How a Focus on Speed & Simplicity Can Fuel Your Flywheel with Loom's CEO Joe Thomas

I know that this is actually a very interesting time of the day because this is a globally distributed audience and LOOM is a globally distributed team. So when it's around 1pm, it's post lunch for those that are Pacific time zone. It's also a little bit later for Eastern time zones. And then if you're joining us from Europe or apac, it's like either very late or very early. So I appreciate you being here with me and I hope that you all are kind of similarly stocked with me in terms of having I got my post lunch afternoon coffee in order to fuel our discussion around the Flywheel. So really excited to walk you through kind of what we have learned over the course of time in terms of creating a business that is largely driven and sustained by what we believe is a flywheel motion, which is why we've been able to build a video messaging product that has largely taken off. And so three key takeaways that I want and hope that you all are able to walk away and be able to answer these is understanding what a flywheel is and why it's important and the process by which you articulate your own. You won't be able to actually articulate your own by the end of this understanding the framework of how you accelerate your flywheel. So what output metrics would you potentially and what Loom focused on and the input metrics plus experimentation that we in the earliest days of LOOM were really focused on. And then third is understand how to rally your team around the flywheel for organizational alignment, clarity and impact. So agenda is correlated with this, of course. Like what is a flywheel? Loom's earliest successful flywheel. And then experimentation on Loom's flywheel from their pro tips, gotchas and next steps. And if I do my job right, hopefully we leave at least 15 minutes, but ideally it's 20 and so that we can make this very conversational in nature. So write down those questions or share them in the chat and I think that we can have a fantastic discussion towards the end. All right, so what is a flywheel? For those that actually already know what one is, I hope that this is a good refresher course for you and hopefully reinspires you to look at yours again with some fresh eyes. Or if you're just learning for the first time, this is a concept and a metaphor that was based off of a very real mechanical engineering thing called a flywheel. You might say like, oh yeah, this is a nice size gear, but what you'd see in the bottom Right hand corner here is. That's a full grown human standing next to it. So this was a massive thing that takes a ton of effort in order to push and in order to get going. But once you keep push, the flywheel ends up building momentum and it starts to generate momentum on its own. And so if you really want a tactical example of this, as you might remember a merry go round as a child where you were pushing that and maybe you had five or six of your friends on it and you're running and it's really hard at the beginning, and then at a certain point it starts to take off on its own where you actually can't keep up with it. And so you kind of like fall tripping over yourself in terms of trying to get it to go as fast as possible. And that is conceptually what a flywheel is, that if you can generate enough momentum that it starts to go on its own. And that is when a company goes from good to great. Good to great is actually an intentional phrase used here. Jim Collins was the one to first write about this in his book Good to Great. And I will actually make a separate recommendation for reading for you. All that most people don't know about, which is he came out with something that's specifically relevant to flywheel and is a monograph to his book Good to Great. So this is the quintessential and core example of a business that actually was able to articulate theirs in the earliest days, where Jim Collins went and worked with Jeff and the Amazon team in the early days before Good to Great even came out, in order to explore this concept of a flywheel. So just going through it step by step a little bit is that selection and convenience was something that Amazon was really focused on and felt like was critically important. And if you increase selection and convenience, that you can drive a better customer experience. Better customer experience drives more traffic to Amazon, which then attracts more sellers because they have an audience that they can go after and sell to. And more sellers increases selection and convenience. And you can see how that just goes round and round. And then if you actually are able to execute on this flywheel and get it going, there's ways to innovate on top of that as well. So a core principle and belief of Amazon was to drive a low cost structure. Like one of the examples from early days of Amazon is they would get two sawhorses and a door from Home Depot and that was a $12 desk that they were able to build versus buying the next cheapest fully assembled desk, which was like 40 bucks. And so saving that $28 per desk was something that they could put into lower prices. And lower prices led to a better customer experience, which again leads to an acceleration and a momentum in the flywheel. And so Jim Collins, going over to work with Jeff and the leadership team in terms of articulating this, this was the first business to say, we have a flywheel. This is what we're going to be pushing on, and this is what the core focus of the business is. And so one of the most critical elements of a flywheel is momentum. And momentum is actually, yes, both like the energy that you are using to push it, but also what is the friction. And so removing friction actually becomes a almost religious undertaking in terms of how do you remove as much friction from your user experience as possible. And a great example of how this ended up playing out for Amazon is their Prime Membership offering, which was or still is an example of one of the most successful subscription offerings of all time, which is very relevant to the SaaS audience that we have here today. And so the key friction that they wanted to improve the experience against was the fact that people just, for whatever reason, couldn't get over this concept of shipping costs, is that they said that, oh, if shipping costs are going to be 5 or $6 per item, even if it ends up becoming more expensive for me and time and money to go purchase this in store, that's charging a higher price and I have to spend 30 minutes versus it being deliver to my door. Like, I'm just, I'm going to go to the store, I'm going to default to my natural behavior versus purchasing something online. And so they developed Prime Membership, which you pay for those shipping costs upfront in a subscription fee, and then you just don't have that allocated to each purchase. And by removing that cognitive and psychological friction, that was something that meaningfully improved the customer experience from people's perception. And that was a removal of a key friction point that allowed for them to build a better customer experience that led to more traffic, more sellers, selection, convenience. And the flywheel was reinforced. And so that, I think, is one of the earliest and most quintessential examples of a business putting a flywheel in place and how they think about and talk about their strategy as a business. So I wanted to share the earliest example of Loom and our Flywheel and how we ended