but great, yeah, the opposite can be true too. Maybe you push a new pricing update, maybe you push a product feature that's different or like a product update that kind of changes everything. Your gross.
customer retention might actually still be healthy, but maybe net is not looking so good. And it's because you made a recent change and now you're tracking that through the future.
Kim Talarczyk (23:36.667)
Right, a newer cohort of customers now is kind of falling off because this doesn't fit right. That makes sense. So it makes sense why you really want both numbers. Yeah.
Asia Orangio (23:40.448)
Exactly.
Asia Orangio (23:46.753)
Yes, yes. In some of my CMO work and board work, gross customer attention came across my plate and I was like, that's an interesting KPI that I've never heard of. it makes sense. It also makes sense too why the bar would be a little bit lower because if you think about it, like it might take a company one to two years to really land this and to get it right. But once they do get it right, they should hopefully see it increase.
Like the rate of increase should hopefully happen a little faster because it's a law of averages, right?
Kim Talarczyk (24:20.621)
Right, and ideally you kind of want to see like a graph slowly improving over time, right?
Asia Orangio (24:27.382)
Right, right, exactly, exactly. So those to me are better indicators, far better indicators of product market fit. And the reason why is because they're not looking at us a time slice, least not, know, NRR is something that you do track over time and you can do look backs. So you can look back at the last 12 months, for example, and see what the average was.
Kim Talarczyk (24:36.541)
Hmm.
Asia Orangio (24:53.538)
But similarly, you can make changes now and then track how it goes for the next 12 months in the future. And that's why I like NRR. But those, think, are still better because it's not dependent on vibes, wishful thinking, aspirations or imaginations of customers, and not to say that they don't love you or are lying to you. It's just more, think, when you ask customers how they feel about product.
they're gonna be raving fans, like you're gonna find the raving fans, you're also gonna find the lukewarm people, but also depending on when you send that survey is going to determine how they feel about it. So if you're sending, we tend to see very high scores a month or two after someone getting onboarded and deploying the product and they're like, it's like the honeymoon phase. They're so excited, they love it, they think it's great.
And then send that same survey eight months later. Do they still love it? Because you might find that the score changes.
Kim Talarczyk (25:53.042)
Right, or that person may not be a customer anymore. They don't get the survey and you have no way of really indicating that fall off and that's those survey numbers.
Asia Orangio (25:56.856)
They might have already turned. Exactly.
Asia Orangio (26:02.862)
Exactly. So that's why I like NRR and GCR because, or GLR, because they show long-term impact. And that's actually how you determine product market fit, in my opinion. I think it is folly to say that you've got early product market fit when you've got like five customers and it's been six months. I just don't think that you can say that yet. Maybe that's just me.
Kim Talarczyk (26:25.701)
Yeah. Yeah.
Asia Orangio (26:29.038)
Because what we find is fast forward 12 months and actually maybe you had a little bit of product market fit, like a tiny bit, but actually, but like not really, but you wouldn't know that until 12 months from now. And then this thing called hindsight bias kicks in, which is like, well, now that I know what I know, we didn't really have product market fit, but we have better product market fit now. But the thing is, is hindsight's not always 20-20. That's why I'm telling you now, if this is you, if you're in this seat, this is how you have
much more reliable ways of determining if you have product market fit than not. And also understanding that your numbers will fluctuate. So if you're early, your GCR might be 100%. And then six months, 12 months from now, it might crater. But you're gonna be figuring it out as you go. So that's my word of caution in general. There are a couple of other things that I look for, but there are lot more, I don't wanna say that they're nuanced. It's more like,
Kim Talarczyk (27:10.301)
Mm-hmm.
Asia Orangio (27:27.946)
They are in isolation, not necessarily the best things to only look at, but they are like other hints. But I digress. That's kind of what I think about when I think about measuring product market fit.
Kim Talarczyk (27:40.381)
Yeah, I like that.
