so sorry to disappoint you all, as you just heard Jordan said it was her. It's not her. It's me. There are other news editor here at TechCrunch. I hope I can do as good a job she would. But I'm very thrilled to have ro hora here rule is currently Nizam introduction, but founder and CEO of superhuman founded reported before that. And we're thrilled to have him here to talk about something near and dear to I think many founders hearts, which is growth hacking and growth in general in the early stages and how to do that without kind of breaking the bank. So thanks for joining us roll. Thank you for having me. Yeah, it's great. Always great to talk to you very excited to see what you've got to show us today. I know you have a presentation about this. So I'll I'll throw it to you. And you can go ahead and take us right into that.
Okay, fantastic. So for everyone listening, I am happy to talk about anything relating to the early stage of growing your company, this could be product market fit, it could be growth, it could be pricing, it could even be growth hacking. However, the most important of these is product market fit. And this is sort of the standard disclaimer that anyone who you would ever talk to about growth would ever give you, which is you shouldn't try and grow a thing that isn't yet ready to grow. So what I'm going to start off by sharing is my presentation on product market fit, you may have heard of the product market fit engine. This is the algorithmic way that we use a superhuman to find products market fit, and it can work for you too. Okay, so let's take it away. Thank you Darla, my name is Rohit. I'm the founder and CEO of superhuman, but we of course build the fastest email experience in the world. Our customers get to their inbox twice as fast as before they reply to that important email sooner, and they see inbox zero for the first time in years. Now this is the story of how we built a product market fit engine. And product market fit is the number one reason why startups succeed. And the lack of product market fit is the number one reason why startups fail. So the question I really want to start with today is what is product market fit? Well, Paul Graham, the founder of Y Combinator, would say it's when you made something that people want. Sam altman would say it's when users spontaneously tell other people to use your product. But it is Marc Andreessen who has perhaps the most vivid definition, he would say you can always feel it. When product market fit is not happening, customers aren't quite getting value users are not growing that fast word of mouth is not spreading. Press will use a kind of blur in the sales cycle takes to download. But you can always feel it's when product market fit is happening. Customers are buying as fast as you can that serve as you're hiring, sales and support as fast as you can. reporters are constantly calling you about your hot new thing. Investors are staking out your house and money is piling up in your checking account. Now this is indeed a vivid definition of one that I was staring at through tears in the summer of 2017. You see, it seems so subjective. So an actionable what do you do if by this definition, you don't have product market fit? Indeed, can you measure product market fit? Because if you can, and maybe you can optimize it, maybe you can systematically, systematically perhaps even numerically increase product market fit? And as it turns out, the answer is yes, you can measure product market fit and you can optimize it and it is the precursor to growth. But before I share how, let's wind the clock back, by Gosh, about 11 years or so, because in 2010, I started this company called rapportive. We built the first Gmail plugin to scale to millions of users. When people email to you, we show you what it looks like where they worked their recent tweets, links to their social profiles. We grew rapidly. And two years later, we were acquired by LinkedIn. And during those four years, I developed a very intimate view of email. I could see Gmail getting worse every single year becoming more cluttered using more memory consuming more CPU slowing down your machine, still not working properly offline. And on top of this, people were installing plugins like ours, reportedly, but also Boomerang mix, Max klewitz, you name it, they had it. And each plugin took these problems of clutter, memory, CPU performance offline and make all of them dramatically worse. So we decided it was time for change. We imagined an email experience that is blazingly fast for searchers, instantaneous for every interaction is 100 milliseconds or less an email experience where you never actually have to touch the mouse where you could do everything from the keyboard fly through your inbox, an email experience that just works offline so you can be productive anywhere, an email experience that had the best Gmail plugins built in natively, and yet somehow with subtle, minimal and visual recall. This sounds like a slam dunk right? And so in the summer of 2015, we set up our off If this was our fancy side, and we started to write code, and then the summer of 2016, we were still coding. And in the summer of 2017, we were still coding.