up articulating this step by step. And I just want to say it's the earliest days of Loom we're talking about, like 2016, like almost five years ago. And so false starts to some of our flywheels and then output metrics that we had associated with it. So the very first experience that Loom deployed is, and for those that haven't used it before, it is and was a Chrome extension that allowed you to record your screen, your camera bubble, and audio. And by just clicking the Chrome extension, it would start recording. By clicking the Chrome extension again, you would stop recording, and it would pop open a video on a publicly shareable URL. Our original thinking was that we just needed to build an experience where people could sign up, where they could install the extension, and when they could successfully record a video. When we went out with that product In June of 2016, we very quickly realized that there was a missing part of our flywheel and experience, which is viewers. Like, just because somebody successfully recorded a video doesn't necessarily mean that that's going to drive signups for the business. And so we wanted to complete the loop. And I also wanted to take a moment to say that in the earliest days of loom, like, yes, this is actually a quintessential viral loop versus a flywheel. But the flywheel for Loom was that we are creating a new behavior in the workplace. We wanted to build and enable people to communicate via messaging at work. And the way to do that is not just by building a tool and offering, but by also normalizing this behavior at work. So by making an individual successful, you can make teams and organizations successful with video messaging. So even though it's a viral loop, it's also a flywheel, in essence. And so our viral loop was highly correlated with our flywheel. And so by enabling more individuals to sign up and install our extension, record a video and drive viewers, that drove more signups, which normalized this behavior at scale. But there was also something that we felt like was a needed iteration on our core flywheel, too, which is the fact that signup and install should generally be in the same kind of camp as one another. And what we were missing actually down here is the fact that sharing, like sharing content that was created and recorded was the missing element that drove viewers that we felt like, in terms of a key qualitative step of our flywheel needed to be broken out, because that was more meaningful than just being able to sign up, install, record, and drove to viewers. That sharing was a key part of the flywheel for us in the early days. And so now that we qualitatively have defined what it is that we believe to be the core flywheel of loom you start to get into what the output metrics could be sign up and download is. The output metric that we really wanted to drive up was extension installs. So how many people are literally just downloading our software? And from there it's once you actually record complete, how do you end up driving up the number of recordings that are happening? And so sharing would be associated with video first views, which is still our North Star metric to this day. And then there's total video views. And then as long as we drove this flywheel, that we would increase and create this behavior at scale, which unlocks more value for individuals, which compounds and keeps accelerating itself. And so an output metric by definition has a lot of inputs that end up creating this volume of specifically for recording completes. There's a lot of elements that potentially go into driving up the volume of recording completes. So now that we're at this stage and we have Loom's core flywheel and we have the output metrics that we are looking to potentially drive that there's the experimentation on Loom's flywheel. So I wanted to provide some insights around how we thought about the weighting of associated output metrics. What are the input metrics? So what I would call bright spots, where are things working, which is things that you could put on the shelf and then where are things really frictionful in terms of where can you relieve that and how do those end up becoming the biggest accelerants to the business? And so experimentation framework and showing you all how we in the earliest days thought about how to drive the flywheel. So these are numbers on the page in terms of the earliest days of LOOM and where we allocated our handful of engineering resources. The reason why we have these percentages is largely because in the earliest days you want to be as data informed as possible. But I intentionally don't use data driven data informed because there's no such thing as having perfect information when it comes to making decisions. And in the earliest days, you are just trying to create behavior and remove friction as much as possible. And a lot of that is going to be based off of your intuition. And so we actually had said that qualitatively. Arguably the most important thing for us to focus on as a business is how do we end up creating more successful individuals to record more videos on loom? Like, sure, there's elements of making sure that they can install the extension and that Maybe there's some CTAs on the video page that allow for people to install it really easily that we could focus on certain elements of video. First views that allow for us to increase the number of shares. But we ended up putting 0% around, driving up total video views, just because we know that we have limited resources as an early stage company and that recording complete and making sure that somebody could successfully record a video was the most important thing. And so also adding a little bit more details around this is that closing the loop is that a little work could go a long way to drive virality. So 20% there. If you couldn't record, you couldn't loom. So by saying that you couldn't record, maybe you didn't understand the user experience, maybe something wasn't intuitive, maybe it's psychologically similar to Amazon prime membership that there's just certain things psychologically that prevented you from recording a video that we needed to get an understanding of. And then third is potentially what are the bugs or technical infrastructure that is preventing you from actually recording a successful video? Not necessarily understanding or psychological things, but like bugs in the infrastructure that we needed to fix. And so the next part of the journey in terms of saying where do we want to. We now have the output metrics and we intuitively associate what are the most important elements for us to focus on in the early days. Now it's understanding what exactly it is that you end up working on. So one thing that was incredibly helpful to us, and I can't imagine not having done this in the early days, is that you have to be really diligent about listing all of your input metrics. And so you can see here on the right hand side that this is actually one of what could have been five or six different screenshots shots of all the different things that we felt like intuitively could be input metrics. And so there's things that you could potentially measure, there's things that maybe you can't measure today, but could measure if you've spent the time. And then there's things that in a perfect world, if you were able to measure everything that you would be able to measure these sorts of things, but we would just document over the course of a two day workshop, like what are