EP. 14: Can you measure product-market fit?
How do you know if you have product-market fit? You measure it! In this episode of InDemand, Asia Orangio, founder of DemandMaven, breaks down the different ways to think about measuring product-market fit and how to apply it in your Saas business.
The four measurements covered are:
- Cohort Analysis and retention rates
- Net Promoter Score
- Churn Rate
- The 4 Question Framework from Superhuman
- 1:25 – Yes, you can measure product market fit
- 2:25 – How to use cohort analysis, grouping customers using customers from the last 6 months, and looking to see if you’re keeping more than 50% of your customers.
- 5:30 – As your business matures you’ll be looking for 12+ months, or how many customers stay on for more than 12 months.
- 6:40 – Net Promoter Score (NPS)- You can measure N.P.S. by simply creating a survey asking, “on a scale of 10″ how likely are you to recommend the product to someone?” Then you can use this information, along with your background info about the customer, to identify what the customers with low N.P.S. and high N.P.S. have in common
- 950 – There are some downsides to Net Promoter Score. Because it is just one question, you don’t have a ton of insight into what is driving the score.
- 10:40 – Understanding churn, maintaining less than 5% churn is a sign that the business is quite healthy. There are a number of different ways to look at churn, from the percentage of users to percentage of revenue.
- Churn can be a great signal, but without more context it can misleading, so it’s important to think about the overall context around the churn.
- 15:40 – The four question framework.
- How would you feel if you could no longer use this product? With three options: Very disappointed, somewhat disappointed, and not disappointed
- What type of people do you think would most benefit from this product?
- What is the main benefit you receive from this product?
- How can we improve this product for you?
- If you have 40% of your desired customer segment saying that they would be very disappointed, that is a great sign you have product market fit
- 22:30 – Recapping the different ways to measure product-market fit
What’s up founders! And welcome back to the In Demand podcast where we talk all about how to reach your first $1m ARR. I’m your host Asia Orangio and I’m the founder of DemandMaven where we work with early-stage SaaS companies on reaching their very first growth milestones.
Let’s do this today is actually going to be a continuation of something that we’ve talked about before on the podcast. And it is going to be all about if you can actually measure your product market fit before I have discussed product market fit in terms of have we built the right product for the right market and right, of course, being defined by are they paying for it? And are they staying? Are they sticking around? Do they love it? Hopefully they love it. But product market fit is one of those things. Again, I have argued before that. It’s really not an end point. I would, I even don’t like saying it’s a milestone because it is something that is constantly fluctuating depending on the market or the segment that you’re currently acquiring at any given time. However, one of the big questions that comes up whenever it comes to part of market fit is what can we measure this? How do we, how do we know quantitatively is that possible?
And the answer is well, yes, actually it is kind of possible. And the fun part about this is that there are actually many different formulas and KPIs that we can use to identify if we have product market fit from a financial perspective. And then also we’ll discuss a little bit about what you can do from a qualitative perspective to identify product market fit. We’ve discussed how to know if you’ve got product market fit before, but today’s episode we’re really going to focus on again, the more quantitative side of product market fit. Okay. So can you actually measure product market fit? I would say the short answer is yes, there are certainly metrics, KPIs and practices that can give you a really strong sense of if we’ve achieved product market fit. Or if we have reached a certain strong level of part of market fit for a particular customer cohort or a segment.
And there’s, there’s a couple of ways I’m going to list out the first way is to do a cohort analysis. Now, cohort analysis, that’s a really fancy term for visit going to analyze a particular group of our customers or users, ideally customers. Ideally, these are paying customers that we’re actually analyzing. And a cohort is just a fancy term for, we are going to group a particular set or subset of customers by some, either it could be taxonomy. It could also be which another fancy word I apologize, but it could also just be, we’re just going to group people by some variable or some attribute that they all share together. So for example, you might have customers that, um, maybe one cohort could be, well, these are marketers or one cohort. Another cohort could be, these are people who have started a trial in the last 30 days.
Another cohort could be these are school teachers, or these are, um, maybe they are in the medical industry or what have you. There’s a billion, one different cohorts that we could technically name define. Maybe there’s a cohort of people who signed up for a particular feature of the product. That’s another cohort, so that those are examples of cohorts. This particular cohort is going to be people who have signed up for the product in the last five to six months and became paying customers. So the first cohort is going to be paying customers of the last six months. And if we have retained at least 50%, 50% being the least, because if it were less than we’d be losing more customers and we’re keeping, and if it’s more than 50%, then we are keeping more. If it’s about 50%, like exactly like on the nose, then it’s about the same.
