The Exchanges

Q Before we dive into kind of the system and the mechanics itself, I am really intrigued because you mentioned a couple of different cases of product market fit there and lack of in Yammer, gettable, and ease. I guess specifically with regards to PMF, what were your lessons on experiencing the product market fit of each engine, maybe starting with Yammer and progressing?

A At Yammer, we struck gold pretty early on. We were one of the first products to bring consumer thinking to the enterprise, putting the user first instead of the buyer. Yammer used the same tools that consumer startups were using to evaluate the success of our product. We measured virality, growth, engagement, and retention, and we ran A-B tests to make sure only the features that moved the needle made it into the product. We could tell from these metrics that we definitely had product market fit, but we never had a leading indicator for it. My time at Yammer taught me the importance of validating your ideas through metrics and testing. Gettable, on the other hand, was a lot different. When I joined the team, we didn't have a product yet, let alone paying customers. We didn't have any metrics to track, but we did have a lot of ideas to test. So I spent my time talking to customers, literally wearing hard hats on job sites to watch them work, And iterating through many MVPs in search of product market fit. Though we never found it, I learned the importance of staying close to your customers and making them a part of your process. Ease, also a very different experience, was a rocket ship on a roller coaster. Being in the cannabis space, we had to deal with a lot of chaos, and we had curveballs thrown at us every week. Sometimes these curveballs were more like cannonballs, like whe…

AI assessment note: “Yammer taught me the importance of validating your ideas through metrics and testing.”

Q Not at all. Listen, I'm excited for this one, but I do want to start today with a little bit on you, so hit me. How did you make your way into the world of startups first, and how did you come to found today Viable Fit?

A Sure. So my co-founder and I are identical twins, and we got our start in tech at around the same time. I went the engineering route, and he went the design route, but we both love products, so we've been focused on product for a long time. Back in 2006, we co-founded an app development agency up in Portland, Oregon, that built prototypes for super early stage products. We worked with dozens of clients to help make their vision come to life. Eventually though, I got the itch to focus on a product for the long haul, instead of just helping founders prototype their initial MVP. So I moved down to the Bay Area in late 2010, and by early 2011, I was working at Yammer as an early engineer. When I joined, we didn't have a designer yet, so I brought Jeff on board to help on the design side. We were somewhere around 30 employees at the time, but it was pretty clear that we had product market fit, and we were growing like crazy. Two and a half years later, after growing from 30 people to about 550, we were acquired by Microsoft. But after the acquisition, I found myself in a 90,000 person company that not only had multiple products, but whose target market could be summed up as simply everyone. So I got the itch to switch back to the early stage, and I joined Gettable as its CTO. Gettable was a construction equipment rental marketplace that was still in its infancy. I spent the next fou…

AI assessment note: “Back in 2006, we co-founded an app development agency up in Portland, Oregon”

Q Okay, so I need to track it now, and PMF is, like, the best metric to track for these kinds of companies. I'm a startup founder, and I'm like, shit, I need to start tracking PMF. In terms of, like, putting that into a process, what's the one simple question, who do I ask it of, and what options do I give them?

A Sure, so we recommend that you send a survey out to all of your users, and this is generally right after they've been able to see the value of your product. So if you're a transactional startup, like a marketplace, it might be after their first purchase. If you're a enterprise SaaS company, it might be a couple weeks after they've been using it for a while. Same with something on the consumer side. So we asked one simple question at the very beginning of this survey, and that is, how disappointed would you be if you could no longer use this product? We give the user three options, not disappointed, somewhat disappointed, and very disappointed. This single question allows us to calculate your product's PMF score. Segmentation is key here, though. Some of your users might not actually be in your target market. Without defining a target market, And marking each user as in or out of that market, you won't know your true product market fit.

AI assessment note: “how disappointed would you be if you could no longer use this product?”

