Unlocking the Secrets of Product Adoption with Moshe Mikanovsky

What would be some of the ways to strengthen stickiness beyond just onboarding?

Um, sure. I mean, stickiness is really all about the value to them in the product. So the more, um, they get value in the product, the more sticky it will get. Um, you also have to look into what is their cost for them to switch to another product. So the higher the cost for them to switch, the more likely is that they will not switch and then they will stay with you. So there are different ways for that. Um, some companies are doing this— it's not always, um, I don't know if I want to use the word ethical. I think it is ethical, but it's not always very nice to the users. So for example, uh, giving the user hard time to get the data out of the product. Or giving the user a hard time to disconnect from the product, or whatever, you know, things like that, uh, to, to, uh, get them to, to move to another product. But a cost to, to switch is not always about hard costs, you know, like the license you're paying for it. It's also about all the time it will take you to learn a new product, the time it will take you to offboard from the old one to the new one, to convert your workflows and behavior and data and stuff like that, right? So, so that could be one way as well.

Hi and welcome to today's Founder Institute event on how to measure and strengthen product adoption. We have invited a special guest today from Root Cause, but before we invite him on stage, a few words about our agenda today. We will have a short presentation And then there is a few minutes to ask your questions on the topic. And after that, we're going to break out into our networking rooms where you can connect with each other and maybe meet our speaker for a while. So today, okay. We can invite Moshe to introduce himself. Our speaker is Moshe Mikanovsky, who is a product director of RootCourse. Welcome.