Asia Orangio (27:44.143)
So that brings us into other like softer indicators. I say softer because I don't think that this is enough. Like I don't like, I think you should look at GCR and NRR and use that like those as your litmus test. like, not, also, I want to be careful here too because I'm not saying don't do NPS and I'm not saying don't do the product market fit survey. Still do those things. I think they are still helpful to understand. I wouldn't, I wouldn't, you know, like put all chips on those piles, but
I do think that there are other things you can look at. we, mean, turn is an obvious one. Monthly turn rate, monthly turn rate is a, is a simple, you can look at revenue turn rate. You can also look at customer turn rate, either one of those. Ideally, if you're a subscription based SaaS, which most of them are, if I'm not mistaken, I mean, you're looking at, especially if you offer like a monthly plan, you're looking at ideally less than 5 % month over month. And I believe annual plans factor into this too, but I digress.
That to me is a pretty good indicator of, okay, yeah, like if it's less than 5%, Ramly John actually, an activation expert, he actually says less than 3 % is best. We also look for other typical metrics like we're converting customers pretty well. So this speaks more to like sales and marketing and also a little bit of like the product experience. But if you're primarily sales led, we wanna see a pretty good close rate, at least 30%.
Kim Talarczyk (28:52.978)
Hmm.
Asia Orangio (29:11.32)
could be above. Now this could be more about the sales process than it is about product market fit. But generally speaking, like if we know that we can reliably sell this, then there's something there. Like again, it's a softer touch point. It's not like a let's put all of our chips in this basket. I think GCR and NRR are gonna be way better litmus tests for you. But sales close rate tells us a lot about our ability to sell something. Now again, this could be more indicative of our sales process.
There are just some sales teams who can just close at really high rates, like 60%, 80%. But it's because they, I mean, it could just be because like they're incredible salespeople, which we've seen that before. Where sometimes like the sales team is a little too good. And then people turn later because they're like, oh, that wasn't what I thought it was. Or maybe I was sold something that I, you know, wasn't actually accurate. That can happen. We've seen that before. But so monthly churn rate, monthly customer.
Kim Talarczyk (29:52.477)
Mm-hmm.
Kim Talarczyk (30:02.172)
Mm-hmm.
Asia Orangio (30:09.518)
return rate and then close rates. Similarly, if you're more product-led, we might look at trial conversion rates into paid or freemium conversion rates into paid. And that tells us a little bit also about, like this seems to be selling to some degree. Now, whether or not they retain is a different story, but there seems to be something here in terms of our ability to at least convert people and sell it, which either the sales team is doing or the product is doing or both.
Kim Talarczyk (30:39.727)
At what point when you're looking at those things, are you pulling out maybe different segments of the market?
Asia Orangio (30:48.098)
Great question. I think at every stage, so.
You've seen me do this in some of our analysis work on the growth side, but also in survey work, like when we're analyzing survey responses. I think it is absolutely critical that you are looking at these numbers, not just as a whole, but also cohorted. I gave a talk at Microconf and this was Microconf Atlanta. I want to say it was like a year, two years ago now. I gave a talk and it was about like the top
growth levers that you could pull to help grow your company today. And I covered things like NRR, et cetera. And Ben Chestnut, who was a founder, one of the, believe, co-founder and CEO, creator of MailChimp, they had sold Intuit, I want to say, gosh, has it been like three or four years now? But Ben Chestnut was at Microconf. He's based in Atlanta, as am I. And at the end of my talk,
is his fireside chat with Rob Walling. And he said, I just want everyone to know that Asia's talk that she just gave is cheating. Because when I was doing this type of stuff, I had to do it manually. So he was talking about how he was calculating NRR manually with spreadsheets, which is a giant pain in the ass. Like it's just a pain in the ass because you can't just look at one month and then multiply it by 12 or whatever and get your NRR.
