Discover Howdy.com's 30M ARR Retention Secrets

Full video transcript

What if scaling your company didn’t mean hiring faster, but hiring smarter with AI? Today, we’re pulling back the curtain on how Howdy.com cracked the code to $30 million in annual recurring revenue with a stunning 98% retention rate.

It was 2019. I happened to be in Colorado Springs and there was a US Treasury there and they had a wall of $30 million in cash on the wall, and I stood in front of it and I was like, “Take a picture. If our company hits 30 million, I’ll post this picture.” And so that had happened on Monday, and I posted it on Monday.

All right, y’all. Howdy.com, one of our platinum sponsors of the event to make this event possible. Make sure that you are subscribed to us on YouTube to stay up to date with all the latest from Gen AI University. And in this conversation with Jacqueline and Frank, going to be talking about what Howdy is doing to help enable an AI-powered workforce. Jacqueline, Frank, great to see you guys here.

Thank you, Darby. Great, great to be here. Hi. Hi, everyone. Good to see you.

Good to see you guys. And I’ve got a number of questions I wanted to ask both Frank and Jacqueline about their company, because Howdy is doing a lot. They’re growing fast, and they are up to a lot of really cool things, especially when it comes to finding top talent, but also placing that top talent at companies, and AI is obviously a big part of the conversation. First question I have, Jacqueline, is that Howdy has been growing rapidly. What have been the biggest factors driving your expansion, and how has this growth impacted your business and your clients?

Hi everyone. We’re so happy to be here. Darby, thank you again for having us. And yeah, I’d love to tell you a little bit about it because I was reflecting on this journey recently — Howdy actually just passed the 30 million ARR mark. And I remember this number is very vivid in my memory because when we were just starting out and we had barely any customers and we were barely making any money, it was 2019. I happened to be in Colorado Springs and there was a US Treasury there and they had a wall of $30 million in cash on the wall, and I stood in front of it and I was like, take a picture. If our company hits 30 million, I’ll post this picture. And so that had happened on Monday and I posted it on Monday. So this reflection of this journey is very recent.

And I will say that something that surprises me now, that I wouldn’t have thought of in 2019, is how much we’re still doing today what we did in the very early days. And I think what people who are building companies don’t realize is that so much of the sales — 50% of the sales — are still coming because Frank and I are prospecting. So much of what we’re doing is still the stuff we had to do in the early days. So all of the things that you do in the early days of your company to make yourself successful is going to continue to make you successful. So if it works early on, it’s going to work later on as well.

And so this question about what’s the biggest factor for driving the expansion is: one of the things we did really early on was to make sure that we were a really great service for one company — not to try and be all things to all companies, or to sell many things to many people. What we really wanted to do was just make sure that we nailed the one thing for one company, and it was such an amazing experience that if we were to give them an NPS, of course they’d mark 10 because they were just blown out of the water. And then we needed to make sure that financially we could scale it, and that operationally it could be linear, because if it delights one person, and if it financially makes sense that we can scale it without losing money — in fact, we make money — then linearly we can expand to deliver the same amount. Then as we’re growing, we can create efficiencies. We can make things better. We can make more money. We can make more profit margin. We can squeeze things here and there. We can delight things in other ways.

But what we really wanted to do was just isolate it down to literally n of one and just make sure that we were excellent for one. And then if you can do that, you can duplicate it. So what is the biggest factor that’s now driving our expansion is that we have been hyperfocused on one thing and being great at one thing. And now we just have time on our side, because we’re getting referrals from our customers, we’re getting our name out there, we’re still prospecting, Frank and I are still doing sales calls, we’re still meeting with different folks all the time, but now we have an entire team that’s helping and duplicating these efforts, so it’s not just us doing it. We have a massive team helping us with it. But get it right for one, and I promise you it will keep compounding, especially if you just have enough time behind you.

A great insight, and congratulations on that milestone. That’s huge.

Thank you.

Next question is for Frank. You know how AI tools are making developers more productive, but they’re also creating challenges in oversight and quality control. So how is Howdy balancing AI-driven productivity with human expertise, and maybe a quick background about your role with Howdy gives more context to this question, Frank?

