Software Social

Talking to other entrepreneurs made Michele realize that she hasn't talked about interplay of qualitative *and* qualitative data enough. Michele and Colleen talk about what that means in the context of feature requests and high-touch sales.

Show Notes

Talking to other entrepreneurs made Michele realize that she hasn't talked about interplay of qualitative *and* qualitative data enough. Michele and Colleen talk about what that means in the context of feature requests and high-touch sales.

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Creators & Guests

Host
Colleen Schnettler
Co-Founder of Refine, Founder of Simple File Upload
Host
Michele Hansen
Co-Founder of Geocodio & Author of Deploy Empathy
Editor
Cory Stine
Audio Editor
Writer
Meghan Coleman

What is Software Social?

Two indie SaaS founders—one just getting off the ground, and one with an established profitable business—invite you to join their weekly chats.

Colleen Schnettler 0:00
Hey, Colleen, Good morning.

Michele Hansen 0:02
How's it going?

Colleen Schnettler 0:03
Great. How are you?

Michele Hansen 0:05
I'm doing good. I'm so so last week was kind of a busy week. And I was in the US for two conferences, one of which you were at. And I had some interesting conversations with people. I mean, first of all, just like, I just love that, like, I can see people in person again, you know, yeah, I just like, I'm gonna sidetrack for a second. But like, I was thinking about that between. And you just think that the conferences I've been able to go to this year, like, I have made so many friends this year. And it's

Colleen Schnettler 0:40
awesome. That's amazing. I love that.

Michele Hansen 0:43
I feel like it kind of makes up for how, I don't know, well, terrible for many reasons. 2020 and 2021. We're but like, it's also so fun to finally meet people that you, you know, talk to you online, you know, on Twitter and everything else regularly, and then you actually meet them in person, right? Like,

Colleen Schnettler 1:01
have you experienced this? Oh, for sure. 100%? Yes.