Q Okay, so I need to track it now, and PMF is, like, the best metric to track for these kinds of companies. I'm a startup founder, and I'm like, shit, I need to start tracking PMF. In terms of, like, putting that into a process, what's the one simple question, who do I ask it of, and what options do I give them?

A Sure, so we recommend that you send a survey out to all of your users, and this is generally right after they've been able to see the value of your product. So if you're a transactional startup, like a marketplace, it might be after their first purchase. If you're a enterprise SaaS company, it might be a couple weeks after they've been using it for a while. Same with something on the consumer side. So we asked one simple question at the very beginning of this survey, and that is, how disappointed would you be if you could no longer use this product? We give the user three options, not disappointed, somewhat disappointed, and very disappointed. This single question allows us to calculate your product's PMF score. Segmentation is key here, though. Some of your users might not actually be in your target market. Without defining a target market, And marking each user as in or out of that market, you won't know your true product market fit.

AI assessment note: “how disappointed would you be if you could no longer use this product?”

Q Not at all. Listen, I'm excited for this one, but I do want to start today with a little bit on you, so hit me. How did you make your way into the world of startups first, and how did you come to found today Viable Fit?

A Sure. So my co-founder and I are identical twins, and we got our start in tech at around the same time. I went the engineering route, and he went the design route, but we both love products, so we've been focused on product for a long time. Back in 2006, we co-founded an app development agency up in Portland, Oregon, that built prototypes for super early stage products. We worked with dozens of clients to help make their vision come to life. Eventually though, I got the itch to focus on a product for the long haul, instead of just helping founders prototype their initial MVP. So I moved down to the Bay Area in late 2010, and by early 2011, I was working at Yammer as an early engineer. When I joined, we didn't have a designer yet, so I brought Jeff on board to help on the design side. We were somewhere around 30 employees at the time, but it was pretty clear that we had product market fit, and we were growing like crazy. Two and a half years later, after growing from 30 people to about 550, we were acquired by Microsoft. But after the acquisition, I found myself in a 90,000 person company that not only had multiple products, but whose target market could be summed up as simply everyone. So I got the itch to switch back to the early stage, and I joined Gettable as its CTO. Gettable was a construction equipment rental marketplace that was still in its infancy. I spent the next fou…

AI assessment note: “Back in 2006, we co-founded an app development agency up in Portland, Oregon”

Q Before we dive into kind of the system and the mechanics itself, I am really intrigued because you mentioned a couple of different cases of product market fit there and lack of in Yammer, gettable, and ease. I guess specifically with regards to PMF, what were your lessons on experiencing the product market fit of each engine, maybe starting with Yammer and progressing?

A At Yammer, we struck gold pretty early on. We were one of the first products to bring consumer thinking to the enterprise, putting the user first instead of the buyer. Yammer used the same tools that consumer startups were using to evaluate the success of our product. We measured virality, growth, engagement, and retention, and we ran A-B tests to make sure only the features that moved the needle made it into the product. We could tell from these metrics that we definitely had product market fit, but we never had a leading indicator for it. My time at Yammer taught me the importance of validating your ideas through metrics and testing. Gettable, on the other hand, was a lot different. When I joined the team, we didn't have a product yet, let alone paying customers. We didn't have any metrics to track, but we did have a lot of ideas to test. So I spent my time talking to customers, literally wearing hard hats on job sites to watch them work, And iterating through many MVPs in search of product market fit. Though we never found it, I learned the importance of staying close to your customers and making them a part of your process. Ease, also a very different experience, was a rocket ship on a roller coaster. Being in the cannabis space, we had to deal with a lot of chaos, and we had curveballs thrown at us every week. Sometimes these curveballs were more like cannonballs, like whe…

AI assessment note: “My time at Yammer taught me the importance of validating your ideas through metrics”

Q Yeah, no, I totally agree, especially in terms of frequency of usage. If we think about kind of getting that data in, though, Now we have this kind of actionable user data that we can really engage with. What questions should founders be asking of that data to make it really fundamentally useful to that product decision-making going forward?