Kim Talarczyk (32:02.589)
you
Asia Orangio (32:23.854)
You have to look at like, okay, how much revenue did we get from that month? And of those people, how much should we get in the next month and then the next month and then the next month after that? Like you have to actually measure it. You can't just assume because people can drop off at any point in the journey. So he's like, he's manually measuring this. And then he said, and then he took it a step further. He wasn't just calculating NRR. He started cohorting it, meaning I'm not just looking at this month. I'm also looking at this month.
and these types of customers or these types of plans or these types of buyers or users or whatever, which is a whole other level of specificity. And I actually don't know if he's a data guy, but I can imagine if you're not a data guy, that would be very challenging. You'd have to work with someone, to do this. Someone technical, someone who can kind of like an analyst or who can act like an analyst. But all that to say,
Yeah, he he his point was you have software now that can help you do this things like profit well, but probably more like chart mogul. I'm not sure if Stripe can do cohorts, but his whole point was like, yeah, like look at these numbers and segment them like it's impossible to or it would be unwise to look at this at face value. You really got to understand the different buyer types. And the only way to understand that is to collect that type of data. Ideally, when someone signs up or books a demo or whatever it is.
that data is getting put somewhere and then you can actually do that type of analysis later, like down the road. But honestly, at every stage, we really shouldn't just be looking at like bird's eye. mean, bird's eye is super helpful, but what we might find is there are certain segments that just far drastically outperform others. So for example, in my fractional CMO work, there are two segments that we are targeting. There's one segment that
through no fault of anyone really, their NRR is just much worse. This is a segment that tends to go out of business. And so when that happens, I mean, obviously, they cancel the churn, but they cancel and churn everything. And it's not necessarily something that we can fix all the time at least. There are resources that we provide that help these business owners. But this is a...
Asia Orangio (34:47.689)
It's a riskier segment, but it's also, for better or for worse, a more passionate segment at the same time. The second segment, however, the ones who make it and are more established and are larger, their net revenue retention rate is excellent at 12 months. It's usually like 112%. It's like really high. So if we were to look at everything blanket,
Kim Talarczyk (34:54.62)
Mm-hmm.
Asia Orangio (35:14.412)
Like if we were to look at everything like high level, we'd assume that our NRR was like not that great. Cause if you average those two segments, you get a 12 month NRR that is like kind of barely ticking along. But if you just, if you, if you look at this segment though, that's performing really well, that kind of tells us like, maybe that's a segment that we should focus on growing, which is exactly what's happening. Like we're focusing on growing that segment. In the meantime, we're implementing things and practices and resources to help the other segment. But understanding that a lot of that is probably out of our control.
Like a lot of it we won't be able to fix. However, we can certainly provide resources to help that first segment. But really the second segment is what we should focus on expanding. And that's the type of data gathering that helps with decision making when it comes to where your business goes next, how you think about growth, how you think about go-to-market, all the other things that kind of fall out of that. But yeah, to answer your question, in terms of like when we start segmenting, as soon as humanly possible.
Kim Talarczyk (36:04.53)
Mm-hmm.
Asia Orangio (36:11.006)
even understanding churn based on the type of plan that someone's on or the segment that someone's in. Even if you can't do NRR quite yet, just monthly churn kind of helps a lot. Like, so, and sometimes some, certain segments will have net negative churn because they're actually retaining and expanding faster than they're churning, which is incredible. Like I've worked with a couple of companies that have had net negative churn. It's really cool to see. Like it's kind of wild too, cause like,
Kim Talarczyk (36:35.25)
Yeah.
Asia Orangio (36:37.088)
In profit well, think the bar like literally like goes below like the X line and you're just like, Whoa, like that's possible. And it's like, yes, it is possible. And it's very awesome when it happens. It's rare, but when you, if you can do it, it's great. Cause you're quite literally growing with doing very little. like in, know, obviously it took a lot of work to get there, but once you, once you do get there, it's kind of just taken off on its own. Yeah. Great question.
Kim Talarczyk (36:43.867)
Mm-hmm. Yeah.
Kim Talarczyk (37:00.733)
Yeah, so then, mean, you're obviously focusing on product market fit. You may have product market fit with a certain segment and not with another. So splitting it out early is important, not just thinking, oh, everything looks too low. But when you start segmenting and cohorting, OK, now you can specifically look at one area. And maybe those metrics are better.