Michele Hansen 1:05
So at one of the conferences last week, there was someone who I talked to online all the time. And I was like, 1,000% sure that I had already met them. And they're like, oh, like, hey, Michelle. And I'm like, Oh, hey, and they're like, it's nice to finally meet you. And I'm like, No, we've met before. And no, we haven't. And I was like, No way. Wait, wait. I have definitely met you before. And know. And so yeah, that just definitely just, you know, filled up My Cup, you know, to like, meet so many people and feel so energized. I finally got to see the Geocoder yo lanyards in person at Longhorn PHP, which was pretty exciting. That is awesome. Yeah, picture. Yeah. So I should actually I should put them on Twitter, or Mastodon since since that's, we're also doing that now. And yeah, that was pretty fun. Because we sponsored last year too. Well, it was actually 2020. Then it was cancelled. So it was last year. Yeah. So that was really fun to get to see them in person. There's there it is kind of surreal to like, look out at a conference ballroom and seeing like, hundreds of people wearing our logo, like, yeah, I was. I was like, starstruck. I don't know if that's like the right way to put it. Right. Like, but it was like, Whoa, it was really cool. Needless to say, we will be sponsoring it next year. Because that was totally worth it. Yeah, okay. So anyway, so I had so many good conversations with people. But one thing I realized, is, I don't think I have done a good enough job talking about the role of quantitative data. Okay, with search, like I yeah, I have actually kind of like, I think I have, I don't want to say I've messed up. But like, I need to do more than that. So I was talking to someone who, you know, they have their own indie software biz. And we were talking about how they could figure out what to build next, which features to build next. And they mentioned, no, they recently add one of those, like, feature op voting tools, right, where like, users can submit features, and they're, you know, they're trying to figure out which ones they should build next, because they want to, you know, figure out how to grow their business. Right? And, yeah, and no, you know, I started started started asking some questions. And I'm like, okay, like, Do you have any sort of, you know, like, Do you have a sense for which users who are, you know, either they're paying you the most, or they have, you know, renew their subscription? Like, they're retaining, like, which features they're voting for? And like, and then we started talking about conversion. I'm like, so people who actually, you know, create an account, like, Do you have a sense for, like, how many of them actually end up paying you? And then like, any sort of behaviors that correlate with that, like, Are there any features that correlate with that, and kind of diving into the data a little bit, and saying that, then you use that to kind of figure out, okay, these features that people are telling you that you need, then you can kind of get a sense for which ones are the most important if, you know, there's a feature that people who have been renewing their subscriptions for, even if it's just once, right, if it hasn't been around for a long time, that's valuable to know, versus if there's a feature that people are asking for that they've never paid you? Right, right. So like, as you can see, at least who was is already a good fit for, and you know, chances are, you're closer to being an even better fit for someone than you are from going from not a fit to a fit, if that makes sense. Like that's a bigger jump. Yeah. Right. And I was like, oh, man, I would love to just like, you know, kind of like play around with your data for a day and like, there's probably a lot of really interesting stuff we could find. And then you could use that and you'll figure out which segments you should talk to and then, you know, kind of feed all of this together. And I realized through the course To the conversation, both through what they said. And as I was talking that like, I have not explained that very well, like how you use both qualitative and quantitative data together and like feeding into each other, and using one to inform the other and you kind of like ping pong them off of each other. And as I was talking to this person and a couple of other people I saw later on, I asked him, I was like, so you know, I've said, like, use qualitative and quantitative data together. Like, I'm curious, what did that mean to you when I said that? Because there's people who've read my book, and they're like, Oh, I thought it meant, you know, you should look at Linux. And then you talk to people. But I didn't really know how you connect them to each other. And so this was really interesting. And it was also kind of interesting seeing how, I guess surprised people were by seeing how excited I get about playing around in spreadsheets, which I think for good reason, people don't see me as a numbers data person, which is fair, because I don't really talk about it. But the reason why I don't talk about it is because a billion other people talk about it. And there's tons of books on data analysis. So I didn't feel like I needed to, you know, I didn't feel like there was a hole there. Right. But I think the problem is, is when if people are just coming across my book, and they're using that as their primary piece, like I actually like I need to do, I need to do more, I think, to really like give like concrete examples of the interplay of quantitative data. With qualitative data. Does that make sense? Like, this is the thing is like, I don't even know if I'm explaining this in a way that makes sense, because I actually, I really haven't tried to articulate it before, because I just thought it was so well covered elsewhere that I didn't even like need to bother. You know, I have one chapter in my book that says use both. But that was basically it.

Colleen Schnettler 7:04
Yeah, that makes sense. I'm curious, like,

Michele Hansen 7:06
for you, as you are now. I mean, you you are like, pedal to the metal right now. I mean, of course, you I mean, you guys have a feature list of miles long. And I think to a certain extent, this is almost been your problem that you have been waiting to sort of like build all of the things and then launch. I'm curious that as I say this, like, what does that mean to you? Like how, like, how do you look at your list of stuff to build next and try to figure out what is impactful for the business? Well, I

Colleen Schnettler 7:43
feel like we're so early. So compared to where you are, or it sounds like this person you were analyzing data with? Is we're still we don't really have any quantitative data

Michele Hansen 7:56
this year. Yeah, you've got like, it's early. Yeah. Did rails? Yeah, I'm on rails right now.

Colleen Schnettler 8:03
Five on rails 10, on Laravel. Okay, ish, somewhere between 15 and 20. So I don't think that we have any quantitative data, I think when we look at features and think of like, how to prioritize that, we're still just kind of going off gut feelings. But, and I this is not what you're talking about. But I like this approach to high touch funnels, because I'm trying to learn more about like high touch high value funnels. And one of the things I'm not doing, which I'm going to start doing is literally write down how many calls I have a week. And I don't think that's what you mean, at all, I think you're talking about analyzing data. But even just like, if I have five calls a week, how many of those people got on a second call? How many people have those people convert? numbers in any sort will help us right now?