A Sure. To help you figure out what to build next, we add three additional questions to the survey. So those are, what kind of person would get the most benefit from this product? This one digs into how the customers describe themselves. So they'll describe themselves as busy executives that have a lot of email. For example, for superhuman. But this will help you understand how the market that you're targeting talks about itself. The next question is, what is the main benefit you receive from this product? This shows you your product strengths. And the last one is, how can we improve this product for you? This shows you the holes that you can target to fill in to help more people become really strong fits for your product. These questions are aimed at helping you understand your market, discover what your target market loves about your product, and what you should build to grow within that market. We surface themes in responses to the question, what is the main benefit you receive from this product and filter it down to just the customers who would be very disappointed if they could no longer use your product to show you your product's biggest strengths. By doubling down on those strengths, you're showing your customers that you really understand what the value in your product is, and you're maintaining your lead against the competition. Similarly, by surfacing themes in response…

AI assessment note: “we add three additional questions to the survey. So those are, what kind of person”

Q Yeah, no, I totally agree, especially in terms of frequency of usage. If we think about kind of getting that data in, though, Now we have this kind of actionable user data that we can really engage with. What questions should founders be asking of that data to make it really fundamentally useful to that product decision-making going forward?

A Sure. To help you figure out what to build next, we add three additional questions to the survey. So those are, what kind of person would get the most benefit from this product? This one digs into how the customers describe themselves. So they'll describe themselves as busy executives that have a lot of email. For example, for superhuman. But this will help you understand how the market that you're targeting talks about itself. The next question is, what is the main benefit you receive from this product? This shows you your product strengths. And the last one is, how can we improve this product for you? This shows you the holes that you can target to fill in to help more people become really strong fits for your product. These questions are aimed at helping you understand your market, discover what your target market loves about your product, and what you should build to grow within that market. We surface themes in responses to the question, what is the main benefit you receive from this product and filter it down to just the customers who would be very disappointed if they could no longer use your product to show you your product's biggest strengths. By doubling down on those strengths, you're showing your customers that you really understand what the value in your product is, and you're maintaining your lead against the competition. Similarly, by surfacing themes in response…

AI assessment note: “we add three additional questions to the survey. So those are, what kind of person”

Q that. I guess my question is, you know, speaking of kind of engaging with those customers very, very early, we've seen a massive rise of closed beta products with everything from superhuman to clubhouse more recently. What do you make of the closed beta model and how does that impact your early PMF score? I guess it would be much higher response rates and maybe more engaged product loving users.

A Yeah, I think that's true. I think closed betas are super interesting for a subset of products. Prosumer tools and consumer apps are specifically well suited for this and that you can just keep bringing more people in. If you use the PMF score, it allows you to only bring in users who are the right fit for your product's current feature set. And it allows you to expand that user base as your product appeals to new customer segments. So measuring the PMF of each customer segment can help you figure out which segments to bring in at which time. If you take this approach, you may see that you reach product market fit much faster, albeit with a smaller initial target market.

AI assessment note: “measuring the PMF of each customer segment can help you figure out which segments”

Q that. I guess my question is, you know, speaking of kind of engaging with those customers very, very early, we've seen a massive rise of closed beta products with everything from superhuman to clubhouse more recently. What do you make of the closed beta model and how does that impact your early PMF score? I guess it would be much higher response rates and maybe more engaged product loving users.

A Yeah, I think that's true. I think closed betas are super interesting for a subset of products. Prosumer tools and consumer apps are specifically well suited for this and that you can just keep bringing more people in. If you use the PMF score, it allows you to only bring in users who are the right fit for your product's current feature set. And it allows you to expand that user base as your product appeals to new customer segments. So measuring the PMF of each customer segment can help you figure out which segments to bring in at which time. If you take this approach, you may see that you reach product market fit much faster, albeit with a smaller initial target market.