I felt this incredible intense pressure to launch, both from the team and also from within myself. After all, my last company reports if I'd launched, scaled and been acquired in less time, and here we were two years in and we still had not watched. But deep down inside, I knew, no matter how intensely I felt pressure, that launch would go very badly, it would not be the Marc Andreessen story. I did not believe that we had product market fit. And then though I'll I knew it I couldn't just say that to the team. You see these super ambitious hyper intelligent engineers, they poured their hearts and souls into the product. I needed a plan. And so in April of 2017, I started my search for the holy grail for a way to define product market fit for a metric to measure product market fit, and for a methodology to systematically increase product market fit. I searched high and low I read everything I could find spoke with all the experts, and I came across this guy, Sean Ellis. You see Shawn run growth in the early days at Dropbox, LogMeIn event brights, he even coined the term growth hacker. And as vivid and as compelling as Andreessen his definition of product market fit is, it is still a lagging indicator. By the time cash is piling up in your bank account. Guess what? You already have product market fit. Or Shawn found a leading indicator, one that is benchmark and predictive. Just ask your users this, how would you feel if you can no longer use the product and measure the percent who answered very disappointed. And after benchmarking hundreds of startups shown found that the companies that struggle to grow, always get less than 40%? very disappointed. And the companies that grow the most easily almost always get more than 40%. In other words, if 40% of your users or more would be very disappointed without your products, you have initial product market fit. This example shows that same question answered by just north of 700 slack users back in the day 51% of whom will be very disappointed without slack. 51 is greater than 14. So slack has product market fit. Now today, this may seem self evident, you don't need a fancy questionnaire to tell you that's about like, This example shows how hard it is to be 14% benchmark. This metric is more objective Than a Feeling it predicts success. That's the net promoter score. And this is not only the best way to measure product market fit, and lets you develop your very own product market fit engine. And with this engine, you now have a methodology for increasing product market fit. And this engine will even generate your roadmap for you, and do so in a way that will make your company grow. Big claims I know. So let's take a look at step one. Step one is survey. Email these four questions to every user. Now you should only send these questions when your users have experienced the core of your product. As superhuman we wait until they've had about three weeks, analyze the results to question number one, you'll end up with results something like these. These are the actual numbers from superhuman in the summer 2017 we very clearly did not have product market fit. Now that might seem sad, but I at least could explain our situation to the team. And most excitingly, I had a plan to increase our product market fit score, which brings us to step two of the engine segment. Now we really want to understand who are the people who love our products. And here I like to use the concept of the high expectation customer which is a concept by found from Julissa Pat, Julie lead early Munson at Dropbox, Airbnb and many other great companies. Now the H XTC is the most discerning person in your target demographic, they will insure your products for its greatest benefit. They will help spread the word. And most importantly, others want to be like them because they see them as clever, judicious and insightful. That's key when it comes to growth. Now I realized that this can be abstract. Let's take a look at two examples. The Dropbox h MC wants to simplify their life. The very trusting the technical savvy, they're looking to save time and at the end of the day, they want to know that somebody has their back when it comes to their life's work. I'm an example of a Dropbox HSC and I'm sure many of us here are also one more the Airbnb h XTC does not simply want to visit new places they want to belong. They want to experience Paris as if they really live there. And Airbnb is early success came from focusing on these influences. And these tastemakers now here's the amazing thing.