Michele Hansen 8:51
I think it's also worth keeping track of, you know, to the extent that you can dissect things about those calls, right? Like that, that are that can be used for that interplay of data. So for example, like, what industry they're in, like, are they? You know, they might be software, but are they serving a specific vertical? How big is the company? How many, how many developers do they have? Who is the person you're talking to? Because, and you know, here's a really simple example of where that interplay might happen. Like, if you if you actually have all this just like keep a spreadsheet of as much sort of little data points you can get on each person you talk to, and then you look back at and you say, okay, so of 10 I don't know engineering managers that I talked to, or you like, head of engineering that I talked to you only, like, let's say two of them ended up converting both okay, but I talked to this other function, and actually five of them ended up converting Okay, that's really interesting. Like, can I go back to like your notes from those calls, for example, and see what are similarities in the use cases or what's going on in the organization and then dig into that and see if you can use that for your copy and then Have those other calls that you do might become more successful, or you can start specifically, like targeting that kind of a use case, or that kind of an organizational problem, or whatever it is they might be facing. Right. But yeah, I think it's a good idea to start just just kind of just keeping notes in a spreadsheet, on, on whatever you can pull out.

Colleen Schnettler 10:21
Yeah, totally agree. But what exactly when you were talking quantitative, I thought you were talking about like, analyzing 1000s of customers, something you've only talked about very, very briefly, is your bi annual customer analysis spreadsheet that you make? Is that what you were referring to?

Michele Hansen 10:38
Um, so that's just one type of analysis that I do. And the first one I talked about a little, I got really in depth again, I'm kind of kicking my I think

Colleen Schnettler 10:47
twice in two years, we've talked about it, Michelle, no one really knows what you're just kind of hand wavy pivot tables, no one really? No, sorry.

Michele Hansen 10:57
Yeah, I mean, so there's that one where basically, you know, I download all of our revenue data for that year. And then I analyze it effectively as if it were a stock portfolio. Does that make sense? When I say that? Yes. Because, right. So I'm like, grouping it. So not only is it you know, everything is by customer and by the amount of revenue, but it's also looking at industries, for example. So like a very high level of saying, Okay, if we looked at this, like a portfolio, right, so I think one example I talked about in the book is when I did this in 2019, and I saw that 20% of our revenue was coming from real estate, for example. And so for me, for just my own risk tolerance. I was like, you know, if this for for me, if this was a portfolio of stocks, 20% in one industry, nevermind, real estate, which, you know, as a child of the financial crisis feels a little bit risky for me, as far as like, we're going to intentionally try to go after other segments, we still serve real estate, we still have tons of real estate customers, it's still a great industry for artists for us to serve. But 20% just felt a little bit high. And so then it was okay, but what other segments are we in that are pretty low percentage of our new percentage of customers compared to say, two years ago, or three years ago, but are very sticky? Right? They have really low, cancel or churn rates? And can I talk to them and figure out, what are we doing that makes us such a good fit for them, so that I can create marketing that speaks more to those use cases and pull in more of those customers? And so ratio wise, we end up ratio wise, decreasing the percentage, for example, from real estate.

Colleen Schnettler 12:33
Yeah. I have like a real practical question. Yeah. You said, I download all my data, I assume you meant from stripe or whatever your payment provider is correct? How do you know what industry these people are? In? Are you like googling their companies?

Michele Hansen 12:47
Really, you are like looking at every single website? Which I mean, it's kind of fun sometimes. Because you're like, takes like, five minutes to be like, what do you what do you actually do? A lot of them, it's like, pretty clear. It's like, okay, this is finance. You know, this one is, like, mortgages. This one is, you know, this one. So real estate like that's like you like usually it's, it's pretty straightforward. It's some of them, it's a little bit of a challenge. And it's also like, you know, trying not to classify something as software when they're very clearly serving a specific industry, which is, you know, if you're looking at like a portfolio risk, composition perspective, it's not the fact like of what it is, it's who they serve, that's important. So not the fact that that's good software, but keep in mind that they're safe. Okay, if they're serving one particular industry very strong than if, for example, the logistics industry was impacted, even if their software, they're going to be impacted by that, right.