Uh, thank you so much, Emilia. Um, I'm excited to be here again. Um, I've been here for previous workshops and I hope they gave value to everyone, so I'm happy to be here again. Let's maybe start right away with the presentation. So if we can bring this over. Here we go. And I think everyone can see that, right? So we will have time at the end, and I want to make sure that people will have time to ask questions and stuff like that. So I will do that You know, we keep time towards the top of the hour to, to ask any questions related to what we're talking about today. And so, so, so let's start diving into product adoption. We are— that's the topic we're going to talk about today. And it's not just about, you know, what is product adoption, but also a bit about how we measure it. and why is it really important to, to follow. The one thing that I wanted before I even dive into that, I wanted to actually, you know, start first with some examples of companies that you might all know. But, you know, there are some issues there with adoption. And that's really why the importance there of talking about the options. Hopefully some of you are familiar with some of these companies, you know, like Clubhouse and Threads are more recent ones. Quibi, actually, they were out during just before the pandemic and they don't exist anymore, Quibi, these days. But Clubhouse and Threads still do exist, but Their adoption is not what it used to be when they started. They had, uh, some, uh, you know, of them, like, um, Quibi had, uh, 3.5 million application downloads and 1.3 million active users, and then they really struggled to keep them. And before they shut down, uh, they only have half a million subscribers that, um, were actively in there or maybe paying them, uh, for some of it. Um, Clubhouse, uh, really became, um, a unicorn during, uh, um, COVID, during the pandemic. Um, but these days I was struggling to find information about how many active users they actually have. Uh, when I was looking into it the other day, the website wasn't really working very well for me. Um, I tried to use it back then and I didn't really see the value. So I left after a while. But this happened to a lot of companies and not just unknown ones, but also big names like these. So I want you to put in the comments on the chat, what does product— what does product adoption mean to you? So just use the chat that you have here. I'm going to look into it a bit and read some of the comments that people are putting over here. So everyone, please don't be shy. What does the product adoption means to you? So I see already some people are coming in. How much users are returning and using the product, regular use of the product. Workflow integration, uh, product becomes part of a routine. Good. So people are talking here about a workflow, a habit, uh, integrating into their product. How many new users use the products? Talking about new ones, uh, changing the product according to market needs. Okay, so this is adoption of the product to the market. Uh, let's see what else is going on here. User that comes online once a week and conduct a A rating usage. So this is like weekly active users, I would say. Start using a product, end users interactions, utilization. Good, great. Lots of great answers over here. And it's probably all of the above. So the right answer here is that there is no one place that we can say this is just product adoption. It's actually a continuum and that's what we're going to talk about throughout the session today. But thank you so much, everyone. I love the participation over here. Now, before I continue, I do want to present myself a bit more. One second, just find my screen here. I was looking at the chat and now I lost the right screen. Here we go. Again, my name is Moshe Mikhanovsky and I'm Product Director at Root Quotient. At Root Quotient, we help our customers strategize, build, and scale better product, uh, products. We have worked with, uh, you know, over 150, um, products to, to build them, different scales, uh, for all the way from startup, zero to one, to scale up, uh, growth and enterprises companies. Uh, we have over 150, um, uh, product specialists these days. And we work in multiple domains, lots of experience in, in a few sectors, but we are sector agnostic, so we, we can help in any sector. You can see some of the clients that we have and some of the products. And I've been with Root Quotients for over a year and a half. I love talking about product management and everything about products and strategizing about products and how to build products, and then how to get them adopted by the audience. So let's continue. I hope you got a bit excited about what we're going to talk about today, and, and we will move on to, to, to the meat of it. Okay, so the next thing is I'm still having— here we go. So, so the next thing is like, I want to make it clear up front that adoption is not exactly like usage. So some of you have said, you know, how many users are there, or how much they use the product. It's part of it, but it goes much deeper than that. And that's where we want to look into behavior. We want to see what is the behaviors that people are using and what is it that we have to focus on in order to improve that behavior. That behavior will eventually improve the usage, but it's the metrics and the things that we have to measure for that and look for are usually much deeper than just how many users we have or something like that. Um, still have a bit of a delay here. So if we're looking at metrics, on the left side we listed here a few vanity metrics for adoption. So things like signups, so how many people sign up to our product, or application downloads, or just daily active users without actually knowing what active means. Um, if someone just came into the product and signed in and didn't do anything? Is that considered daily active user? Numbers like that are really vanity numbers and we have to go a bit deeper into the behavior. That's where on the right side you will see some behavioral metrics that are associated with adoption, the time to first core action. How much time does it take to people to realize the core action in the system that give them the value for that value they are using the product. The frequency of repeated actions. So it's not just doing it one time, but actually how many times are they doing the same action? And some people mention habits of users. Numbers of drop-off after onboarding could be another way. Maybe it is a negative way, but you want to reduce that number. but it still shows you something about the behavior of the users, etc., etc. So we always look into more behavioral metrics rather than just those numbers that don't say much about the actual usage of the product. Next, we want to shift a bit. What are the questions we need to ask ourselves when we look into what to develop next and what's next on our board. So from thinking about our roadmaps and thinking about, you know, bug fixes, UX and UI and UX enhancements, or, or new releases, so the things that we have to develop and usually will go into the roadmap, we have to be a bit more, a bit more empathetic to the users. And see what are the users that— what are the questions those users are going to ask. So this is really throughout the duration that they are using the product, all the way from a new user that is coming into the product. They found the product and they should— they are asking, why should I use this product in the first place? So what is it for me that I'm going to try this product in the first place? What does it do better or different from any other solution I might use today to solve the same problem? Usually problems are the same. Um, the many problems have not changed for many, many years and decades and generations. The solutions are different these days because of new technologies, emerging technologies, new ways for people to do things. Then the users, as they start using it, they will have these questions or this mindset, even if they don't physically ask this question. But this is really what we want to answer. Is it easier than what I used before? Do I need to change how I do my job? Now, job is not necessarily just in the workplace. It could be anywhere that they use the product. So job here is more is what is their job to be done? So what is the outcome they're trying to get from doing something? Do I come back without being reminded? So are there things there that create a habit and I really want to use it? So I will take the effort of doing it even if I don't have the habit yet, or I need reminders all the time to use it. And then when they start using it, How often do I need to use this? When they use it for a while and maybe there are some other products, competing products coming up, they might ask, why might I switch from this to something else? Maybe there is something else better out there. Or if they're not using it, they used it in the past and now they're dormant and they might say, why I might want to come back and use this? So all of those are mindsets in the retention curve of users, and those are the things that we want to pay attention to. So what we have come up with through our hands-on work with our clients, early-stage companies, as well as growth-stage startups, we came up with this ADORE model. And this is a model for behavioral adoption. So it's only focusing on behavior of the users with different KPIs and different things that we want to measure throughout the adoption of the product. And I will go into a bit of details in the next slides what each one of those letters stands for. But the idea here is that we can help diagnose, interpret, and strengthen the product adoption based on each one of the stages, so the lifecycle of the user in the product. So the first one, the A, stands for aware. And here what we're saying is users somehow have to find our product. So either there is a push We have to push the product to the users. So we have to do marketing, find users, convince them why they want to try it out and stuff like that versus a pool where the market is coming to us and the people are coming to us. They've heard about the product and they want to try that. So there are different ways to do that. Some of it is done by, you know, different type of smart marketing, but some of it can be done by the product itself and the problem that it's solving. And this is where we want to move from a push to a pull. Then the next stage is the desire. And in the desire, we are basically saying, yes, people know about it, but now they want to try it. So what is the desirability of this product for, for users? And here, We're looking to see what are the stages that people are moving from hard onboarding. Maybe the onboarding is not that easy. When I'm saying the word hard, I mean there might be challenges to the user, there might be challenges to us to onboard those users. The onboarding process is not very slick. Then all the way to a fully self-serve product that they can do it all on their own. So they have desirability, but they can also go through that process and become users. Then the next one is opt-in. And in the opt-in, we are basically saying they didn't just find it and they didn't just desire it, but they also find the intended value. So they got from start using the product into realizing the value of the product. Here we want to really move from users that