It’s about breakeven, which honestly, there are many, um, there are many investors, VCs and other analysts who very strongly feel that even a 50% is very acceptable after six months. But the goal would be to see, are we acquiring at least 50% of our customers of our paying customers after six months of being customers. And eventually you get to a place to where, when you look at your cohort retention, after that six months, even up to 12 months and beyond you’ll hopefully see that the retention kind of flat lines a little bit, it gets flat. You see this big curve in the beginning where you see, you know, like 80%, 90% retention. And then after the first six months to 12 months, maybe you see it start to flatten out a little bit. And it flattens out around that 50% Mark, a really strong sign of product market fit.
It basically means that again, the customers that you’re acquiring are sticking around, which is, which is good. We want that. We liked that. We liked that retention. So the cohort retention analysis is one way and in your early stages. So for my earliest age, SAS founders, the first six months is really what you’d be looking for. And then as the product matures, and as the business matures, you’d be looking at 12 plus months. So how many people, how many customers stick around for longer than a year, another incredible KPI to be looking at? And this is a pretty simple metric or KPI to pull from a tool like ProfitWell or bare metrics or ChartMogul. So I highly recommend using one of those platforms. It probably already has that data inside of it, especially if you do have it connected properly and you do, uh, actively use it to just measure the progress of the product in general, but that data’s just kind of sitting there.
I’ve pulled these reports all the time inside of all of these tools. So in my mind, I can see exactly like where this is, but it’s pretty easy to, to go and pull it. In fact, I’m pretty sure if you were to go and look up cohort retention analysis inside of any of those products or on their blogs, they probably have our little report about it. Okay. After that, the second one is going to be net promoter score, net promoter score NPS. This is one of the, this is one of, I think the underrated KPIs or metrics or litmus tests, if you will, for product market fit, because it measures the overall happiness. And in other ways, the value add on behalf of any particular customer net promoter score is really a simple, it’s a really simple survey.
It’s probably the simplest survey that you can send a customer. Usually it sent an app, it could also be sent via email or chat, but it’s really simple. It’s on a scale of zero to 10. How likely are you to recommend X product to a colleague, a friend, someone and anything above eight is great. That’s really positive. And then I believe I could be wrong about this, but I think like six and seven, maybe actually also technically eight, I think six and seven are N they are, they are much more neutral. Um, and then anything, I think below five is like an attractor. Like it’s, you know, they’re, they’re not super gung-ho about the product, or there’s definitely a lot of room for improvement. There are many different scales for net promoter score that I highly recommend going and researching, but net promoter score can actually be a wonderful way of gauging, especially on the customer type.
What kind of, how much of a promoter is the customer? And on average, what is the global net promoter score and based off on segments, based off on any number of other data points, who, who is a true promoter, who has a really high average net promoter score, and then who is not, who is a much more of a tractor, these data points, especially when it’s sent with a tool that can help you, not just identify who actually like who literally, who like the customer itself, which customers gave you really high net promoter scores, which ones did not. And what unite, those who gave a really high net promoter scores. What about their customer journey or customer experience made that number so high? What about their demographics made that number? So high, same thing for psychographics, meaning what were they hoping to achieve? What, what were they thinking?
What were they expecting? What motivated them. And then, of course, demographics, meaning, you know, job title, industry role, all of these things work together to, um, provide some insight into what are some of the things that unites the people that gave us really high net promoter scores versus not. And again, I mentioned, if you’re using a tool that is strong at helping connect those dots, then you end up with even more data and insight opportunity than before, which is incredibly powerful. You can typically make pretty good decisions based off of that net promoter score. However, also does have some downsides. So for example, because it has just one question, it does not give you a ton of extra context again, about what influenced or inspired the score that was given. So a customer who might’ve had a relatively great experience up until one particular moment where something was not great.
And then that influenced the net promoter score. That’s where our net promoter score does get a little tricky. And there are other reasons why net promoter score on the w does have some downsides as with any of these frameworks. But if you were, if you were to combine it with some other practices, this could also be a really interesting way to, again, gauge product market fit. And the next quantitative Dana point, when it comes to product market fit, it’s probably one of my least favorites, unless it is applied with a certain level of diligence, because it’s really easy to misconstrue and that’s churn low churn, low churn numbers. When we say turn typically, we mean, these are people who continue to use a product up until they decide not to, upon which they cancel. And the cancellation is considered part of the churn. So churn numbers, turn rate, for example, it’s about how many customers you lose on average every single week or month, year, et cetera.