Asia Orangio (37:24.376)
Yeah, exactly. And I think there are certain scenarios where we can say we have product market fit despite the numbers. So I think in that, in our context, it's funny, because it really does depend on who you ask. Some would argue that you don't have product market fit, because that market can't sustain the product. But at the same exact time,
We also know that when people churn, the majority of the reason is not because of us. It's because of things far beyond our control. I think when it comes to stuff that is within our control, think it's a little different story. But I think that there are some people who would argue that technically that's first segment that I mentioned earlier, the ones who churn because they go out of business. I think there are some who would argue that that's actually not the product market fit. Despite them getting value out of the product and canceling,
for reasons totally outside of our control. So there are some who would be in the camp of like, technically that's not product market fit. But then there are others who might say like, I mean, yeah, like as long as it's nothing that you did or is a context that you truly can't control it, it might be okay actually. So TBD. But what we can say is that other segment has much better product market fit. Like it checks all the boxes.
Kim Talarczyk (38:38.651)
Mm-hmm.
Asia Orangio (38:47.054)
we know that that's like, okay, this clearly is showing signs of strong product market fit. We should go in this direction and continue to expand there. So we can't at least say that. I think the first segment that I mentioned, yeah, there are some who would argue that we don't, and I think that that's fair. And then there are some who would say like, well, technically, maybe you do for these reasons, but.
Asia Orangio (39:11.192)
Cool. What questions come up for you? What did we miss?
Kim Talarczyk (39:46.376)
So maybe you could talk a little bit about when clients come to you and us and like in the beginning, I think you said, know, people, they assume they have product market fit, but they probably didn't look at some of these key metrics that you talked about. So like, what's the cautionary tale then? You know, what are they now?
Asia Orangio (40:11.022)
Yeah.
Kim Talarczyk (40:14.171)
What are they now doing and what does that look like when they're not thinking, maybe we don't have product market fit.
Asia Orangio (40:22.286)
Totally. So when companies work with us and this is the first thing that they're kind of shouting from their tops of like, listen, we've got early product market fit. And I am like, you you've got five customers or whatever. It's like a handful of, especially the ones that are like less than 20, I would say. I do think it depends on the ACVs here and the LTVs here.
If you've got 20 enterprise clients, I think that that's a little better than you've got 20 like SMBs or VSBs, like very small business customers. I do think that's a little different or even consumers. But what I temper is, okay, it sounds like you have feelings of early product market fit. And I'm not here to debate, you know, like if you do or not. But what I will say is,
there are signs that tell us like if you quantitatively have product market fit, but similarly what we might discover in our work together is that there needs to be more work that is done. And so I like to set expectations for, okay, let's define product market fit, because this is how I see it and this is what I've seen in my work and just the businesses that I've helped and worked with.
If we find that the numbers are less than this, my assumption is that we probably don't yet, but we have maybe early signs, and those early signs can be leveraged to really test a few hypotheses. And that, I think, is better to set expectations for any execution work that potentially happens after, whether we work with them to hire or build a team, or whether we work with them to find an agency.
What I like to do is set expectation for, I know that you feel like this is happening, what our work is going to reveal is how true is that? And also how much work again needs to be done to build more confidently towards true product market fit. Excuse me.
Asia Orangio (42:40.654)
I think the danger here is, when people come to us and they're like, we've got product market fit, we're ready to go, is the danger is that they're thinking we're gonna stand up campaigns, we're gonna do sales, we're gonna do stuff, and it's going to convert, and it's going to retain, and it's gonna be totally profitable. What I find is that that's not necessarily the case. you don't actually have as strong of a product market fit as you think you do,
this stuff isn't gonna fly off the shelves like you might feel like it will. And what I find is like companies that have like true product market fit alignment, it just flies off the shelves. That I will say though, to be fair is more like traction. But even still, I think early product market fit is like, I talked about some of like the softer touch points. You also hear it in conversations with customers. You hear it in like, my God, where have you been all my life? Or like, this was a no brainer for me. Like I was sold immediately.