just sporadically using the product. They download it, they try different things, etc., to users that on an ongoing basis, every time that we get new users, we actually get them to find that value as fast as possible, like a core value or whatever that is. That we define. This is the core that we create for the users in the product, and then, um, we, we can replicate that with new users as much as possible. Uh, the next one is repeat. So repeat is once they find the value for the, for the product, they also come back again and again. So how repeatable it is that they're coming back to use it. And here we are moving from a one-time usage to a time-based active usage. And when I'm saying a time-based active usage, I mean, this is where daily active users, weekly active users, monthly active users come into play, because these are time-based metrics. Either depends on your product, what makes sense to you. But we want to measure that they are really active users, that when we say the word here active, it's they get the value, the intended value of the product. And it's not just they logged in because logged in is a vanity. And then last but not least is evangelize, and that's the E. And here is users love the product and they also extend it. So when they extend it, there could be several meanings over here. One way to extend it is that they invite other people. They tell other people about it, they explain to other people why they love it, etc. Another one is that they embed it into their workflow. So it's not just a standalone product, but it's actually integrated in the workflow. So it's going to be much harder for them to replace it. Because until they do that, they could always take it from— replace it with another product that does a similar thing. You can think about it, probably most of you are using some type of GPT, ChatGPT or Claude or others. How much integrated is it in your flow might mean how much you can actually replace it with another one that is doing a very similar thing. It's easy sometimes to take examples like that and try to apply them to your product that you are developing. And see how your users are actually evangelizing it by telling other people and also by integrating it into the workflow. Okay, so what we're saying next is that if your product is not being adored, um, you're either pre-PMF or heading towards a pivot. And we will talk next about this, um, what pre-PMF means and many things related to that. But I, I want to actually say this is a— there is even a stronger option, and the stronger option is that you might have to kill your product. So, um, pivot is one way to continue moving on with a different idea. Um, killing your product completely and maybe killing the companies is sometimes a choice, uh, um, founders have to do. If their product doesn't reach that adoration level. Let's move now forward with the 3 stages of behavioral adoption because it's really divided into these 3 stages. You probably have heard about this in the past, product-market fit and growth. But I want to put it a bit more in context of what we're talking over here. In the pre-product-market fit, We are basically validating ideas. So we are validating that the problem we're trying to solve is a real problem. We're validating who has this problem. So the market, the clients, the users, and then we're validating a solution for that. Before we validated all of these, that there is a good fit between all of those three, there is high chance that we have to pivot one of them or some of them. Based on everything we learned, we have to make those decisions if we have to pivot or keep the route. Now, based on what the product that we have, then we, we need to have some type of a minimum number of people and clients that we'll need to learn from or learn with as they are becoming like your design partners or whatever that is. B2B is usually the numbers we're talking about are 5 or 6 clients. With B2C, it's much higher. I'm not an expert on B2C, so There might be better people to ask how many users you need to fit your product, to validate your fit. But in general, you need to have some basic numbers and some massive numbers of people that you know there is a real problem, there is a real market for this problem, and then there is a real solution we can provide them. The product market fit. This is once we find this, we validated this, then we know we want to grow and we want to scale, but we're not there yet. We have, let's say, in B2B only 6 clients. Now there might be a lot of things that we have to do to move forward into actually that growth stage. So this is why this is a bit of in between before we actually can grow. And here we validated and we found this problem, market, and solution, but we actually want to validate access to a scalable market. So the scalability is all about, you know, growth is all about scalability. And this is where we want to make sure we know how to scale it so the technology can scale it, our processes can scale it. Everything there can work. And as we go with that, we might lose our product-market fit because things could change in the market, or things could change because of things we have done with the product. So we always have to be on top of those things, and we'll talk about some of the things that you can check, uh, throughout each one of those stages. And then lastly is growth. And in growth, we already know that we have access to a scalable market Now we are scaling our operations. We're scaling how we sell to that market, how we find the market. Hopefully in that case, in that stage already the market is coming to us. So the pull from the market is much higher than the push to the market. But we still will have to deploy marketing and sales. It doesn't mean that we don't need that. It just means that there is more emphasis on how can we serve this market better in scale. And I see people are coming and putting questions in the chat. Maybe if you want to put the questions in the Q&A, and then later on at the end, we'll go through the Q&A questions. So there is a place for those questions in the Q&A. Thank you. Okay, so talking about that, let's break down each one of those a bit more. Um, so in the pre-product market fit, it's much more important to focus on a behavior fit than to, um, you know, to— in order to understand what is that product market fit is going to be. So yes, we are trying to reach a product market fit, but we don't know yet what it is. So we are focusing on things that are in the behavior of the users to learn that and to understand what actually is going to work and what is not going to work. So here we are not in growth yet. Uh, it's, uh, this is way too early for growth. Uh, many of the, um, leaders in, in the market are always saying about that before you're getting into the product market fit and you know that you have that product market fit don't start growing yet because you need to find that fit between your problem market and the solution you are providing. Things that we're focusing over here are things like, are users repeating the core behavior without being pushed? They are getting there and they're repeating this core behavior that create the value to them. Do they tolerate bugs or incomplete flows? Sometimes it doesn't have to be perfect as long as they get that value. If they get that value, the value to them is much more perfect than— much more important than the product being perfect. Are they giving unsolicited feedback? They want us to be successful. They want us to get better at the product and give them the solution for that. That's where they— it's another behavioral signal. Are non-users staying away for specific reasons? We want to check why people that started it are not coming back or why people that know about it are not trying it, things like that. Here we have a few more in-detail signals to look for. Some behavioral signals, and I'm not going to go over all of these. We will have those slides available for everyone. Also, at the end of the presentation, we'll have a link for playbook, and the playbook will have, you know, everything in there as well. But things like some behavioral signals to look for in the pre-PMF is a few users return and repeat the core actions. So you have some users that are coming back and see the core action, but you have to see what's the number of users and why those that are coming back are coming back and those that don't come back So it's all about this retention, um, um, you know, KPI. Um, you want to see here, you know, signal signs for a pull versus a push, like we talked about before, uh, unsolicited, uh, feedback, etc. Um, you also want to look, of course, for red flags. Um, so there is sign up but not repeat usage, that's one of them. Users are dropping off before they reach the, the core action that you defined as the core action for them. There is confusion about the value proposition, so they're not even sure what is the value of your product. And look for signals like that, because this type of confusion will kill the adoption of your product. Uh, what? Okay. Um, and then, uh, what can you do about that? So there is— here we go. Something with the slides doesn't— one second, I see that you're not seeing everything here. And I'm not really sure why. Let me come back to this. Here we go. So what do you do? There is a lot of things that you can do. And again, in the playbook, we will list quite a few things that you can do based on the stage that your user is at. Who do you do it with? Do you do it with the users or with the clients or the, or the buyers of your product, etc.? But some things that we, we highlight over here is interview churned users and ask them, you know, what were their expectations? Why did they churn? What did they find? What they didn't find in the product? Uh, did they find the value, or did they understand even the value of the product? And things like that. Improve the time to value. So improve how fast they're getting to that core value that you're trying to get them in the product. And that's really important. The fastest they will get to that, the faster they will see the value in the product and will want to use it more. Optimize it based on their behavior, not based on assumptions. So always learn from what people are doing versus just your assumptions about how they should do it. Now pivoting is also important, especially at this stage of pre-PMF. If we cannot find, um, a product market fit and we tried— I'm not saying you should try it only once and then, you know, give up, uh, you should try different things and, and, and get enough information to see whether it's going to work or not. But when you decide to pivot, then you want to revisit one or more of those, um, aspects of the product-market fit. Your problem— so is this a real problem or the right problem to solve? You have to pivot on the problem. The persona or the market, the, the users— who are the users this problem, um, that have this problem? Or do you need to pivot? Maybe they are not the right users. And/or the solution— so is the solution you're providing them the right solution? So there is always a combination of those when you, when you try to pivot. So, um, moving next to the PMF stage. So you hear users do stick in because there is a product-market fit, but, uh, and they stick in when it, when it breaks. And here we want to start testing how do we scale it to the