Yeah, absolutely. So hi everybody, co-founder and CTO of Howdy. My background — I’m based here in Austin. My background is in software engineering. I was a software engineer for the first half of my career, then a product manager, then eventually a founder. So I definitely come from a technical background and have been fascinated to watch how teams and engineers are deploying these new tools.

I guess the thing that really strikes me about these new tools, and how developers and software engineering teams need to adapt to them, is that these tools are capable of massively boosting developer productivity. But the trade-off is that it needs to be really balanced with quality and careful oversight, because AI excels at doing things like boilerplate code or repetitive patterns in code, performing large-scale changes that would be pretty tedious for a person to do manually. So that means that a lot of the technical grunt work can be offloaded to these new AI tools.

The problem, though, is that these AI tools have blind spots and they can get things wrong. They sometimes don’t understand the context. Sometimes they’ll cheat. My favorite example is when a developer was telling me a story where they were trying to get the AI to develop test cases automatically, and eventually the test cases magically started to pass. And when they looked more carefully at why the test cases were passing, it wasn’t because they had fixed the bugs in the code — it was because the AI code generator realized that it was better to just change the test cases so that they’d pass more easily than it was to actually fix the bug in the code.

So what this means is that in terms of balancing real software engineering practices, the old-school basics of peer reviews, some human-level testing, some collaboration and reviewing the code carefully — there’s still a lot of that going on. And the way to really do it is to ensure that culturally the teams are strong with all those basic software engineering principles that lead to high quality software, and then strategically point these powerful productivity tools to the problems that are just boring, grunt work for humans, and still at this moment leave the architecture, the taste, the judgment in human control. Who knows how long that’s going to be a requirement — maybe in five years or three years that will also be taken care of by AI. But for now, that’s what we’ve noticed has been absolutely key for the software engineering teams that we help build with Howdy — the most successful ones have really struck that good balance of where to point the big cannon, but the stupid cannon, of AI, and then where to keep the scalpel with a discerning human in the loop.

Yeah, that makes a lot of sense, and it brings me to my next question. How is Howdy actually integrating AI into your own recruitment and management of that workforce, and maybe what are some efficiencies that you’ve seen doing this from the recruiting standpoint and your own workforce management, to find the right talent that’s actually going to be using these tools?

Yeah, the number one place where we’re starting to apply these things in our day-to-day processes is to streamline talent recruitment and assessment and matching. So it’s funny — when we started Howdy, one of the big things we have to do is vet talented people for their soft skills, for their technical skills, for their experience, and then somehow match them to the opportunity where they’re going to succeed long term, because we want to provide great value to our partners in the United States, and we have another customer, which is our teammates and colleagues in Latin America. So both sides of that equation need to really work.

At the beginning we had so many amazing things that we wanted to do in terms of machine learning and data mining to make that match automatically, mathematically figure it out. We were thinking about the OkCupid algorithm that “solved love” — I remember a marketing term for them that like 80 or 90% of their couples didn’t divorce after five or ten years or something like that; they had some amazing retention metric. I don’t think there really was ever enough data to make this work, because you’d need thousands and thousands of data points, if not millions, to make something with the traditional machine learning algorithms actually work.

But what we’ve found in our operations is that if you have a bunch of opportunities and a bunch of CVs, these large language models are really masters at evaluating and considering the match over soft skills and technical skills. I’ll give you an example. If a customer is looking for, let’s say, an e-commerce engineer, what does an e-commerce engineer really mean? If you have a recruiter and a technical veteran and a software engineer all in the pipeline trying to find an e-commerce engineer, that can lead to misses. But these AI systems are actually really good, especially if you fine-tune them and give them the right kind of data — they can tell you actually usually what they mean by an e-commerce engineer is experience with WordPress or PHP or Shopify or payment APIs and so on. And so these automated tools help us unpack that, find people that match those kinds of opportunities, and then crucially also pick up on the soft skills that our partners are asking for. They might be asking for somebody entrepreneurial who’s okay with chaos, and in the interview transcripts these LLMs can actually pick up evidence for these kinds of behaviors, which makes a behavioral interview such a rich place for real data that an AI system can reason about.