all the things that could potentially measure that would end up going into driving these output metrics. And so we had hundreds of potential variables. And so once you actually go through and do the work of listing the things that could influence the core flywheel, then from there, like one of the pro tips in retrospect that would have been really helpful is we just kind of did a shotgun approach. We were just Listing things and then you post categorize them. But I actually felt like by day two, we got into how do you actually list things and just go step by step around the experience? And so it helps if you do it in order of the flywheel. And you might actually learn that maybe you need to change the flywheel a little bit as you go by order. So do that in order because the first day was a little bit more chaotic than the second day. And then from there, once you actually have all of the input metrics that you feel like could influence, now start filling that in, right? Like what are the actual numbers that we do see? And for those that end up being gaps, we actually spent a week in terms of just instrumenting better our analytics in order to understand the key things that could be input metrics to driving the output metric. And so once you start to measure these various elements to the flywheel, then where are things working and what are the key friction points and where are the drop offs. But there's a ton of work that goes into simply understanding what drives the output metrics. So now kind of going back to where we netted out for Loom in terms of we decided that 70% of our efforts needed to go into hopefully helping people successfully record a video. And after going through the measurement of all the different variables that could go into this, we determined five key inputs that would drive a successful record and complete extension. Click. So does somebody even click on the extension to start a recording? And then from there choosing the settings? Do they want the camera on or off? Do they want to choose a certain microphone? Do they want to record their full desktop? Or do they just want to choose a Chrome tab? Then once they actually have their settings, clicking Start recording is one thing, and then clicking stop recording two, three, five minutes on, there's a big gap of time in between. How do you end up driving people from starting a recording to stopping recording? And then lastly recording success rate of the people who ended up clicking stop recording, what is the percentage of those that end up becoming a successful recording that they could, that a user could share? Which is like there's an intuitive pass off at that point to your next step. And so for loom, and this even like pains me to share back with everybody, but in the earliest days of Loom, June, July of 2016, we very quickly learned that recording success actually had a 15% drop off from people who clicked stop recording to those that ended up having a successful video for them to share. And so we are now down to like 0.1% at this point, like orders of magnitude difference in terms of our recording success. So don't be worried about using Loom, if that's what you're thinking. But you know, in the earliest days we said that this is where we needed. We had three engineers and so there was a couple things that were in front of us that we knew we needed to fix immediately. So two engineers went off and started working on that. And then we actually again trying to go a layer deeper, which was that we had one engineer spend four weeks on just instrumenting more measurements around our video infrastructure. You know, measuring page view analytics and certain events on that front is very different from measuring video infrastructure analytics. So we brought in new tools, more rigor, and so we were able to have a roadmap that drove to allowing all three of our engineers to work on recording success for the next four months. And so we ended up allocating all of our resources to making sure that there was no bugs or no errors on everything that we could control on our end from a technical infrastructure perspective. So over the course of those four months, you know, it's not just engineers that we have on our team, it's actually that we have product manager, which was myself by trade, we had a product designer and we also had somebody in support because we had enough users at that point that we just needed a little bit of help. And so over the course of those four months, you start to learn more and more about what are the sorts of things that could actually end up driving the flywheel. And so you'll see that these percentage of resource allocation end up shifting over time, of course. Right. But I think that it was really interesting for us to be able to have that perspective of saying, okay, now that we have recording complete in our video infrastructure under control a little bit more, can we actually find the next biggest friction point in our experience? And sometimes you don't have engineering resources in order to go and measure and say that these are the things quantitatively that we need to focus on. And qualitative insights are so valuable. Like the talking to customers was actually the main way in which Amazon discovered the shipping costs issue. And so for Loom, we had kind of like an aha moment in the early days too, is that we are all tech leaning individuals. And so we thought that going up to the URL bar of Loom and copying that link and sharing it with somebody else was going to be the thing that like everybody intuitively understood, like you just copy the URL bar, it's like sharing Any sort of article that you want to share with somebody else. And so we quickly understood that people had this dissociation in terms of how quickly videos rendered, that they didn't think that the video was ready to share at that point, and that they also thought that they had to download the video and re upload it to another video distribution platform or file sharing platform like YouTube or Dropbox. And so there was this disconnect for a certain percentage of our audience that they didn't understand that they could actually just copy a link and share it with somebody else. And so some of the other input metrics that we thought about in terms of driving video first views is the number of link shares. So after you copy a link and share it with somebody else, do they end up going to Slack and email and pasting that in? And then the third is that when I'm somebody who receives a link, do I click on it? And we knew that we could only control really what percentage of users when they successfully recorded how many copied a link. And so one of the most successful experiments that we've had at loom of all time, where we drove up video first view rates by 20%, was that we just put a big purple button with like plain English text that was copy this link to share your video. And they just like click that cta, it's now copied to their clipboard and they could now share it with somebody else. And so providing that psychological assurance that the video was actually ready to share, that they didn't need to download it and upload it someplace else was one of our most successful outcomes that we ever had. And that came not through quantitative measurement, but through qualitative conversations. And so you could imagine how this flywheel is something that ends up shifting over time. And so getting towards kind of like wrapping up is that I wanted to provide