next level, to the growth level, right? So there is a clear fit, um, and we, we can focus on, on other things over here. So for example consistent repeating usage despite the rough user experience, increased feature adoption tied to core workflows. So there are areas that we might have not focused on before, and now we can double down on those to say, okay, maybe there is more hábitos creation or integration into the workflow. You will see communities created around this. So people are talking to each other about using this product outside of your control many times, and listen to that, see what they're saying. And this will also bring that pull from the market because those communities, depends where they are, of course, will tend to bring people that have not yet used the product. And then you will see also different signs of habit formations. Things to look for over here, some behavioral signals to look for. Users return and perform the core actions repeatedly. It's not just that they do it once, they will come again and again. This is where you have these time-based active users to measure. Retention is consistent across new cohort. You look into Every new cohort— well, cohort really depends how you, you get users and how you onboard them, but it could be based on the version of your product, it could be based on rollout of the product to different locations, things like that. And you can see, is there consistency over there for their retention, or is it different between new cohorts? Users adopt the product into their workflow, so deeper integration to their process, um, the time to value is shortening over time. Some red flags to look over here is early users are loyal but new ones churn faster. So that could happen if you, um, did find a product-market fit, but the way you try to scale it and sell it out to, um, you know, the, the broader market, uh, doesn't really work for you. And you're not finding those same people. Maybe there are different people that you're finding, so they churn. Or maybe you extended it to people that are beyond your product-market fit. So you still have to check, do we still have product-market fit based on everything that we are getting now? The usage drops when incentives are removed. So many times we will give some incentives like freemiums or discounts or additional functionalities that now we put them behind the paywall, whatever that is, we want to see how does that impact the behavior of people. Do they still see the value in the product and the adoption stay the same of new, um, of existing usage or new users, or does it drops? Retention curve flattens or falls with each new cohort. So retention curves, usually healthy ones, usually looks like this, or they go up, which is great. But if they start going down, that means more users are leaving the product over time, so they churn. The feedback is positive, but behavior isn't consistent. So you will see some differences between what people tell you, the users tell you, to how they actually use the product. So look for those things as well. So some things you can do over here, you can optimize onboarding for aha moments and speed to value. So, so look into how do people find what is the path to get to that core value to see it and they will see this aha, they will have this aha moment. And how can you reduce the time for that? So reduce any traction, any tension they might have, to get to it and they should get to it as soon as possible. Watch behavior across different types of users. Try to build habits. Create habits for the user rather than just adding features, feature, feature. The more features that you add might create more confusions for users, especially if they don't have habits yet. Try to focus on how you create that habit for the users. And then, um, strengthen stickiness before investing in scale. So don't start scaling until you see those signs and numbers for stickiness. And then, uh, for growth. So in the growth, we, um, want to scale behavior. So it's not just usage, meaning not just like how many users and number of users and just, uh, those vanity metrics, but it's more about their behavior. How do we scale the ability of more and more users to use it more and more time and to integrate it deeper into their workflows and create deeper habits in there? It's probably better that you have less number of users, but those are much more using your product much deeper and see the value in it. Over time, you could create more value to your organization as well from those users. Things like here are which actions are being repeated most often, so you can focus on those things rather than everything around it. What behavior is being referred or shared? Things like look for behavior of people when they evangelize, how do they evangelize, and what do they evangelize? They may not evangelize every feature or just a product in general, but a very specific behavior in it. Are new users following the same adoption patterns? Look for adoption patterns and compare it between cohorts of users and see what happens there exactly. Are you reinforcing the behavior loop or distracting from it? Especially when we add new features or we are changing the way things are working, trying to improve them, sometimes we are reinforcing, sometimes we are distracting. Pay attention to things like that as well. Just a bit more in details here. Some behavioral signals to look for in growth is high activation rates for new users, cohort retention is stable or improving, feature adoption is extending across users, etc. Some red flags to look here, The retention curves weaken over time, so it doesn't keep or grow, but it goes down over time. Engagement shifts to secondary features. So people are moving into something secondary that wasn't the intention of your product or not the core intention of your product. Power users. Look into power users. They are those that will evangelize you eventually. Are they disengaged or do they churn quietly? Growth driven by paid effort and not by behavior. So do you still need to spend money to, um, you know, push the product to the market, and, uh, and, and this is the only mean for growth, or there is a mean for growth also by people behaviors that has nothing to do with you pushing it into the market? And some of the things that you can do over here, you can, um, and, and some of them are similar to the things we already discussed, like optimizing onboarding and early experience. But now you have to do it at scale. So doing it at scale might be, uh, different than you do it, um, uh, you know, in the early stages. There could be more automation over there, there could be more processing power that you need, uh, there could be other things that needs to happen. Monitor behavior across user segments. Um, don't assume product-market fit. Assuming product-market fit all the time— once you've got the product-market fit and you feel, okay, now I'm there, um, make sure you always assess it to see, are we still in product-market fit? And, and it's, it's always a thing that we have to validate because things do change over time. Uh, protect and reinforce your core value loop. So this is really like, what is the core that creating the value? And you learn that over time. So now you should know what it is and then make sure this core value is protected and reinforced. And protected means also like you have to see what's going on in the market. Are there any other solutions that coming up that might be, you know, clients might want to move to that new solution. So why they will choose to stay with you. And then always re-engage with churn risk users. So there is, um, I, I've been, I've done it in the past in a company I worked for. Um, I didn't do it myself, it was, uh, much smarter people than I am in the data science, but they created models for predictive analytics to understand based on what criteria that we will know that people are going to churn. They that customers are not going to renew their SaaS license. And we knew 3 months in advance before, so we had signals based on their behavior and we were able to get to them, talk with them, and basically reengage with them based on this churn risk and save them. So the churn actually was reduced after we did that. So these are other things that can be done. You need to have quite a bit of data to do that. And like I said, smart people that know how to analyze this data and understand what are the signals for, for this future churn risk. But this is another method that you can do for sure. Okay, so moving on, um, we have also, um, put together for our clients this way to score where they are in each one of those, um, stages and understand a bit more where they can move, you know, what they should do next, and, and where are they exactly, etc. We call it the product adoption scorecard, and I will show you next an example of what does that mean. But I want to make sure you understand that this is what I'm going to show you is not— this is a scorecard for everyone. It's very specific for each client and each product. So you will have to define your own scorecard for that. But this is a good way for us to have a better understanding of where we are and for the company behind the product to understand where they are. And you could adapt that to your situation. So this is an example in the next one. And here what I'm showing is like, scorecard for the pre-PMF stage. So there are different indicators or metrics that we're using over here based on the ADORE framework. So things are related more to early stage, um, um, you know, awareness, or things that are related to desirability or to adoption or opt-in, whatever that is. We identify what are the 5 key metrics that are important for this specific product, and then we score them based on the information that we have at the time. And I will talk in a couple of slides when exactly we do that. But the idea here is to get a bit of a scale to say, oh, we are not there yet. There is no behavior fit yet. Or there are some emerging signs, or we believe that we got into a product-market fit validation. Again, this will change based on your product, your situation, what KPIs make sense to you. So, um, then we do the same thing in a PMF, um, and in the growth scorecard. So each one of them will have a bit different because There are things that you need to— that are more important at that time that you need to validate. But the idea there is to actually see that we are moving forward where we are right now and how we move forward with that. Now, the, um, how to apply it. Like I mentioned, uh, we— you, you really want to customize the KPIs in there, um, based on your product. You want to use some range-based scoring. Some of the things, if I go back one second over there, some of the things are easy to measure. For example, the percentage. Sorry, I went forward instead of going backwards. Here we go. A percentage, you can say, if I have 50% of my users are repeating a specific core action, maybe that is, um, um, 10 out of 20 because it's 20%. Uh, but something like, uh, time to first, uh, self, uh, um, self-indicated meaningful action initiated, meaningful action, that might be a bit more tricky for you to set up. And usually what I recommend to do is to have your, your specific, whatever makes sense to your product, almost like a scale to say For our product, the end results we want to get is that they see the value