AI assessment note: “you may see that you reach product market fit much faster”

Q investors' Really trying to understand the granular minutiae of CACs and LTVs at seed stage. Really, it's so transient and, you know, CAC volatility is so high, especially with platform costs being where they are. Would you agree with me in terms of kind of actually denigrating focusing so exclusively on CACs and LTV at seed? And how do you approach the centrality of unit econ and CACs at seed?

A So in my opinion, at least, seed stage is definitely too early to start measuring CAC and LTV, but they're still super important to keep in mind. You want to be sure that once you find product market fit, your unit economics can work out for you. PMF is a much better seed stage metric because by showing you how much your product is resonating with your customers, it tells you when to start focusing on CAC and LTV. Once you reach product market fit, that's the time when you can really start thinking about positioning of your marketing messaging to drive down your CAC. That's also when you know that LTV is going to be long enough because you're not going to have as many people churning. So in my mind, PMF is a much better metric for those kinds of companies.

AI assessment note: “seed stage is definitely too early to start measuring CAC and LTV”

Q Can I ask then, if you think about, like, timing, When's the right time to start measuring for PMF? Is it in literally the beta when you have your first few customers onboarding? Or is it when you kind of raise your seed around and you've got multiple thousand or, you know, multiple logos on board? When does one start measuring?

A So no matter where you are in the product development cycle, the answer to this question is the same. The right time is now. If you're already growing like crazy, you never, you never know when your market's going to change. I mean, look at the current COVID situation. People's markets are changing from under them right now. If you're an early stage startup and still in the wilderness, you're going to need a North Star metric, and PMF is the only metric that's going to tell you how close you are to hitting that growth stage. So it works for both early stage and growth stage, and even later stage if you're expanding into different product lines or wanting to understand your customer segmentation a little bit better.

AI assessment note: “no matter where you are in the product development cycle... The right time is now.”

Q Can I ask then, if you think about, like, timing, When's the right time to start measuring for PMF? Is it in literally the beta when you have your first few customers onboarding? Or is it when you kind of raise your seed around and you've got multiple thousand or, you know, multiple logos on board? When does one start measuring?

A So no matter where you are in the product development cycle, the answer to this question is the same. The right time is now. If you're already growing like crazy, you never, you never know when your market's going to change. I mean, look at the current COVID situation. People's markets are changing from under them right now. If you're an early stage startup and still in the wilderness, you're going to need a North Star metric, and PMF is the only metric that's going to tell you how close you are to hitting that growth stage. So it works for both early stage and growth stage, and even later stage if you're expanding into different product lines or wanting to understand your customer segmentation a little bit better.

AI assessment note: “no matter where you are in the product development cycle... The right time is now.”

Q investors' Really trying to understand the granular minutiae of CACs and LTVs at seed stage. Really, it's so transient and, you know, CAC volatility is so high, especially with platform costs being where they are. Would you agree with me in terms of kind of actually denigrating focusing so exclusively on CACs and LTV at seed? And how do you approach the centrality of unit econ and CACs at seed?

A So in my opinion, at least, seed stage is definitely too early to start measuring CAC and LTV, but they're still super important to keep in mind. You want to be sure that once you find product market fit, your unit economics can work out for you. PMF is a much better seed stage metric because by showing you how much your product is resonating with your customers, it tells you when to start focusing on CAC and LTV. Once you reach product market fit, that's the time when you can really start thinking about positioning of your marketing messaging to drive down your CAC. That's also when you know that LTV is going to be long enough because you're not going to have as many people churning. So in my mind, PMF is a much better metric for those kinds of companies.

AI assessment note: “seed stage is definitely too early to start measuring CAC and LTV”

Q expansion within the org. I guess my question for you is one that I'm sure you've had a lot, especially kind of when speaking to investors, is I totally understand this. It's one of those products which totally makes sense, but I guess directly, it feels like a startup product. Why do you believe that there's a massive company to be built in the kind of more PMF assessment space?