Your users will almost always describe themselves if they're happy with your product, using the words that matter most to them. So you can use this to create your HSC. Take the users who have been very disappointed without your product. Remember, these are the folks who love it, and analyze the answers to question number two, who do you think this is best for? This is a very powerful question, you can then turn these words into a rich and detailed explanation of your highest expectation customer. Let's take a look at an example. This is Nicole, superhuman HSC. She's a hard working professional, she deals with many people, she might be an executive founder, investor or manager. She works hard. Often into the weekend she considers herself busy, he wishes she had more time. She feels she's productive. And she's self aware enough to realize she could be better occasionally, she'll investigate ways to improve. Now obviously, she doesn't have an email. On a typical day, she will read 100 to 200, she might send 1540 on a very busy day, she might send as many as 80 or more. And critically, this is the most important is part of her job to be responsive. She prides herself on being so she knows that if she's not there can block her team to have a reputation or cause missed opportunities. Now, she actually gets Inbox Zero, but she'll get there at most, a few times a week. And very occasionally, perhaps once a year, she'll declared email bankruptcy. And generally she has a growth mindset. She's open minded about new products and keep ups keeps up to date with technology, that she might have a fixed mindset about email, whilst open to new clients, she's skeptical at one could make her faster, you'll need to develop the same quality if not better at HFC for your customers based on the server. Now once you have this, let's head back to the survey. Let's take each response and assign a persona to each one. And here's the magic. Take the users who most love your products, those who be very disappointed with answers and use them to narrow the market. In this simplified example, we're going to focus on founders, managers, executives, business developments deliberately ignored sales, customer success, engineering and data science. Just by segments in our product market fit score jumps by 10 to 32%. Now we're not quite at 40% yet, but in two minutes, we made significant progress on to Step Three of the engine, which is to analyze. Now we need to understand two things. Number one, why do people love our products and number two, what holds people back from loving our products? To understand why people love our products, we go back to our survey and we focus only on the users could be very disappointed without it. And then we analyze that answers the question number three, which is what is the main benefits you receive more products. And here's some example answers for superhuman processing email is much faster I get to my inbox in half the time the app is crazy fast, the keyboard shortcuts maybe an actual superhuman, it's so much faster than Gmail more efficiently my time, incoming email more quickly, speed is better. So I can do everything from the keyboard speed, and a great set of keyboard shortcuts. And you want many more than these, this is just a sample, you collect them all. And then I like to create a word cloud. And it becomes as clear as day putting the spit put it up on your wall, make it your zoom background. I am forever reminded that people love superhuman for its speed, its focus on people chocolates. Now, we have that piece of information and we want to grow the size of the very disappointed crowd. First, as painful as it is, we have to ignore the knots disappointed credit, they are so far from loving the products that they are essentially a lost cause. This is important because they're going to ask for all kinds of distracting things. And as counterintuitive as it may feel, do not act on their feedback. That leaves just the somewhat disappointed crap. Maybe we can help them fall in love with our products. Now the answer is most certainly can. But again, I cannot stress this enough, do not act directly on their feedback. Why? Because many of them will remain somewhat disappointed no matter what you do for them. And so their requests are just distracting. So how do you decide who to listen to?
Well, once again, here's the magic. We use the main benefits of the very disappointed users to segments the somewhat disappointed users. First, and somewhat disappointed users for whom speed in our case was not the main benefit. I strongly advise that you ignore these people because the main benefit does not resonate with them. Even if you built everything they wanted. They will never fall in love with your product. But second, is somewhat disappointed users for whom speed was the main benefit. We pay very special attention to these because the main benefit does resonate with something and probably something small, holds them back. How do we figure out what what we analyze their responses to the fourth and final question, how can we improve our product for you and once you Again, I like to create a word cloud. And here are the results for superhuman back in 2017. At that point, the main thing holding back our users was simple. It was the lack of a mobile app, which we have, of course, since address, but then it got less obvious more interesting integrations, attachment, handling, calendaring, unified inbox, better search, read receipts, and so on into the long tail. And guess what, we're still working on this list. This is a never ending journey. To increase your product market fit store, all you have to do is build these things that would then convert these users who are only somewhat disappointed without your product into fanatics who love your products. Now, this brings us on to Step Four of the product market fit engine, which is implement, we now understand two very important things. Number one, why users love our products. Number two, what holds users back, I promised this regenerating roadmap for you, here's how we do it. To increase our product market fit score, we should spend half our time doubling down on what users love. In our case, that means even more speed, even more shortcuts, even more efficiency, and more aesthetics. And just as importantly, equally, so