Colleen Schnettler 13:45
So I'm trying to think of how I can apply this to my much significantly smaller customer base. And what I'm hearing and this is something that I haven't done yet, maybe take the 30 people I've had a call with and classify them based on their role at the company, the size of the company, and this, like you said, it's just a spreadsheet, right? So let's take these people, their role of the company, the size of the company, and the industry they are serving to see if I can see any patterns emerging.

Michele Hansen 14:12
Yeah, and I mean, maybe if you can put in like if you know anything about their revenue, or anything that I mean, that that has emerged to you so far as important. So you know, we had early on talked about, it's like talking to the like, developers, for example, they don't see the value of their own time, so that it's really important for you to talk to non developers or leadership level people. So that might be important. But also, did they end up becoming a customer? And how much like, even just say, like, but an estimate of how much time you spend supporting them, right, even if it's just low, medium high? Yeah. And then I imagine you have notes from all of these calls. I do. Yeah. And then so then if you can If you know, 30 is a really small sample size, and it's not really like, not really enough, right, but I think there were probably some be some things in there that are directionally interesting.

Colleen Schnettler 15:12
Yeah. Cool. So continue to tell us more about what you do with your quantitative analysis.

Michele Hansen 15:19
Yeah, I mean, so like, the portfolio analysis is just one example of something. So for example, like, it's also using, you know, competitor data and like broader market data as well. I think this is something that I maybe have not also not done a good job of communicating is. Listen to your customers, but be selective about what you take action on. Yeah, right. Like what like, when you're in the act of listening to someone, just just listen to them, don't start filtering, don't start thinking about how you could build it or whatever. And I don't think everybody gets that at this point. But it also doesn't mean that because you have heard these 10 different problems from them, that you are then obligated to build them. Or that it makes it makes sense for you to build even any of those. So for example, something that I always do is somebody, you know, suggests a feature to us, in addition to getting some information about what their current processes, you know, I mean, if we're speaking numerically, like the amount of time and the amount of money they're currently spending to solve, that is a hugely important number to get from that. So they might tell you that they want something specific. And if they're spending, you know, like, hours every week, like, I'll have people tell me, like, you know, this, this used to take me and a team of five people a week to do and now it's done in 10 minutes. Like, that's really important. And also how frequently they're doing that, right? Like, if this is the first time they've ever run this data, and they don't really do this they does every five years, then that's not very interesting to me. But if they're doing this every day, every week, every month, that's interesting. And also, yeah, how much they're spending on it. And sometimes they'll tell you this directly. And sometimes it's more well, I use you guys, but then I also have to use this vendor over here. And then, you know, takes tons of back and forth to get it going with them and their software, super janky. And it's also like, how long does that take, like, just from finish to start to, to just get the data from that other vendor? Right? And then like, oh, six hours total? Great, that's helpful for me. But then also looking at, Okay, who else is in the industry? Like, how many other companies are competing with? How big are those companies? What are they charging for it? That kind of stuff, which is sort of which is qualitative data? But it's basically looking at, like, the market situation of something, and then trying to figure out from there, okay, is there an opportunity for us, you know, thinking back to what our competitive advantage are, and our niche in the market for us, you know, given the user needs, and given the competition, and basically, given the, you know, in addition to everything else you might be thinking about about what you can charge for it, and you know, how complicated is for you to build and support, for example.

Colleen Schnettler 18:10
So this person that you were advising, looking at data, were you able to, did you have enough data, data that you were able to extract some kind of actionable feature list? Did that work?