three pro tips and gotchas. I also Saster normally sends out the slides as a PDF. I hope that we can send out the G slides to you all because I just created a quick little template for you all to drag and drop to your business. And then also revisiting the three key takeaways and getting into the Q and A section so it looks like we're pacing well on time for 20 minutes or so for Q and A. So really looking forward to that discussion. All right, three pro tips. One is that you need to be patient and persistent with your flywheel. This is not a single meeting thing. Those first three iterations that I cruised through at the beginning and just kind of like click through those slides over the course of two or three minutes. We're talking about two or three months that we had to figure out what our core flywheel was and through a lot of conversations. And so it seems relatively simple. Most things end up becoming obvious in retrospect, but it's anything but in terms of the very complex elements of your business that all could be key drivers to it. And it takes through not just a series of conversations to articulate the first one, but also a series of experimentation and putting that out to market. So be patient and persistent. This is not a single meeting thing. The second thing that I would say for a pro tip is get the whole company involved. One of the most meaningful iterations that we've had on our flywheel up until this point is when we had our first customer success hire and we were again like a very product oriented and product leaning team. Of course we talked with our customers, but somebody who was purely dedicated to this was our first customer success hire. And so what he was able to help us articulate and where we ended up going down this quantitative path was which Personas seemed to be the ones that were most enthusiastic about loom, and then also the ones that had the highest propensity to share with others. And so the customer success team was able to relay back the fact that a Persona ended up being the most meaningful accelerant to our business. Because when we got more individuals of this Persona type in order to use loom, it spread and the flywheel went faster in terms of creating this behavior at scale within organization. So get every department involved because this is something that should be owned by the entire company. And when you have more people involved that there's more of a sense of accountability and responsibility to help us articulate and accelerate it. And then third is creating a cadence around it. I had mentioned for input metrics, we had a multi day workshop that helped us kind of like write down all the things that could accelerate it. So I do encourage you all to potentially workshop this with your company where you spend a couple days just kind of writing down the various permutations of your flywheel and all the output and input metrics that could go into it, but then from there actually create a cadence around it. So maybe towards the beginning, while you're still codifying it and making it actually your own and still learning really quickly, maybe it's a weekly or every other week sort of conversation, and then from there review it on a monthly basis, hopefully you're learning about new things all the Time, key friction points and key accelerants of the business. So create a cadence around it. Three gotchas. I would just, you know, I'd hinted at it with having customer success involved and talking about different Personas, but don't forget about non technical elements. So even though Looms was largely a viral loop that ended up unlocking a macro network effect and Flywheel is that, you know, NPS could be a really meaningful part of the flywheel. And don't be afraid to articulate that because NPS is something that you could say, you know, what percentage of promoters do we have? And then if you're a detractor, what are the things that are deterrence to you being a promoter of your product and your offering? And so, you know, just don't be afraid to have non technical steps in your flywheel. And you would see that in terms of the next. In a few slides I'll have follow up materials for you to read that you'll get examples of non technical elements as part of the flywheel. So also don't be afraid to say that we don't have one yet. Right? Like this is, this is related to the third one about not prematurely optimizing where it's okay to be like, hey, we don't have a clear understanding of the steps that are driving the business. And so I think that we actually need to do a strategic build out where like if we built this specific element of the Flywheel that it does feel like it connects the whole. And so part of this Flywheel exercise that you're going through is just don't be afraid to say that we don't have one yet. But in the ideal world, if we did have one, this is what we would build and this is how it would all connect together. Then the third one is don't prematurely optimize. I think that this is one where you may say, oh, we do feel like we have one. But imagine if Loom's viral loop actually didn't connect to the bigger picture of normalizing behavior and that we actually it was just a viral loop and not a flywheel is that improving the video first view rate by 20% actually doesn't unlock the biggest macro picture for Loom. And so I would just say keep in mind the big picture and don't prematurely optimize on an existing file or flywheel, understand? One that could drive the business more meaningfully. So this is the template that I was talking about. If we end up sending out the G sides that you could just fill in your key actions and output metrics. I didn't provide a template for input metrics, but I do just suggest that you have a G sheet that you list everything as much as possible. And if it starts to get into the hundreds, that means that you're doing a good job. It's not excessive like you want to be comprehensive because then you can understand the key input metrics more holistically. So hopefully this template is helpful to you. But also now that you see it visually, you could also create it in G slides for yourself in like five minutes. And then the follow up reading, I highly suggest if you haven't read good to great, go ahead and read that. It is a classic. But then also there's this monograph of specifically turning the flywheel. So basically taking a chapter of the book and making it more comprehensive with more examples. It's about 50 page PDF, but then also it's kind of 25 pages of actual content and 25 pages of examples. And so it's really good to get the jog or the brain jogging about like what the core elements of a flywheel could be and then really good ways about not just articulating your own, but how to put it into practice. So this is just hopefully a taste test of what ours was at loom and also how to potentially articulate your own. But Jim Collins is the absolute master here, so I suggest that you, you go to him and learn from what he has to say about flywheels. And so just to recap really quickly, three key takeaways. What is a flywheel, why it's important and how to articulate your own. The framework of how to accelerate your flywheel and understanding how to rally your team around flywheel for organizational alignment, clarity and impact. So I see that we already have five questions that have been passed to me through the chat, which is fantastic. I'm going to stop sharing my screen and I think that 18 minutes of Q&A is did okay.