You hear that type of stuff in sales conversations. There are some industries where customers just aren't like that. Like you're never going to hear that in a million years, even if like your product is bonkers, amazing. And like, it like blows people's minds actually. But maybe that market is like stiff upper lip or whatever. Like there are certainly markets that like you'll never get that response. But you still will hear it in some cases. In most cases, I find like you'll hear that. All I to say,
The cautionary tale, I think, is just making sure that really the company is aware that you might feel like this is true. Our work might reveal that you have more work to do and you just have to be open to that. And my job is not to crush souls. It's really to help enable people to get to where they want to be. But I also have to be realistic. And if what we're seeing is like, gross customer retention rate is actually way low,
Kim Talarczyk (44:21.221)
Mm-hmm.
Kim Talarczyk (44:25.277)
you
Asia Orangio (44:37.29)
it might get better if net revenue retention is also low, then that makes me think, okay, we're not there yet, but here's what we gotta do to get there. And here's what it's gonna feel and sound like. And kind of giving them more truer, what's the word, North Star ways to measure versus vibes which don't actually align with what the reality is.
Kim Talarczyk (45:04.881)
Right.
Asia Orangio (45:05.826)
But I think most founders and most founding teams are very optimistic. And I think that that's great. We don't want to get rid of founder mojo. We don't want to get rid of team optimism. All that's awesome. But what we do want to say is, hey, this might take a little bit longer, or this might require more energy and effort than maybe what you're imagining. And it might not be as simple as turning on ads or launching a campaign or launching, period.
We see that too, where companies wanna go stir, like, we've got early product market fit, we're ready to launch. Help us with launch. And then we're like, okay, but just to prepare you, our work might reveal that you might not be ready for a big launch, but a soft launch,
Kim Talarczyk (45:49.82)
Right. And we've talked about this in other episodes of being too early in certain channels and companies that want to prioritize execution and go and hire an agency, whereas sometimes that's not money well spent off the bat. Yeah.
Asia Orangio (46:06.124)
Exactly, Yeah. All of those are in service of, is your product market fit as strong as you think it is? Are you actually ready? And then, I mean, there's certainly argument for just trying it. Like there are some companies who are just lucky. Like they're at the right time, place. We've got things kind of aligned in a way that is not hard to replicate, but sometimes luck does strike. Like you can just do the thing.
Kim Talarczyk (46:34.669)
Right. And of course, in early stages of companies, there's a lot of just throw shit at the fan, see what happened. mean, it's it's a mess in the best way, you know. So of course, there's that.
Asia Orangio (46:42.348)
Yeah. Yeah.
Totally. But I would say most companies are not that. Most of the companies that we work with are not that. It's a little bit more of a struggle. There's a little bit more uncertainty. There is sometimes more risk. And they're really trying to figure out how do they land the plane without it falling apart in midair. So there's a lot of that, of course. Most of it, I think, is that. I think of the book, Competing Against Luck, all the time, less from a jobs perspective and more just from the title. It's such a great title. And I think about it of like,
You want to compete against luck if you can. don't want to rely on luck. Relying on luck is the worst plan. There's a phrase that one of my old VP of Ops used to say, VP of delivery, in one of my consulting gigs way back in the day. He used to say, the harder I work, the luckier I get. I think about that all the time, actually. It lives right in front of my brain. That's exactly how we should be thinking about.
Kim Talarczyk (47:17.949)
Mmm.
Kim Talarczyk (47:38.557)
Mm-hmm.
Asia Orangio (47:45.219)
the early days and like if you actually have product market fit or not. So if you're listening to this and you're like, yep, we want to work with demand maven. We have early product market fit. I just want to be prepared. If you, if you jump on that discovery call and you're like, we've got early product market fit, I'm going to ask you, what's your net revenue retention rate? How many customers do you have? What's your gross customer retention rate? And if you look at me with blank stares, I'm going be like, you don't know actually, you don't know if you have, you know, early product market fit. You've got
intuition, it would be more accurate to say, I have an intuition or I feel like we have early product market fit. It's far less accurate to say we have it unless you've got some numbers to back it up and prove it. So that's just me being a stickler though. And also at the same time, set expectation because again, I think a lot of companies come in and they're like, they're very optimistic, very glossy eyed. They've got the rose colored glasses on.