we should spend half our time systematically addressing what holds users back. If we only double down on what users love, as wish a vision driven teams tend to do, we would not increase our product market fit score. But if we only address objections as data driven teams tend to do, a competitor would eventually overtake us. the right balance is about 5050. This is your new roadmap, it automatically writes itself. And this roadmap will increase your product market fit score. Now, this brings us on to the fifth and final stage of the product market fit engine, which is track, this framework will work. But of course, there are no silver bullets as you double down on what users love. And as you address what holds users back, you should constantly survey new users, I recommend tracking your product market fit score every week, month and quarter, just rolling the numbers up. In the summer of 2017. For example, our product market fit score was 33%. After a quarter, it was 47%. Of course after it was 56%. And the quarter after that 58% 58% of our users would be very disappointed without superhuman. So I can tell you, the product market fit engine really does work. It gives you a way to define product market fit and metric to measure product market fit and the methodology to increase product market fit. And it will even write your roadmap for you. So if you're thinking about growth, if you're thinking about growth hacking, I hope you consider using this at your company. And if you do please do let me know because I'd love to help in any way that I can. Now I'm super excited to get into your questions and to talk about the steps to come after this or around it things like growth tactics, channels pricing scenarios. I'm just gonna throw up my contact details for a few minutes or a few seconds. In case anyone wants to get in touch I raffle at superhuman calm, my DMS are open Rahul vora on Twitter. And I'm looking forward to a robust session of live questions now. So Darryl, back over to me. Yeah, thanks
very much. That was great. Really, really, like super thorough. And also what I appreciate about it was it was all very substandard. Right? I think that you get this bad rap of some growth hacking tactics that like it's a it's a losery or whatever, right? But this is all like, no, this is you really do it. This is how you do it so that the growth is lasting and meaningful. Right. So that's, that's excellent. So we do have a lot of questions in here. We're gonna jump right into them. I'll go to this one first, because I think it kind of sets the stage but Raghav asks, What stage should one do the survey for product market fit score? And how frequent can it be done without overwhelming users? I think you just kind of touched on that at the end of your presentation, the frequency, but yeah, that's that's for graph question. Great,
raga. Fantastic question. Okay, so first of all, when should you do the survey, you should do the survey, once your users have had the chance to experience the core value proposition of your products. So it's super human, super fast email clients, you're going to save in the region of half an hour to an hour a day. It's really meaningful. To fully learn the products though it does take time for anyone who's ever used it or used it. You know, there's keyboard shortcuts, there's a lot of training to do, it's probably going to take you about two weeks to get up to speed. So we wait until a user's second or third week before asking the question. If it was more of a transactional products, let's take the example of Uber or Lyft. I would wait until the third ride or so. After about the third ride. You have understood the value prop of a car turning up whenever you want and never having to remember to pay. It's pretty valuable. But after the first one, especially if it didn't go very well. It might not be the right time to ask a user. The second part of the question was around frequency. only ever ask the user this question once, if you at all care about the 40% threshold, all of the original studies were done based on asking the user at most once. And there's all kinds of reasons for that. But the main ones to consider is the more you ask a user the same question, the less signal you're going to get out of the user for that question.
Makes sense? Yeah. This is a related question. from Daniel, it's koski. He asks, How do you get users to try your product sufficiently to even be able to survive?
A boy? Question, Daniel, it honestly depends on the type of products you have. I'll I'll give you a generic answer right now, if you want to, you know, maybe follow up with your with the specific type of products, I can try and give you a more useful answer. Ultimately, this sort of goes without saying, create a great product, there are definitely things that you can do to make people give it more of a college try than they otherwise might, for example, build hype around your product, make it seem like the bee's knees, get influences to shout about it from the rooftops, create social proof. Do what we do do onboarding. To this day, we still onboard every single user wants one in a VIP concierge setting. This is a 30 minute zoom call, where we get to know you, we look at how you're doing your email today, we show you how to do it twice as fast as inside superhuman. And so 10s of 1000s of people in the world who use superhuman, all now have a friend at the company. Not only does that make the products more sticky, it also makes it more viral and increases MPs. So those are just a few tactics to consider. But fundamentally, you have to build a great product.
Great, you know, this is a related question, but I think it dovetails nicely with my experience. It was a very expensive home cooking device. And but they did the same thing. And they do they do a personal one on one kind of like introduction to the product and somebody walks you through like their aspects of this, that can be intimidating, but they're actually very powerful. But it creates a lot of stickiness and a lot of creates fans for life. Right. But so the question that Noah Marsh asks, who also, by the way, thanks to you for being so generous with your time, but he asks, have you seen this flame framework applied to a consumer good or hardware? Or is it strictly applicable to SAS businesses?