And then there’s of course revenue churn, how much revenue is actually churned it from, from the MRR total, but there’s, there’s user churn. How many users do we turn on average? How many customers would be turned on average? And then there’s course there’s revenue churn. How much money do we actually lose on average every given month? And what’s the rate of that. And here’s where churn gets a little tricky when you have really low churn. And I would say typically we’re looking at less than 5% for most. I hate to say traditional SAS, because what SAS is truly traditional, but for most SAS products, less than 5% churn is considered pretty, pretty healthy, less than 3% churn is pretty great. And then negative turn is just like, Whoa, Holy crap, teach us your ways. What are you doing to get negative churn? You know, tell us your secrets.
And with churn, it gets kind of tough. It gets tricky because churn can indicate so many different things and without proper context about why the churn is happening, it can be a great indicator of product market fit. It could also be a terrible indicator of product market fit. Here’s what I mean. You might have a customer segment that has incredibly low churn. They have just the lowest churn. It’s, I mean, it’s like two to 3%, and then you might have another customer segment that has really high churn. However, based off of the rest of the product based off of your funnel could actually indicate why the churn is as high as it is, or just the overall level of product market fit. So for example, one customer segment might have extremely high churn. And the first assumption at that might be, Oh, well, they just don’t have the best product market fit.
And I would generally agree, however, there might actually be other indicators elsewhere. So for example, that customer segment, let’s say your funnel requires a credit card to sign up for the product. And for whatever reason, one particular customer segment has no problem with that. They sign up easily. They they’re not tripped up by the credit card requirement and it doesn’t bother them in them at all. They might actually have a credit card on hand on file. They might be a leader in a team and have their own budget. And then this other customer segment might have a lot higher churn, but they also might not be a credit card holder. The credit card requirement might have an actual implication on the level of turn, whether or not they have challenges with the product or what have you, you know, of course remains to be discussed, but there are certainly other implications across the entire business and that ultimately results in the churn that happens.
So. I would say churn, I think is great just from a, a face level perspective, but to really understand it, it does require a certain level of diligence like any of these frameworks or KPIs or resources, but especially when it comes to making assumptions about product market fit churn can be a really easy, quick way. And at the same exact time, it can be a tricky way because depending on the customer segment you’re focusing on, or the cohort that you’re focusing on, you might find that some have incredibly high churn, some have incredibly low churn. And then of course there are other aspects of the business that impact that churn is the last activity or thing someone can really do with your product, assuming they don’t reactivate or re-engage later. What I mean by that is a cancellation. It’s kind of like, I wouldn’t say it’s the final, final straw, but it’s certainly one of those last moments that a customer experiences with the business, unless of course they reactivate later, which means that every single little turning point in every single little experience or, um, uh, engagement with the brand or the product itself could have had an impact.
And it’s really tough to know exactly how much of it was customer experience, brand experience versus product, and how much of it was just, you know, what this product didn’t meet my needs. That’s why I would say if we’re going to use churn as an indicator of product market fit, just make sure that you double it up with something else. And also some customer research, if possible. Okay. Finally, we are going to get to probably one of my favorite ways to measure product market fit. And the last way certainly has many different opportunities, but it’s to use a framework that measures product market fit. You might’ve actually have heard me talk about this particular framework, but I learned about it through superhuman and superhumans founder role actually found out about it through a few other various sources. Some I believe had to do with Sean Ellis, the author of hacking growth.
And if I’m not mistaken, it was either base camp or buffer who actually used this process as well. I could be wrong about that, but the framework itself is actually really simple. It asks just four questions. And by asking these four questions, we can actually dig a little bit deeper and retrieve a quantitative assessment of what our degree of product market fit is for a particular customer segment, which is incredibly powerful. The four questions are this. The first is how would you feel if you could no longer use superhuman or in this case, you’d put your product name. The choices are just three choices, and this is critical because some I’ve noticed some teams will change these options. And this is where I would say, I don’t know if it says statistically relevant or scientifically relevant at that point, if you change the framework in general, but the options are very disappointed, somewhat disappointed and not disappointed.
And this is very similar. It’s kind of reminds me of net promoter score. But what I love about this is disappointment level is a direct reflection of how much value someone is getting, and then therefore, how many other opportunities or options do they have? AKA what makes a product truly special? So in superhumans case, if they got mostly not disappointed, that says a number of things, it says they probably have other options. Superhuman probably didn’t allow them in the way that they were expecting. And it probably wasn’t solving the right kind of pain on the contrary. If they’re very disappointed, then superhuman probably has a very unique value proposition that they need to uncover. And it’s probably providing a very special kind of value to a certain kind of customer. The second question is what type of people do you think would most benefit from superhuman?