Yes. Great job, Jo. Thank you so much. That was really interesting. I was mentioning earlier to you obviously, but love that book. So it's really great to see how you guys have used it, optimized it and important just how to, you know, know that you have to keep at it. So encourage everyone to turn on their videos if they'd like to ask a question live. I'm dropping a bunch of questions into the chat now, so feel free to answer one at a time, whichever you'd like.

Okay, so the first one being what gives you conviction on being so laser focused on one thing, in your case, video messaging. I think that for loom specifically, what's interesting for video messaging and how that ends up being not just a viral loop, but a flywheel, is that if you can unlock a medium or mode of communication at work, that that ends up being a huge value unlock at scale. And it also has a ton of work that needs to be done in order to create this behavior. And so it's not just the fact that there is enough value for us to work against, but it's also a complex enough undertaking that we had to allocate all of our resources to it as much as possible. So there's a little bit of a forcing function on being laser focused on. One thing is that we very simply didn't have the option to focus on multiple things that video messaging took all of our resources. All right, so love the flywheel image. Where in the loop does loom focus? Are you focused on signups, shares and views? So that is where like, you know, I think I covered it for the earliest days of Loom in terms of how that shifted over the course of time, where we were really focused on recording completes and then we shifted that to video first views. We've actually meaningfully changed our flywheel over the years. So in October of this past year, we introduced a business and enterprise tier and also a team based offering to loom. And so when you think about the key output metrics that we would want to drive that end up driving the overall flywheel of loom, like we've meaningfully shifted that over time. But in the early days it was about how do you understand where the biggest friction points are that are deterring people from potentially utilizing and driving your flywheel? And I think that when you measure quantitatively and qualitatively as much as possible, that ends up honing in on where you need to focus as a business. So I would just try and be as data informed as possible. And that is what we try to do both in the earliest days of Bloom and also to this day as being data informed.