within 5 minutes. Again, some products it makes a lot of sense. Other products, 5 minutes is way too early and it takes time because there is so many things involved in that product. Then you can go from that and you say, okay, the 5 minutes is our goal. Maybe that will be a full 20 for the score. But then you create almost like a range of specific values so you can use for that. So, so the idea there is that it really— the scorecard fits your specific situation. Um, define criteria for the qualitative indicators. So sometimes you will have quantitative, but sometimes you will have qualitative. So for qualitative things, you want to also see, you know, how do you score that. It could be based on a number of feedbacks that you're getting, type of feedbacks you're getting, Who do you get those feedbacks from? Again, whatever fits your situation. Focus on trend, not absolute scores. So this is more directional. It's very hard to quantify exactly. So that's why it's really just a number between those, uh, 0 to 20 that is, is a directional towards it's, it's the ultimate number or it's the lowest number, anywhere in between. Then leverage results to drive iterative improvements. You want to see, how do I improve this to get to the next level on each one of those? It's not just about the total score, it's also about each one of those metrics. Then we have what we call the— first of all, the frequency. I mentioned before, how often do you want to do that? So you probably want to track it on an ongoing basis where early on, pre-PMF, you want to do it more often, like, you know, weekly. That's what we recommend because things are moving rapidly. There is a lot of changes in the pre-PMF. You might pivot many times and stuff like that. So you do want to do it quite often. Once you get to the product market fit, you can do a bit slower, like maybe every sprint, like 2 to 3 weeks. Then when you grow, probably monthly will work well because growth is more about building in scale. It's the same product, it's the same problem, it's the same solution, it's the same people or market. Now it's more about the scaling. Then we want also to put all of them together on one graph. Now each one of them might mean a bit differently, so we have this normalization table. I mentioned earlier the playbook that you can download at the end. This is mentioned in the playbook as well. And we have that table in there. But the idea there is to be able to draft this graph. So this graph is really over time, how do we, when we start towards continuing, are we on the right track to moving to growth? So from pre-PMF to PMF to growth and beyond that. Or is it going up and down? So what does that behavioral maturity curve look like? So the idea for the normalization table is to be able to take each one of the numbers we got from the pre-PMF scorecard, PMF scorecard, and growth card, normalize them into one graph so we can show it in that way. And this looks like a pretty healthy and very quick— only in 8 weeks we did it. Just for demonstration, of course. But it's really interesting to see in real life situations how these graphs actually look like and whether you are getting farther from where we're trying to get or closer to where we're trying to get or you are on track. A few examples I want also to talk about what to fix when adoption drops. Because sometimes things will break and things will not go as expected or something like that. So just a few examples that, that we have over here. The first one is user drops off after the first use. So this is very common. We released an app or released the product, we got some interest, people, um, you know, download it and used it, and then they drop it, right? So there are a few things we, we can do here. We want to focus, for example, on reducing the time to value. So how fast they actually see the value. So maybe they just didn't see the value in the product. Now, whether the product has the value or not, hopefully you built it in a way that will create the value. If you build it in a way that doesn't create any value, that's a different problem that you should address much earlier than that, right? But, but here you really want them to get to that value as soon as possible. Um, you want to remove setup blockers, etc. So there are a few ideas over here. Another problem is user repeat actions but not consistently. So they do come back, but— and they do some actions, but it's not consistent. Here, they maybe sometimes they will come once a month and other times they will come twice a week, and then they, they drop for a few months and they come back. So you want really to encourage them and clarify to them when and why they want to, to return to it. Some contextual nudges. So nudges, you know how sometimes you get emails about, oh, maybe you should try this or try that, or message from the app or things like that. Or even inside the app, you might use it for one way but not in some other ways. So there could be nudges like that. You want to reinforce some action reward loop. So it's It's not just about actions, but also about the value that they're getting. So what is the rewards they're going to get when they use that? Another problem, new cohorts drop while power users stay, and retention drops post-scaling. So there is always, you know, in each one of the stages, you could have different problems. And you can, you can look through those. I'm just looking at the time. So I want to make sure we have more time for questions. Let me move on to the next one. Here in the document, in the playbook, we have a whole section towards the end of the playbook with different ways for you to try fixing behavioral issues. In different stages of where the user is, like new users, early adopters, uh, midterm users, long-term, um, dormant users, etc. And then in each one of the stages— so this is just a few examples here on the screen, but like I said, you, you really want to download that document because it has a full, um, list of things in, in the tree structure. So it should be quite useful for you to do that. And then I just want to conclude this with a bit of an example. And the example we chose to use today is from Slack. Now Slack originally started as a game company called Tiny Speck, and they developed this product called Glitch, which was supposed to be a web-based massively multiplayer game, but it failed pretty miserably. So when it failed, they decided to pivot, but they didn't pivot the game, they pivoted the entire company to be instead of a gaming company to be an enterprise, uh, software company because they had this communication product that they developed internally for themselves, um, and then they rebranded it to be called Slack, right? Um, originally before pre-PMF, they really went after a few clients that they were able to try it with, so they didn't publish it. It wasn't an external you know, release. People could not use it outside, but they found 6 to 10 clients, uh, in different sections and different sizes of organizations. And, and they were building it, you know, for them and with them, and all the time looking at the behavior of the users— what blocked the users, what the users were able to do. And then you see some of the problems that they saw: team were joining but not returning after the first day. So it was really depending on the team, on the size of the team, and things like that. Lack of cross-team usage, not enough value spread— that was an insight that they found. And then they added the channels to simplify this. And then they looked for signals to see that what they added actually improved. And they found that when 3+ people used it in 2+ channels, retention shot up. So they saw that there is a correlation between specific behavior to retention of the product. Now, this created an outcome that they were able to early validate that this will work for, for, you know, specific companies and different types of companies. Then they went into the public release. They actually didn't call it a beta. They didn't want to call it a beta. But That's where they already have a product-market fit because they know this is going to work for this type of organization with these sizes of teams, and then they could launch it. But they still have problems. One of the problems is power users were hooked, but new organizations were not activating. So it was harder for them to see how new companies that got it and they wanted to use it, but they were not really activating it all the way. So what they— the insight that they saw from those that use it was that if they sent more than 2,000 messages, then it became sticky. And they're actually talking about more than 90% of companies that use it for more than 2,000 messages stayed users of Slack. So that's the thing that they wanted to encourage people to do. So this was the aha threshold, and then they wanted to replicate it and they were able to replicate it with other users. Now that they were ready for growth, they still have expansion slowed in new industries like education, healthcare, because they're behaving a bit differently. So even if you grow and now you're going into new industries, this is really a new market in some ways because it might behave in different ways. And now you have to adjust that, uh, you know, see that there is a good fit over there. Um, the usage was deep but invite rates dropped in new segments. So people still use it but they didn't really share it with other people or things like that. Um, what they did is added integration prompts and role-based onboarding. So they solved this specifically for those specific segments that behave a bit differently and maybe they needed more of, you know, role-based onboarding or things like that. The signal that they got was feature breadth and invite behavior returned to the new cohort. And then Slack became the async operating system and we know that the rest is history. Okay, so as I mentioned, we do have this playbook for you, the Behavior-Driven Product Adoption. And I want to shout out to our team at Root Cause for putting this together and, you know, helping me with all— with this presentation as well as the playbook and all the previous presentations in the past. So thank you guys, everyone at Root Cause. And here is a QR code for you. Uh, here we go. Uh, you can scan this QR code for you, and also we should put here the link. Uh, here we go. Uh, the Founder Institute just put the link in there, so you can also click on that and you can download the playbook. Um, there are also other links over here, as you can see, uh, our website rootquestion.com, both our pages, uh, root question and my page on LinkedIn. So please Feel free to follow up. Tell me you came from this presentation because I get lots of requests sometimes. Also we have a podcast, you are free and I encourage you to listen to. We are talking on the podcast, it's called Beyond Product Podcast, and we talk with founders of interesting companies that are using technology in some ways or fashion for their solution. There is a lot of great tips on those episodes. About their user journey, founder journey. So for all the founders over here, I think you will, you will really enjoy this podcast. And I think now we're open to questions. I'm going to stop sharing here. So this way, we can see the questions, I guess, on the screen if you put them. The link to this is still available on the chat. Here we go.