I think most of the original work was done based on consumer internet type SAS businesses, but I see no reason why it wouldn't also work with consumer hardware either. So I wouldn't be afraid of trying to use it. Of course, you have a little bit longer iteration cycles and consumer hardware, it's way more capital intensive. But fundamentally, there's no reason why you can't take the same segmentation approach. Use it to identify the people who love your product, question them in a specific way to figure out what that archetype is, use their main benefits to segment the people who are on the fence, and then essentially ignore everyone who isn't within a stone for falling in love your product. That, in a nutshell, is what this algorithm does. And I just think it's super effective when you can apply it to almost any type of business. Great.
So I actually I'm gonna interject and jump the line here with my own question. But I had, you know, when you were talking about the segmentation, and when you're talking about, you know, narrowing your market, how do you determine how narrow you can go? Like, how do you determine when you're like, well, if I narrow this any further, it's not really gonna work. So is like, Is there a math or process to that that can help you kind of like get to that quicker?
Yeah, great question. I'm actually just going to go ahead and share my screen again. Sure. Now, what's what's funny about this is this, this presentation, I essentially gave a much less polished version of it, many, many years ago, inside of superhuman, and I was anticipating this very same question. And so I wrote this. folks might recognize this from the one of Paul Graham's famous essays, which is, you know how Nisha is Turkish. And whether it's Paul Graham, who said their support for height, they're both well known for saying it's much better to make a small number of people really rapidly love you, as opposed to, you know, a large number of people feel net or mediocre. And the canonical example of course, is Microsoft, that few people remember but actually started by selling a BASIC interpreter basic programming language. For the Altair, which is a very old microcomputer, and they only sold a few 1000 copies. And after that, they wrote a BASIC interpreter for other machines. And then after that they supported languages other than basic assembly C, then one day c++ medicals operating systems and windows and then Office applications in general. And then boom, one of the largest software companies in the world, of course, that boom is a 30 year journey. So, there is no such thing in my opinion as to niche you can always find a way to jump to adjacent optimate.
Great. All right. Cool. Yeah, that's a very, that's a good, good illustrative example. He couldn't really come up with a better one of success. But let's, here's another question from Thomas Peloton who is a super happy superhuman user. So he recalls having received the PMF survey about three weeks after he started using the product. And he's curious about, do you do similar surveys for users who have been around longer? Do you alter the survey? How does it change depending on how long someone's been on the platform?
So again, we don't ask any user more than once. So you've got that survey, back up using superhuman, the message is very heartfelt. And we're not we're probably not going to survey you. Again, you might get an ad hoc email from me, sometimes when we are building a new feature or contemplating the new products, I will personally email personally, meaning I'll probably like mailmerge, four to 500 people about a specific area of interest, and then I'll have real organic, often fascinating conversations with those folks, those are much more ad hoc than on demand as we're doing and building new things. But for the purpose of product market fit, it's one user one time. Great, okay.
But it's the same survey that goes out no matter how long they've been on the platform, right?
Exactly, yes, for three weeks. And we ask those four questions and a few more as well.
And then Adam Gordon asks, how would you do the initial stages of growth today? Would it be the same as you did in the early stages of superhuman or you change anything?
Great question, would I change anything, I would probably hire a little bit faster. I think I was a little bit slow to hire. But in terms of the sequences of activities that I took on, I wouldn't necessarily change those things. The first year was characterized by me personally coming up with a vision of what it is I thought we should build. And then validating that vision by having a ridiculous number of customer interviews, it was north of 700, nearly 1000, in that first year, all done via email so as to be fast. But it's almost the equivalent of three per day, all of which validated what I thought we should build, which was an email experience that genuinely made people faster, and helps them save time. And then there was a series of steps which we can get into a focus once around, how do we sequence a growth strategy alongside running the product market fit engine so that the company grows?
Yeah, I mean, I would like to hear more about that. But we do have lots of questions. So we should go on to addressing these. Jeremy gross asks, and I had this question, too. Any tips on getting users to respond to surveys? And is there a critical mass of users necessary to obtain trustworthy results?
Okay, great questions. First of all, how do you get users to respond to surveys, the easiest way is to charge for your products. People are paying to use your product, you do not have to worry about the survey response rate is going to be higher. If that doesn't work, you can try other mechanisms. It doesn't have to be email, you could have an in app pop up, you could have a text message version of the survey, you could contemplate other ways of delivering the request, it could be a push notification on a mobile device. Now all of these ways are going to introduce some form of systematic bias, the original survey where the 40% threshold comes from, they were all done with email. So you're going to have to pay less attention to the specific number. That's okay. Your goal should always be to get the number to go up.