I'm just curious, with the data, like especially with the flywheel, did you make one change at a time and how long did you give in between each change, if that makes sense in terms of like optimizing and looking at those metrics?

Yeah, that is one that, you know, highly variable in nature. So if I just kind of like double down on the examples that I provided around recording success rate and having all three of our engineers focused entirely on how to scale video messaging and make it so that way there wasn't any things that were in our control that were failing. That was really painful actually for an early stage startup to have 100% of our resources, engineering resources on that. But the experiment that we ran in terms of being data informed and talking to our customers and having more confidence that this could potentially be the biggest driver of increasing video first views, of just adding a share cta like that took us half a day to build and deploy and then the measurement was over the course of a couple weeks to make sure it was statistically significant. And so there's just like there's also forcing functions in terms of the time horizon in which how long do things take to build and how can you measure their impact over time? And I think that that's part of what you need to figure out for your business too is level of effort. Right. So like, I don't know if you prioritization framework that like Rice, in terms of reach, impact, confidence divided by effort. I think that for us, even though we knew it would take a quarter or more to fix recording success rate, the flywheel would be totally broken if we didn't fix that. So we had to prioritize that even though we had high confidence, low effort, things like putting a share link on the video page.

Awesome. Next question for you. In the early stages of the flywheel iterations, did you set success targets or did you just hope to do better than the last time?

That is a really good question too because specifically for recording success, we knew that we couldn't. We had to improve the core product experience. Right? Like it was a relatively, you could call it thin. A lot of the complexity was underneath the hood. But you know, if you were somebody recording, one click to record, one click to stop record, and here's a video page that you could share the link. It just felt really thin, even if it was really powerful. And so for us, recording success rate still continues to be a core metric to our company to this day. And so we did have to set milestones that you would say, okay, we would get to this level and once we got to this level we would move on because we did need to iterate on the core product experience. So for recording success rate, we've literally continued to spend X percentage of our resources consistently over time. So it's literally a limitless sort of focus area. And so for us, for the recording success specifically, we said that we wanted to get to 98%. I think that we were a little bit ambitious at that time because there's Just so many things underneath the hood. But we did set that target. And once we hit that four month mark, we were like, we need to get onto some of the other things. Now for the video share page success metric, we had said that it was the most successful experiment that we had had of all time in terms of driving up video first views. And so we actually just said maybe 5% is something that we would be happy with. And it actually wasn't a meaningful deterrent to us implementing this on our video page. It was just a net new ad. And so for us, the success rate on that front was relatively small in terms of us potentially keeping it. But as we've gotten and grown the product experience over time, there's a lot of trade offs that end up happening. You don't want to over bloat your product or you don't want to make things more complex in nature when you already have a robust offering. Setting success targets actually became increasingly important over time. Where we definitely have those for all of our experiments up until this point. And we make sure to try and remove things that are either neutral or even like slightly positive. Are things that like, does it bloat our product experience? Like, we are much more oriented around success targets than we were in the early days.

Great, thank you. Next question. Do you think every product should can have a flywheel? So you're talking about simplicity and focus, right? So in an organization that has, I guess, multiple products, is that at the organization level or is that at the product level?