A Yeah, so first off, while we do assess product market fit, I wouldn't say that we're in the PMF assessment space. I'd put us squarely in the product intelligence category. So like Mixpanel and Amplitude, we help you answer tough questions about your product, but we're focused on getting solid answers to fuzzy questions using qualitative data instead of getting quantifiable answers to specific questions using quantitative data. In short, we can tell you what your users love about your product and what your users need from it, and tools like Amplitude and Mixpanel can tell you how those users use your product. So we're really complementary in the space. I would say we're sort of forging a new category here, and it's Basically, qualitative product intelligence. So we augment those analytics tools. So really, anybody who is a customer of Mixpanel, Amplitude, any of those product analytics tools can get a lot of value out of ViableFit for understanding how their customers are thinking about their products.

AI assessment note: “I'd put us squarely in the product intelligence category. So like Mixpanel and Amplitude”

Q expansion within the org. I guess my question for you is one that I'm sure you've had a lot, especially kind of when speaking to investors, is I totally understand this. It's one of those products which totally makes sense, but I guess directly, it feels like a startup product. Why do you believe that there's a massive company to be built in the kind of more PMF assessment space?

A Yeah, so first off, while we do assess product market fit, I wouldn't say that we're in the PMF assessment space. I'd put us squarely in the product intelligence category. So like Mixpanel and Amplitude, we help you answer tough questions about your product, but we're focused on getting solid answers to fuzzy questions using qualitative data instead of getting quantifiable answers to specific questions using quantitative data. In short, we can tell you what your users love about your product and what your users need from it, and tools like Amplitude and Mixpanel can tell you how those users use your product. So we're really complementary in the space. I would say we're sort of forging a new category here, and it's Basically, qualitative product intelligence. So we augment those analytics tools. So really, anybody who is a customer of Mixpanel, Amplitude, any of those product analytics tools can get a lot of value out of ViableFit for understanding how their customers are thinking about their products.

AI assessment note: “I'd put us squarely in the product intelligence category.”

Q third and final one, which is that how can we make it better? Because I'm permanently perplexed by, I think it was Thomas Ford's quote of like, you know, building a faster horse. How do you think about the right balance between ingesting and acting upon user data and engaging with it as part of your product? Decision making versus disregarding it and building a car and not a horse.

A So first off, sometimes users want a faster horse. Google and Zoom are both great examples of that. That said, product is both an art and a science. We take care of the science so that you can focus on the art of product management, you know, really understanding what's most important from a strategic perspective for your business, for your users, for other stakeholders, and we really help just surface the problems and areas to focus on. But how you tackle those problems are entirely up to you, and that's where the art of product management comes in. Also, you should discard the feedback that you receive from the users who would not be disappointed if they could no longer use your product. They're not really your target market or your audience, so it's best to just politely thank them for their feedback and shift your focus to the other two segments.

AI assessment note: “you should discard the feedback that you receive from the users who would not be disappointed”

Q third and final one, which is that how can we make it better? Because I'm permanently perplexed by, I think it was Thomas Ford's quote of like, you know, building a faster horse. How do you think about the right balance between ingesting and acting upon user data and engaging with it as part of your product? Decision making versus disregarding it and building a car and not a horse.

A So first off, sometimes users want a faster horse. Google and Zoom are both great examples of that. That said, product is both an art and a science. We take care of the science so that you can focus on the art of product management, you know, really understanding what's most important from a strategic perspective for your business, for your users, for other stakeholders, and we really help just surface the problems and areas to focus on. But how you tackle those problems are entirely up to you, and that's where the art of product management comes in. Also, you should discard the feedback that you receive from the users who would not be disappointed if they could no longer use your product. They're not really your target market or your audience, so it's best to just politely thank them for their feedback and shift your focus to the other two segments.

AI assessment note: “product is both an art and a science. We take care of the science”