Yes. Thank you so much for this incredibly comprehensive, useful content of the resources. They will all be shared with the presentation to everyone who has signed up.

Perfect.

Our attendees really appreciated it. We have amazing comments, and quite a few questions. So I'm going to start with the ones that have the most votes. So this is the question from Roman Didenko: How is the ADOR model applicable for B2B software as a service products, especially complex ones such as cloud monitoring tools?

Right. Yes, definitely. There are lots of complex products out there and cloud monitoring tools. I would assume the users for those are not consumers. And they're also probably not— they're not most of the SaaS companies that are building cloud products, but it's more for the cloud vendors or something like that. Without knowing more information, it's hard for me to tell. But it's always about looking into who are the users, who are the buyers, and who are the actual end users, and what adoption behaviors they have over there. It is definitely different between different types of products. What you have to look for is to see in each one of the categories of the model, to see the intended users and the intended clients. How are they finding it? How or why would they try it in the first place? How do they get the value right away? How do they repeat it? And how do they evangelize it? So in this case, it could be that the first people that needs to find it are the administrators of those cloud, um, tools or or the clouds. Again, I'm not sure who are the users, but, um, you have to, to really see if they are going to be the one making the decisions to use it. They're the— and they're also you, the users. They will be the buyers and the users. Uh, if they're the one making the decision to buy it but not the users, then who are the actual users and what you have to do with each one of those? And then what does it mean really for them to find it? What does it mean to them to decide to use it? So all of those questions that we asked are still relevant over here. But yes, it is complex. It is also a complex problem to solve, I'm sure, right? So the more the complex is it, the more the problem is complex, The more complex is also everything related to that, including the behaviors of the users. I hope this answers the question.