I think that there is both organizational flywheels. And yes, every organization should have a core flywheel that drives a business. And then underneath that, what we've started to do is allocate teams around the input metrics that drive the output metrics. And so there ends up being loops within the flywheel underneath. And so that's where it all should roll up to. Like your company should have a flywheel. And I believe that every product and company have one. And then from there you end up articulating the certain metrics that each team is associated with and trying to drive. And so I don't know, like, is one part of your flywheel that one of the output metrics is that a product in and of itself is like for loom? Right now our new flywheel is that we have our, we want to expand the number of people who are within a workspace. And so like we have an entire team that it's dedicated to driving up the number of people per workspace. And then from there it's the amount of revenue that we're able to generate from high volume workspaces for a number of users. And so like that revenue generation is our sales and success team. And then the workspace consolidation is an entire product team. And so there's opportunities for creating teams and alignment within products that are all rolled up to the organizational flywheel. Awesome.

You're talking about like bigger organizations. That's great for. Great segue to this next question. What do larger, more enterprise customers want from Loom that smaller ones don't?

I think that for, you know, there's kind of like enterpriseready IO if you all don't know this website, it is like there's a lot of standard table stakeholders features that you need to build in order for information to be able to be controlled by an enterprise and that there's proper security and provisioning associated with it. And so for Loom, and this is standard for most B2B products and particularly communication and collaboration products is like you just need to build the simple like enterprise products that are enterprise features like SSO and SCIM and role based access control. And then there's other things that are really interesting around like what are the sorts of integrations in terms of tools and suites that they end up having and power their business? So what sorts of integrations do you offer in order to help power certain Personas? And so for us, like what do larger, more enterprise customers want from us? Like they want more control over the information flow that happens on Loom and they want the specific integrations that already power their business. I think that that is generally kind of like the answers that most companies like you want to build your free offering that drives a lot of usage on the product. You want the business tier that ends up monetizing against that. And then enterprise tier is more of like enterprise control security offerings. And then the integrations that they need that power their business.

More of that kind of customer.

Yeah, approved.

How do you determine $8 a month for basic business pricing? Is there like comparable or a similar app? Dropbox.

So I don't know how many of y' all know Reforge, but it is what they call like a growth academy. And so for Loom, in the earliest days when we were also articulating our Flywheel, the entire eight person team went through Reforge, which helped us kind of all speak the same language as one another in terms of how do we think about and talk about growth. And so one thing that was really interesting as part of that was they have a monetization section of that growth academy that we went through that was all about how do you price and package something that is free in nature? And so the funny story here is that you could actually look at the communication and collaboration space and be like, they're all within a $1 price point of each other. For the business tier. There's not a ton of innovation to be had in terms of price point that was offered. But we went through what was called a Van Westendorf price point analysis based off of the features that were really valuable to individuals. They ask four questions. What price point is so cheap that you would question paying for it? What price point do you feel like is really fair and you would definitely buy it at this price point? What price point is on the upper bounds and maybe makes you feel a little bit uncomfortable, but you're still considering purchasing. And then what's a price point that you consider to be crazy and you would never consider it? And so when you ask those four questions about here's the feature set that we would potentially provide in our business tier, you're able to cross reference those four different lines and you get a certain intersection point. And after going through a two month sort of like survey and analysis, we landed on $8 per user per month, which we could have just said, okay, Dropbox charges that or Slack charges that and gone with it. So I would just say don't try and necessarily innovate on price point. But that is also largely determined by what is the volume of user that you're trying to go after. If you are more of an enterprise oriented offering and you are selling to a lower volume of customers, like understanding what price point is relevant to you is incredibly important. Like segment when we first started using them back in the day was much, much cheaper and they were leaving a ton of monetization on the table. And then all of a sudden the next time we re upped our contract, we were like, whoa, okay, now we're a little bit closer to the value that you're delivering and the price that we have to pay. And so just want to say smaller volume of customers usually means that there's more flexibility in the price that you end up charging for loom. We're trying to make everybody comfortable, confident and successful with using video messaging at work. And so we have to price for that higher volume.

Awesome, thank you. Jason has a great blog post around customer pricing on the Sasser blog that is pretty recent the last couple weeks to check out too. That's interesting when you talk about scaling when did you hire each of your VPs? Was it too early in your opinion, or too late?