Great. Okay. Yeah, here's another one from anonymous. What if everything you built map to your customer discovery product market fit, but now that you've launched, there's no fit? This is kind of related to what I want to questions I have, which is kind of like do you always start with a hypothesis or intuition? Or do you kind of like try to pre engineer the product market fit?
Yeah, great question. I Apologies to the previous asker I forgot to answer the second part of the question. All right. The answer is about 200. Once you have about 100 responses, then it's directionally correct. Because it's just it's, you can do a lot not gonna get into statistics on 100 pluses What you need is a significant group. Yeah. Okay, so if I understand the question correctly, it's what happens if you sort of fit around in customer discovery, and then then you launch and then it goes horribly wrong? Well, superhuman is never launched. And I don't really believe in a launch, I think you launch if you need one of the three C's, more customer leads more capital, more candidates, and, you know, maybe a PR event is a good way to get some eyeballs on what it is you're doing. But if you're a sufficiently great founder, and sufficiently good team and a sufficiently interesting market, you might be able to find other ways to satisfy all of those things. And many companies never actually launched, they're just constantly striving to grow every single week or every single month. And that that would be the thing I would actually sell for. But nevertheless, I think the question still stands, what happens if as you're growing, you end up with different people to whom you originally taught? Well, this is inevitable, this will happen. I think Andrew Chen calls it the law of shifting metrics, every metric will come down, it's I mean, there's nothing that we can do about it, except for to fight the good fight, and to constantly be bringing it back up. That is why to this day at superhuman, we still run the survey, we still run this engine, the users that we have today are very different to the users that we had several years ago when we when we first did this analysis, and that's okay. What's important is that you never stop improving. The work is never done.
Great. Thanks. And Danilo Salazar asks, What was your experience building your pricing model based on the product fit?
Great question. Okay. So I always say the same thing, which is, before you figure out pricing, you must first actually figure out your positioning. And it's kind of hard to have one without the other. And we started, we started rather with an article by Ariel Jackson. Anyone can Google it, it's really great. positioning your startup is vital. Here's how to nail it on first round review. Now she advises using a formula like the following. It's a little bit of a Mad Libs exercise. For a target customer who has a need or an opportunity. Your product is in a products category that has a key benefits. And unlike any competing alternative, your products has this primary differentiation. And in the article she gives an example of Harley Davidson, the only motorcycle manufacturer that makes big cloud motorcycles for much too, guys and mature wannabes, mostly in the United States who wants to join a gang of cowboys in an era of decreasing personal freedom. Now, whatever you might think about all of those words, probably probably somewhat accurately captures the Harley Davidson audience, as we actually sat down with Arielle, and we're very fortunate to be able to work with her. And we worked out a similar statement for superhuman back in 2016, which was for founders, executives and managers of High Tech High growth companies, who feel like their work is mostly female, superhuman is the fastest email experience ever made. Unlike Gmail, which was built 15 years ago, in superhuman, everything is exquisitely crafted, responds and responds in less than 100 milliseconds. So you want to develop your own positioning statement. Now, one other resource, by the way that I would recommend for this is positioning the back of your mind, this is a fantastic book super helpful. So we started asking questions like, Are we the Forte of email? No. Are we the Mercedes of email? Are we the Tesla of email? Well, maybe we're getting there. And that's, that's when we came up with that statement. Of course, we've since expanded beyond that very tightly defined target. Now, when you hear the positioning, it's clear that superhuman is a premium tool for a premium market. And it's only once you've understood your positioning that I recommend you then move on to your pricing. And one of the best books on this is monetizing innovation by modicon ramanujam. He works at escapee Simon Kutcher apartments that like the preeminent pricing firm here in Silicon Valley. Now he covers a lot of Wilson developed pricing, and one of the easiest methods is the Van westendorp price sensitivity meter. And in late 2015, late 2016 ish. We asked around 100 of our earliest users, these four questions at what price would you consider superhumans be so expensive that you would not consider buying it? At what price? Would you consider CPM to be priced so low that you would feel the quality wouldn't be very good. So you wouldn't buy it? Number three, at what price? Would you consider CPM to be starting to get expensive? So that isn't out of the question, but you think really hard about it. But you'd still actually buy it? And then before what price? Would you consider CPM to be a bargain? A great bye for now, most startups go after that question. They're in usually a new market and trying to get all the users really quickly. There's some kind of network effects. But for our company, where there isn't really a network effect, and there's two gigantic incumbents, we went after question number three, which is at what price? Would it be expensive? You'd have to think about it, but you would still buy it. And the median answer to that third question for us was $30 per month. So that's how we picked our place.