Thanks. Yes, hopefully it helped. And someone has shared this structure here. Our thinking in terms of phases: find problem solution fit, ideation, find product market fit, testing at scale startup.

Yeah, I'm reading through this. Maybe it didn't come through properly in the, in the, um, formatting here, but I'm not sure why you say zero users in find product solution fit. Um, okay, so this is maybe before product market fit, it's the solution. I think you still need to have some users for that. I'm not sure why you put zero users over there. Um, because, um, you really want to validate your problem-solution fit with people. You're not just validating a solution to a problem, you know, in silo. You want to do it in the context of who are the actual users. That's why throughout the presentation I always said these three things: uh, the problem is real, there is a market for this problem. So there are real people that have this problem. It's not just you that have this problem. And then there is the solution is fixing this problem, right? So, so that's, that's where I do that. Now, what is the question at the end? Do I agree? So I probably don't agree for the zero users in the idea in the first one. Gain traction less than 1,000 users MVP. Um, again, I think MVP is in the first one and not here. Um, you want to show that you, you are creating value to these original, you know, users. Um, like I mentioned with Slack, they had 6 to 10 clients, uh, which was a good validation for them. Um, with, um, I don't know if your product is a consumer product, maybe that's why you have this number, 1,000 users. Maybe it's a consumer product. Um, yeah, so, so if you want, reach out to me and we can talk a bit more about that.

Thank you. Next question is, what are solid metrics you can use to assess whether or not you have reached product market fit?

Yeah, um, this is a very hard question to answer. And the reason is because of the word SOLID metrics. And maybe I'm more thinking about the values of those metrics than which metrics they are. Because it's not really— it's really— and the word assess is good in the question because it's more of an assessment rather than it's a yes or no. Okay. Usually we're talking about pull versus push. So the more you— when you feel that, when you see that there is more pull than push in the market, so the market is pulling, is asking for the product, is coming to you versus you push the market. Um, the second thing is retention. So everyone in the industry, um, agree that retention is the highest KPI to look for, but it's not the only KPI. So you want to look at other KPIs, and we suggested some KPIs, uh, you know, throughout the document, um, but it's not an all-inclusive list of APIs. There could be others, and it really depends on you, but retention is, is probably the most important one. And with retention, you do have this behavioral, um, all these behavioral aspects about How do you increase retention looking into the behavior of users? And I mentioned the graph of retention, which looks like that. So usually in a product-market fit, it will look, um, you know, quite, uh, get into a flat and doesn't go down over time. If it goes down over time, that probably means you don't reach— you didn't reach product-market fit. Um, if it goes up That's amazing. Most products don't happen overnight. And usually, we'll have to get through this before you get to that. So I would say when you get to this stage, that's probably is a good place to start. But again, not by itself, you need to combine with that also other things.