Michele Hansen 18:24
Not yet. Because they need more data, like the atom, for example, they need the data on people who are converting, they didn't have that data right away. So they had the people who were basically creating an account. And then the next level of data needed was, I think they had actually the people who were creating an account, and then the ones who ended up purchasing it. Okay, but even getting some sort of data on like, Can we at least see if they're like, Are there correlations between? What people who create an account and what people end up purchasing it? Or like upgrading? Like, what kinds of actions are they taking in the app? How often? Are they using it before they purchase it, right? Because that can tell us? Is this an onboarding problem? Is this like, is there a marketing problem where you're bringing in a lot of people who are falling through? Because it's actually not a fit for them based on like, like, is there a messaging misalignment, right? And so the answer there was was, actually don't go talk to people, you need to collect some data first, and get a very high level of just what's going on. And then once you get that data, then you can dive into a like, find those those good segments, the stickier segments, and then go talk to those people and figure out what are they doing which features are important to them? You know, take a look at your market. Take a look at your competition. Consider how complicated it would be for you to add and support and then consider adding those features. But I think this is this is kind of a danger of, you know, using things like a feature list without taking into consideration. Other information about those customers. And I've heard that some of these some of these tools do that they actually allow you to pull in stripe or other data. So you can see, okay, the the customers on these particular plans are voting for these features. I don't I don't think all of those feature list. Vote apps do that though.

Colleen Schnettler 20:28
Do you add geocode? Do do you track this kind of data from sign up to free API key to paid?

Michele Hansen 20:36
Yeah. Okay. Do Yeah. Like I've done analysis for like, you know, what is the like, if someone is going to add a card, for example, what is the average time from card added to? Or sorry? It's from account creation to card added? Like? Yeah.

Colleen Schnettler 20:50
Do you use a third party software? Or did you guys just write that yourself?

Michele Hansen 20:54
That's all Excel. There's probably software that does

Colleen Schnettler 20:57
it. But had you do it in Excel? Yeah. How do you know? Yeah, but you have I mean,

Michele Hansen 21:03
we were collecting all of the data. Right? Okay. And it's just all in the database. And I don't know SQL. I did write some SQL today, weirdly enough. This is one thing that came out of my conferences last week is like, oh, like years ago, I really wanted to learn SQL, because I actually really like playing around in this data. But I don't know how to use SQL, which would be even more effective. But yeah, so I was doing SQL and MetaBase. And, you know, I mean, what we've talked about, of why I bought refine is because sometimes I'm kind of afraid to go into MetaBase and write a really janky SQL query and make something blow up by accident. And versus with refine, I can get that really important piece of data out of Nova and not worry about blowing it up. Thanks to you. And Erin,

Colleen Schnettler 21:46
your database is alive since 2020. Whatever, yes,

Michele Hansen 21:51
it is a use case it would be keep your Product Manager from blowing up your database. I mean, I don't I couldn't totally blow it up. You know, it's not. But you could write a very expensive query, basically, because

Colleen Schnettler 22:08
there are a lot of products out there that do this for you. Theoretically, if you hook them up correctly. I do not use any of them. But so I was curious if you guys used any of them. But you just track all that data, right? So you have user A and in the database every time. Wow. So you track when user re signs up? You track when user? Sure that makes sense user, we have audit logs to like the user. Yeah, we have? Yes, you've all the data. So you just explored it and you just do it in Excel.

Michele Hansen 22:36
Yep. Cool. Yeah.

Colleen Schnettler 22:39
I mean, she would be I know, you could never do this. But it would be really interesting. And I think to see you do that. I would like to I mean, I know again, you can't but if you ever have fake data, I don't know if you guys use fake, like a staging server. It would be I would be interested just to see. So a once upon a time I worked at, I worked in a BMW, like factory, like an actual, like, we're building the circuit boards that go in BMWs. Whoa, yeah, cool. Surprise,

Michele Hansen 23:09
a lot of cool jobs. And a lot of cool job you like dropped a couple weeks ago that used to work for NASA. And I was like, Record scratch like.