And did you was that open ended? Or did you provide a list of options for the respondent?
It's open ended, but numerical, and then it's actually super cool. There's a very, very short Wikipedia article on the Van westendorp survey, I highly recommend folks check it out. You can then arrange all the numbers in a line, plot the cumulative distribution. And so you can imagine you'll end up with curves, you actually end up with four curves, one for each question. And what you can then do is look at when these curves intersect, so take the question. It's so expensive that you wouldn't buy it. And it's a bargain, it would be a great buy for the money. The point at which those curves intersect is the point where the same percentage of the population believe one or the other. So in a sense, you found an optimum
grip. Cool. Okay. Deepak Kapoor asks, do you reach out to all customers after I think you said three weeks is your usual timeframe? Or does time spent on the app come into play at all?
number of emails sent comes into play a little bit. I believe the lowest threshold is 20 emails. At that point, we think you've had enough experience sending emails to realize how much faster it will make you. So I think the actual algorithm is something along the lines of in your first year, your second week, maybe your second week, if you send 20 emails, but no later than your third week.
Gotcha. Okay. This one I think I'm curious to hear your answer to I think it's one of those questions where you're like, I think you answered your question, but it's Mandisa, Brent, my Sumerian asks, How do you drive the product usage to experience the core product value? Most of the time users download it, but may not necessarily use it as expected.
Yeah, this is another fantastic question, it's going to be things like, develop a really great first time user experience, consider doing something like one to one onboarding, have a really comprehensive onboarding activation campaign, when a new user starts using superhuman, we actually have a sequence of emails, and it's one per day, it's a daily tip for me. And I believe that it's between 40 and 45 days right now. And every single time we make a new feature, we actually add an email to that have really impactful and effective, built in help for the new user experience in your product itself. Build as best as you can build a word of mouth spread, so that when people talk to their friends, or their colleagues or their peers, or, or they look at high expectation customers, the folks that they aspire to be like, they see your product, or they hear about it, right. And that way, even if they hear about those users
are using it right, they hear like this is the this is the thing I love most about.
Yeah, exactly. So those are four or five techniques that you can use. But I've said this before, I'll say it again, that is no shortcuts, you have to build a fundamentally good products.
Right? Yeah, I mean, that was what I was looking at. I was like, yeah, cuz I, you know, it comes up often, especially when you're talking to product managers or whatever. It's like, well, I wanted you to do this thing, but you're not doing this thing, right? And then usually the answer to that is you have to go back and look at the actual product construction, how it's built, and how you're guiding users to what the Green Path is, what the virtuous path is.
There is one more answer, by the way, and I think founders often forget this out of the, out of the culture of growth, right, we're expected to ride which is maybe the wrong user. Or and this happens a lot early and superhuman where, you know, a good half of our early users, they wanted the ability sort of merge seven inboxes into one they wanted the ability to do attachment. Download tracking, they wanted a dashboard of, you know, let me show me everyone who's reading my emails and you know, all of all that sales stuff. And sometimes it's just the wrong user. And we had to deliberately say if the whole point of this product market fit engine is deliberately saying, if you remember, we excluded sales, at least back then we're not going to build those things. So if they're not using the products in the way you want, at least consider the possibility that not the right person.
Yeah, that's great advice. And also very hard to follow advice, I think, for a lot of early startup founders, because those those users, you really love having a platform, but it's true, some of them might not be right for what you're trying to accomplish. That's all the time we have for There's loads more questions. Unfortunately, we couldn't get to all of them. But we're always been very kind to provide his contact information. So please do get in touch with him there. And thanks very much for rival for joining us today. It's been great. Thank you for having me.