And what I love about this question too, is that it’s a psychological question. And it’s one that will imply what kind of person would answer, not disappointed versus very disappointed. Most people receiving the survey will answer with their profile. So if a photographer is completing this survey, they’ll probably say, Oh, well, a photographer will get the most value out of this product. And if it were a CEO founder, they’d probably say, Oh, well it would be a CEO, founder, someone just like me. Number three is what is the main benefit you receive from superhuman or in this case, of course your product name. Again, this is just an open text box, nothing specific here to put, but this gives you a hint at what that value proposition is. As I mentioned in number two, and then number four, how can we improve superhuman or the product for you?
And this gives a sense of out of the different kinds of customers who replied to the survey, what are they expecting in order to stay or get more value? And this can actually be critical when it comes to deciding and determining what customer segment to ultimately focus on. And then also what degree of product market fit do you currently have with a segment? Some people will answer not a single thing. I absolutely love this product. It’s great. I wouldn’t change anything. And then others might have an essay, a list, uh, what feels like a novel of features and requirements, even though they might love the product, it might actually cost them more to keep them. And that’s something that I think this survey does really well. It helps identify who those key people are. What are, what are the value propositions or benefits they’re currently experiencing?
And then finally, what is the laundry list of things that they’re looking for, if anything, and this can kind of give you a sense of, Oh, we’re really strong in these particular customer segments, but maybe not so much in these, but of course this would not be a true framework. If it did not have some degree of quantitative data mining and reverse engineering when it comes to figuring out, okay, but how do we effectively measure this? There’s an article that I’m going to list, uh, or link to at the bottom of the description of this podcast. Hopefully this works in Spotify or wherever it is that you’re listening today. But the article actually goes through superhumans exact process for how they calculated this. And basically what they ended up doing was they have the entire dataset, which is great, but they really wanted to focus on the people who were very disappointed.
So they ended up taking that particular part of the pie, just sifting out the rest of the data just for now. They just wanted to look at this one cohort who said very disappointed. And then from there they broke down and measured. What kinds of customers said very disappointed. And then from there they were able to reverse engineer. What degree of product market fit did they have within every single customer segment? The ultimate number to reach in this particular case was if you had 40% of your desired customer segment or target market that said very disappointed, then you likely had really strong part of market fit, or you were well on your way. If you were less than that, if you had 22% or 32% or whatever other number, then you probably didn’t have super strong product market fit role in the article that I’m going to link to on first round role talks about how close he at the time, at least of writing the article was to product market fit.
And then also he talks about the other signals that he noticed throughout the process. And then of course there are breakdowns of exactly how he broke down these numbers and how he really sifted through the data and, and focused in on one particular data set. But it’s actually, it’s pretty inspiring. And it also makes product market fit feel and seem at least on paper, a lot more achievable and also measurable, which I absolutely love. Okay. So as a quick recap, we just talked about using a framework to measure product market fit. And the one that I’m going to mention is the one that superhuman did use highly recommend leveraging that process throughout your founder journey in general, it doesn’t have to be when you are super early stage, this can be for any stage, any stage can use this particular framework. We talked about cohort retention analysis.
So leveraging the data that you already have and analyzing, if you keep at least 50% of paying customers after the first six months to 12 months, and then of course beyond then there’s net promoter score. We can use net promoter score to identify, engage who is getting value out of the product and who’d recommend it. And then finally we could use churn again, a tricky one because there’s lots and lots and lots of, uh, implications when it comes to churn. But churn can also be a really quick and easy way to gauge what your level of credit market fitness. Thank you guys so much for listening. I, as per usual, I always hope that this is helpful and that it helps break down some ways that you can start to quantitatively measure your product market fit. And also a little bit of qualitative stuff as well, especially with the superhuman product market fit framework. Of course designed, I think originally by Sean Ellis. So I want to make sure to give credit where credit’s due. However, hopefully these are some, again, due to just some simple ways to make it a little bit more crystal clear, even though product market fit is something that is, can be at least shades of gray. Thank you again for listening. I hope this was helpful.
As always thank you so much for spending this time with me to learn more about how to reach your growth goals for your SAAS business, head on over to demand maven.io. You’ll find all kinds of free resources, articles, and content. Don’t forget to subscribe if you haven’t already and I’ll see you at the next one. Let me know what you think. I am always available on Twitter, @AsiaMatos. Thank you so much again and have an awesome day.