You know, like, yep, we've got it. And they've got their five customers, but they've never truly like fully gone to market. And that's where you really know. You also, this is the other thing too. We didn't talk about this, but I also never want to hear we've got product market fit if you've never charged.
Kim Talarczyk (49:01.213)
Mm.
Asia Orangio (49:02.946)
We've seen that and heard that before too, which is like, don't say that. Don't, don't be that person. You literally don't know. I would also argue this is going to make some people mad. I'm also going to argue that even if you have design partners, you don't know actually if you have product market fit. So a design partner is when you usually like, you do this with like a very large company because you're maybe you're trying to sell to enterprise. but like,
Kim Talarczyk (49:04.771)
Right. Don't say that.
Asia Orangio (49:32.225)
Design, you might find like a handful of design partners, people who are willing to work with you on your product strategy and like product development. You might not charge them, but you'll like, you'll build like exactly what they need in hopes that you walk away with a product that you can go and sell to someone else. Design partners are such a cool idea. Like it's a really cool idea. But until you are paying a stranger who's not getting something for free from you,
You can't tell me you have product market fit. I'm sorry. Like you go sell it to strangers and see if they'll buy it. And if you're like, well, the only person who's going to buy is Microsoft, then you only have product market fit for Microsoft or you only have product market fit for Google. And that's it. No one else is going to buy this. I don't know. Like, I don't think it's fair to say, we've got product market fit. We've got two to three design partners, but you've never sold it to a stranger. So that's my other like, that's like, it drives me nuts when I hear that. And I do hear that there are people who are like,
Kim Talarczyk (50:12.963)
Yeah.
Asia Orangio (50:31.856)
yeah, we've got it. We've, you know, but, they've, they've never received a single paid invoice from anyone that wasn't a design partner. And that drives me crazy.
Kim Talarczyk (50:36.357)
Right, right. Or a hundred people just downloaded my free tool.
Asia Orangio (50:43.094)
Right, but they're not paying, right?
Kim Talarczyk (50:45.157)
I have 100 people, yeah, but will they pay for it?
Asia Orangio (50:48.718)
Right, you have leads, you don't have customers. Totally. Yeah, no, it drives me absolutely crazy. But I think it's, I get the optimism. I also think a lot of founders too, especially if they're first time founders, people kind of want to skip the step a little bit. They want to get straight to, we have our product market fit, let's scale, let's grow, let's do the thing. But I caution so much skipping that phase because it is so critical.
Kim Talarczyk (50:50.673)
Yeah.
Asia Orangio (51:16.174)
I do think you should be charging as soon as you possibly humanly can because that is, when you actually know if you have product market fit. But until you get to that phase, until you're ready to actually start charging, you're not gonna know. And even when you do start charging, you won't actually fully know until like you're a year or two in. That doesn't mean that you don't market. That doesn't mean that you don't prepare. It just means when you hop on the call with Demand Maven, you're not like, we've got product market fit, but you don't actually know. That's all that means. I just wouldn't...
Kim Talarczyk (51:41.607)
Thank
Asia Orangio (51:45.25)
go shouting that from the rooftops quite yet. The better way to know is everything that we've already talked about. But then I do think that there is like a general intuition of this does seem to be flying off the shelves. However, intuition is often proven wrong with data. So we also still wanna use our quantitative ways, which we've already talked about. But yeah, I don't want that to mean like, okay, like that means that you do nothing. You still do stuff. You still make progress. But maybe we don't.
fully buy into that idea quite yet until we have the data. And then we can say, okay, yes, we have confirmed this, we validated this. But again, it'd be more accurate to be like, I have initial feelings of this or intuition of this. I think we have product-market fit. I don't know yet is more accurate. And then once you actually get there, you can say it.
Cool. Did we miss anything?
Kim Talarczyk (52:44.647)
no, I feel like that was.
I feel like we got everything.