Thank you for this precise answer. And Let's put this one by Jake. I'm already in the growth phase of my B2C software as a service. When I go about to expand into a different country, should I go through pre-product market fit, product market fit, and growth phases again in the new country?

Yeah, so first of all, congratulations for being in the growth, uh, and that's great. And you might want to do some testing to start with. It might be that your pre-PMF will be very, very short because you have gone through that already in the past. You already have a product, etc. But I would definitely try to go through these three only because, unless you know, you have data, you have confirmation that the new country behaves exactly the same as as where you are right now. And I'm not sure if you mentioned where you are. Oh, in the States. So maybe, maybe that's where you are. So, so, um, if the new country behaves completely differently, you know, it behaves completely differently, then you definitely want to go through a pre-PMF specifically for that country to validate. You don't— you may not have to develop anything. You may, you may have to go through some localization, uh, you know, language and other things in your product But in general, you want to see that whatever you developed, the problem is still the same problem in that new location. The people over there, you can find them, of course, and the solution that you created will fit them as well. Because if they do behave differently, you may have to either drop that country or build something different for them.

Awesome, thanks for this. You have shared a lot of information already and it's already an hour, so maybe just, uh, 2 more questions? Sure. Okay, um, I like this one: What approach will you take to drive adoption in the industry which is for decades relying on Excel and messaging do business? How do you do a shift of perspective dominated by 50+ years generation who do not want to change their habits?

Oh, that's a really good question, and I've been in that situation myself, uh, not so much for the clients of my product but for the company that I worked for and the way they worked. So I hear you, um Yeah, so this is, this is tricky, right? Because you do want to change behavior, but you have resistance to change. And, and the question is whether it can be done in stages. So you don't introduce to them everything at once. You only introduce, you know, one core value in there, or you introduce the, the technology first. And then the solution, um, you know, the, the, there is a similar problem to that, which is using emerging technology, uh, for new solutions in markets that are not yet fully adopting this, this solution. And this happened, you know, again and again with people coming up with some ideas, but if the market is not ready for that, then how do you, how do you get there? And many times it's, it's, um, it's failure. It's failing because the market is just not ready for that. It could be it's too expensive for the market. It could be there is not enough distribution of this technology in the market. And it could be education, right? So your problem here is on a smaller scale because you are introducing a new technology, an emerging technology for this specific sector, and, um, they are not ready for that. Now, it might be a bit easier only because the technology is already there, if this is existing technology, right? Um, and it's not that expensive for them to buy. So it's really all about showing them the value. And then the question, how do you show them the value while fighting the resistance? And I would try with breaking it down to smaller components, hopefully, and see, you know, what's the least one I can do. There is a method actually talking about— we mentioned Slack before. Slack is using this method of introducing features over time. So when you're a new user to Slack, you don't necessarily see all the features available in Slack. Even if you have all the permissions and you're a regular user, you may not see everything because it's overwhelming to try to learn everything at once. So they introduce things over time. So only after sometimes you use the product for a while, they will introduce more features and say, oh, maybe you're valuing this feature, or value this feature, or you can do this and you can do that. So in the same way, you could maybe introduce your product to them in a very, uh, staged way and controlled way to, um, just give them something very basic that will already create value just to get them over this resistance. Again, hopefully this is helping. It's hard to tell specifically without knowing more details, but feel free to reach out to me after and then we can talk more about that.

Awesome, great advice. And one last one by— what would be some of the ways to strengthen stickiness beyond just onboarding?

Um, sure. I mean, stickiness is really all about the value to them in the product. So the more, um, they get value in the product, the more sticky it will get. Um, you also have to look into what is their cost for them to switch to another product. So the higher the cost for them to switch, the more likely is that they will not switch and then they will stay with you. So there are different ways for that. Um, some companies are doing this— it's not always, um, I don't know if I want to use the word ethical. I think it is ethical, but it's not always very nice to the users. So for example, uh, giving the user a hard time to get the data out of the product. Or giving the user a hard time to disconnect from the product, or whatever, you know, things like that, uh, to, to, uh, get them to, to move to another product. But a cost to, to switch is not always about hard costs, you know, like the license you're paying for it. It's also about all the time it will take you to learn a new product, the time it will take you to offboard from the old one to the new one, to convert your workflows and behavior and data and stuff like that, right? So, so that could be one way as well. Um, you, you know, adding new features could be a way to do that, but you have to be careful there because not every feature, uh, will do what you're intending it to do. So you really want to, to test it to see it gives them more value and it creates, uh, better stickiness because you give them more value, right? So I've, I've used products in the past that added new features and it actually detracted from the stickiness, where, um, other products that they added new features and I'm like, oh, okay, good, great, this product can now solve this problem for me as well. So now it's two problems for the price of one, right? So there could be many different ways Great.

And with that, we will conclude the Q&A section for today. Perfect. And thank you so much again for joining us. Thanks to everyone who also participated. And yes, I see that our attendees are using the emojis. They really enjoy the content.

Oh, great. Thanks, everyone.

And you have Moisés' contacts, you will receive all the links and follow our next event for more interesting topics. Now we're going to break out into our networking rooms, and Moisés, it would be great if you can join us. If not, you can find him by the contacts he shared. So Would you have any last words or advice to say before goodbye?

Um, keep focus on the users and what really the users need and not what you think they need.

Perfect, thank you so much.

And I'm also dropping my LinkedIn here in the chat. Oh, sorry, I just wrote it in the Q&A. Not in the chat. So I will copy it into the, to the link, so this way everyone has it as well.

The chat will stay open even in the networking lounge. So see you out there and have a good day or evening. See you.

Thank you.