Colleen Schnettler 23:18
So yeah, so it was a cool job. They ended up moving the factory to Mexico. So the job went away. Yeah, womp womp. But where I was going with this is I didn't know a lot about manufacturing. Or actually, when I got this job. I didn't know anything about manufacturing. So my days were spent deep in Excel and deep and pivot tables and data because of course, you're trying to follow the principles of Lean Manufacturing. So tracking all of this stuff is so important. And I got into it. So I appreciate like, how awesome and how fascinating and powerful a good pivot table can be. But I haven't I mean, that was 10 810 1011 12 years ago, it was long time ago. So I'm kind of far removed from it now. But I would like love to see you do it. Because I'm just really curious as to like what your process is, and what you actually find interesting, because I think the problem one has, and not just me or you but most people, there's a lot of data. So how do you take that data in a meaningful fashion and pull information out of it that is useful to your business?

Michele Hansen 24:23
Yeah, yeah, for sure. You know, it's funny, you say wanting to see me live data. And I, yeah, peanut butter data time. And actually was somebody else I was talking to you last week, I actually ended up zooming with them the other day, because we were talking about their business. And basically the conclusion was like, your free tier is probably too generous. Like, these users are probably costing you a lot of money and like most of them would probably upgrade instead if you had a free trial, for example. Yeah. And so it was like, you know, let's let like and I was the other day I was like, you know, I have I have like half an hour. Like let's just do this really quick. got their payments data, just and then also data on some usage data, some very high level usage data. And I was like, Yeah, this will be like half an hour like, and I'm like just going through in my head and like, Okay, I just need like couple pivot tables, and then we just do vlookup to that, and then we're good. Right? Okay, an hour and a half later, like, we were still like, what like, like, we finished cleaning the data. We had, like, 10 different pivot tables going on, we still didn't actually have the ones we wanted, came up with more questions in the meantime, like, did connect in the usage data, but still was like, wait a minute, okay. Now, we figured out the usage for the people who are paying you, but what about the people who aren't paying you? And it was like, I have so many more questions. And I was like, and what we were doing it live, so they got to see me, you know, like, forgetting parentheses, and like, actually, like having to like, look up why my VLOOKUP wasn't working. And like I was like, oh, yeah, of course. And so it was like, kind of, like, I got a little bit of the live coding anxiety that people get, you know, massive respect for Aaron and all of his live coding talks. But it like I was like, Yeah, with so when I do this myself, I'm usually locked in for like six hours straight. And then I still have more to do. So what I'm trying to say is, if you wanted to watch me live data analyzed, like, this is not like a 20 minute thing. This is a like, performance art exhibits. Buckle in for eight hours.

Colleen Schnettler 26:27
Time Square. until you're done.

Michele Hansen 26:31
Honestly, I am surprised that that has not been a performance art exhibit where we just you know, you put a business analyst in a box and watch them type, which is I mean, got it that does not feel like working in in an open plan office, frankly. Yeah, but I started talking to these people. And I was like, You know what I should do? Like, I haven't been doing a whole lot of newsletters lately. But I think I should kind of do like, not like case studies, but like, kind of actually sort of like work through this with them. And then write about the process as we go and probably like anonymize it, right.

Colleen Schnettler 27:11
It would be interesting, I think, yeah, basically say, okay,

Michele Hansen 27:13
like, here's this thing. And then here's, here's kind of, here's the, here's the process we went through,

Colleen Schnettler 27:19
right? Because you kind of talk in the abstract about this, but you have never talked in detail. I think this is a problem with kind of advice we give and get as bootstrap founders is, there's a lot of general advice. But like, literally, how do you do it? I like specific, actionable advice. And so I think if we haven't done this in years, or we're not quite sure where to start, it'd be really interesting for you to be like, if that person was open to it, or had some fake data or whatever, like this person came to me with this problem. They don't know what features to build, or they don't know how much to charge. So they have 1000s of customers, or whatever it is, this is what we did to analyze their data to actually get quantitative analysis, not just, this is what I think I should do based on the three people I talked to analysis.