Oh man, this is. I know. Actually Jason and Saster has had a 2 ton of amazing blog posts on this front. And every time I go to hire a certain VP in a department for the first time, there's usually a Saster blog post that I read and is part of my influence. The other thing that I would say is that from like a timing perspective, I would say generally we've never been like dead on in terms of the timeline in which we hired one. It's either too early or too late. And so there's usually some sort of tipping point where you are doing everything that you possibly can do. And this was one that like hiring a salesperson for the first time, I think that this was in Jason's blog post, is that you do it yourself until you literally can't anymore. Right? Like that there's enough pipeline for somebody to come in and that you need more organization and rigor to how it is that you're having customer conversations. It's not intuition, it's not founder led sales, although it should start. And so there's like certain tipping points for certain VPs. But I would say that we were never dead on for when we were hired. We hired a VP of product probably a little too late because I was a product manager by trade and I just really loved it. And we also maybe hired a head of growth a little bit too early where we just needed a little bit more strategic perspective. And we weren't able to allocate R and D resources to building out this experimentation machine. And so we ended up hiring a growth leader a little bit too early when we needed all of our resources on strategic product build. And so I think that, you know, with this big of a question of like when did we hire each of our VPs too little or too late? I'd say that, you know, this is one that I could spend 30 minutes probably doing a separate presentation on. And so hopefully that kind of surface level answer is a little bit helpful.

Yeah, next session.

Yeah, exactly.

All right, we've got time for two more questions, if that works for you. This one I'm just bringing over now into the platform. Did any KPIs turn out to be a red herring for success as your team developed a new flywheel?

Yeah, I mean I would say that there's. KPIs are one thing. I would also say as we developed a new flywheel so a net, new strategic build is that it's usually not metrics that drive that like key performance indicators are not also exclusively metrics. But I think that when you do go to build a net new flywheel or add part of the output is like there's just so much that goes into that decision that it's not necessarily any one metric. It's something that's like more of an amalgamation of different strategic inputs that allow you to get there. Now of course there are some that are weighted differently than others. And so you could say that one is more of a leading indicator of where we would potentially iterate going forward. But I don't know if there's ever necessarily a red herring that said, oh, this is something that we definitely need to pay attention to immediately. It's these sorts of big strategic decision at the flywheel level should be made thoughtfully and intentionally and not something that you're constantly changing over the course of time. Which is why a lot of that qualitative and intuition that I actually it was interesting that I thought some of the quantitative oriented people would kind of like push back on a little bit, which is like, why did you put your percentage intuition around like where you allocate your resources before you went into the input section? And that's where just from like an organizational focus and intuition is that yes, of course there's data that informs the decision, but there's a lot in like your gut, especially in the early days of where you focus. And so I don't know if red herring ends up becoming like the singular data point that tips you into working on something. It's an amalgamation.

Awesome. Last question. Sounds like you have another meeting to get to in 10 minutes.

10 minutes.

When you made changes to the flywheel. We've talked about this a little bit. Did you test the change on all users or just with a subset of users?

In the earliest days of loom, we didn't really have our feature flagging infrastructure. It really was just deploying and seeing what changes were made. Now I come from product management background. I know that that's not AB testing, but also it's like where do you allocate your resources? Should we build out the experimentation engine or do you actually just say that we have certain amount of resources that we should be incredibly confident in whatever it is that we're building in the earliest days and just deploy that and then seeing how it impacts that for video first views, like we just decided it wasn't worth putting in a B testing infrastructure and we didn't do that for the first 18 months. Like, we were just working on things that we were very, very confident in. So now, of course, we've changed our feature flagging system. It's much more robust in nature. We have a bunch of experiments going on simultaneously, which has its own complexity. But I think that you crawl before you walk and you walk before you run. And so I wouldn't be really worried about doing the true A B test in the early days. You should have such limited resources that you're just only building and working on things that you're super confident in.

Awesome. Jo, thank you so much. This has been amazing. We didn't get to your question. We can send them over. Maybe you guys can do a blog post about it or something. But Joe's slides are available to download if you're in the swapcard platform. They should be just below at the bottom of your screen so you can get that flywheel template. The recording will also be available. So again, be sure to follow Saster on YouTube and on Twitter. Joe, thanks so much again. Really appreciate it.

Yeah, thank you very much, Caitlin and Zaster and audience. It was really great to be with you all and thanks for all the questions.

Awesome. We've got a couple more questions or a couple more sessions left, so be sure to check out the platform. And at the top of the hour, we'll have the next.