Michele Hansen 28:13
Yeah, I mean, I think for me, you know, I think there's a I guess one of the reasons why I have not probably jumped headfirst into talking about this more is because I think there's a very reasonable criticism of advice from founders, which is you're hearing about what worked in their experience, but that doesn't necessarily mean that they are an expert in that fear field, nevermind have validated that experience elsewhere. Right. You know, there's like, there's a selection bias there that you're hearing about what worked in this one person's experience. And that might not be interoperable, so to speak. And so I think that's for me, why, like, the areas where I have, I guess, I let myself give advice, and I'm very particular about what those ones are, because I make sure that it's only the ones that I actually have some sort of background and or have studied, like, or have, you know, extensively learned from other people and applied in different scenarios beyond my own experience. Like, yeah, it

Unknown Speaker 29:24
makes sense. Like, I

Michele Hansen 29:24
try not to, to do that too much. But I think at least giving, like giving examples of just a workflow is, you know, even caveat ID is probably better than nothing, right? That would be my I mean, maybe that's better than like, sort of generic hand wavy kind of stuff. So I don't have time for it right now. Yeah. But I've kind of I'm already thinking ahead to January when my schedule frees up a bit about you know, doing more writing There's even a new writing project I want to start that I haven't even told you about. So I think I'm gonna, I'm gonna kind of like slot that in mentally for January to at least start working with them and see if we can get some sort of like, I don't know, just like example, I guess of a project of using qualitative and quantitative data.

Colleen Schnettler 30:18
Yeah. Awesome. I love that idea. Cool.

Michele Hansen 30:22
Well, this feels like a good time to say thanks to everyone who supports the show. I want to give a special thanks to our new supporters. Brendon from feeder loop and Pascal from sharpen dot page. Thank you. You can become a supporter too for $100 a year at software social dot dev slash supporters. Chris from tipper CI the daringly handsome Kevin Griffin and Mike from Lazarus. Dave from rocket max of online or not Stefan from talk to Stefan Brendan Andre of bright beds team tuple Alex Hillman from the tiny NBA Remi from hover code and rocket gems. Jane and Benedict from user list. Kendall Morgan Ruben Gomez of sign well Cory Haynes of swipe Well, Mike Wade of crowd sentry Nate Ritter of room steals and a mass of subscribe sense. Jeff Roberts from outside a Justin Jackson mega maker, Jack Ellis and Paul Jarvis from Fathom analytics Matthew from appointment reminder. Andrew Culver at bullet train John Koster. Alex Of course. Oh systems Richard from sunning. Josh the annoyingly pragmatic founder, Ben from consent kit. John from credo and editor ninja cams flown Michael Kapur of new see proposals. Chris from URL box Kayleigh of testlet Greg Park from trade lab. Adam from Rails auto scale. Lena and Alex from recap. See Joe Mazza lots of rail service.com proud mama from Apple net, LLC. Anna from cradle Moncef from Ruby on Mac. Steve of being inclusive Simon Bennett of SNAP shooter backups. Josh Smith of key hero.io Yes for Christiansen, a form back end. Matthew of Work Cited Chris of jet boost.io Darrell Shannon of dogmatic Laravel is the community for women, non binary and trans Laravel developers Arvid call and by the way, I was on arbites podcast recently. So if you want more of me and your podcast feeds, go check out the bootstrapped founder. James sours from castaway.fm Jessica Melnik Damian, more of audio audit podcast checker Eldon from nodal studios and Mitchell Davis from recruit kit. Thank you so much everyone, and I will talk to you soon Colleen.

Colleen Schnettler 32:47
All right. Bye

Transcribed by https://otter.ai