For us that’s just superhuman throughput. We’re still, in places, doing this manually — we’re really good at it, but we still make mistakes because we’re human. As we offload this more and more to large language models under the supervision of people who have experience doing this, we’re finding that things are more consistent, things are much faster, the experience is better for our partners and for the teammates that agree to be hired on the Howdy.com platform, and we’re seeing those kinds of productivity boosts. For example, it might take 45 minutes to get the CV, the transcripts, and all that and build a little synopsis for a partner to review and determine whether or not they want to interview this person, just have a chat with them. That chat could be a 20-minute chat, and it might take an hour of reading through all those things, writing things. In the old world — our new systems can do this automatically, instantaneously, as soon as that data is available. So it’s a drastic difference in productivity for us. And again, it’s the kind of stuff that takes some skill, but it’s mostly grunt work to read a bunch of stuff and collate it and put it together. An AI system is perfect for that, so long as you keep a human in the loop for the final product and the quality.

Yeah, the quality is so critical here. And you mentioned the retention, and the number that came up regarding retention when I was doing some research before this session was Howdy having an impressive 98% retention rate and a fully remote workforce, which I think is a very impressive number. Jacqueline, what’s your secret to keeping employees engaged and committed for the long term?

Going back to when I was talking about how this can scale linearly — one of the things that we realized, it’s going to sound so lame, but it’s something that we always lose sight of, is that every single person we bring on to the team is their own individual universe. They have their own families, their own obligations, their own trials and tribulations, their own aspirations, inspirations. And so we wanted to build the company to be able to support the individual on a one-person basis. What kind of support did they need? What was the infrastructure they needed? Of course, yes, we pay them well — we’re not paying them exceedingly well, but we give them great benefits. Can we take care of the basics of the person first? We pay them well, we give them great benefits, but then let’s start moving up higher through the levels. Let’s learn about this individual person and what does this individual person need? What are they aspiring to?

And what we decided to do was we built this group of folks called engineering mentors — people who were technical managers, technical leaders at companies before, and now they’re more on the people-development side, and they tend to have a higher EQ than, let’s say, someone who’s specifically trying to drive output of a product. These engineering mentors, who used to be former engineering managers, they only take up to 20 people, and their whole job — literally their whole job — is focused on the 20 folks on their team.

We actually adapted this from Y Combinator, when Frank and I went through Y Combinator — it was winter 2021. And what we realized, what makes YC so successful, is that every company gets what they call a group partner. A group partner is a former founder who did something substantial with their company in the past, whether it was a really sizable exit, whether they went public, maybe they’ve done it a few times. To be a YC group partner is to be a well-known founder of the past, and it’s someone that’s walked in your shoes and done what you’ve done, and they only had a certain number of companies and founders that they would mentor. Anytime Frank and I would go to office hours with these group partners, it was as if they were solving all of our problems within two-word sentences, and it was so nice to have that. And so I looked at Frank and said, “Frank, how can we create something similar for Howdy within the Howdy ecosystem?” And so we really adapted this engineering mentor program from that experience.

Uh, just a big thank you for having us, inviting us to chat and share our experiences with the community. It was a pleasure, and please reach out if you all have any questions, especially from a software engineering or product development perspective.

Awesome. Yeah, thank you. Thank you, Darby. Thank you. And also what you’re doing with Gen AI University — it’s a tremendous resource, especially for folks like me that don’t have this background. I would consider myself very much like a non-technical person who has a strong affinity for technical things, and so the more I can learn and the more I can get my hands around it, the more empowered I feel too. So I just love what you’re doing, and thank you for having us — we love supporting you and what you guys are doing.

You bet. For everybody else — oh yeah, sorry, go ahead, really quickly.

Last year I was laid off, and I am super happy that as a hiring firm you have embraced AI, because last year when I was going through different jobs and looking for work, I found that — I mean they wanted to hire me, but then when I went to ask them questions about how they were going to use AI in their firm, or how they were even going to start playing around with it, it was crickets. And I said, “I’m really sorry, but I can’t work for you.” Yeah, because I don’t want to be working for Blockbuster or Kodak at the end of the day. I don’t want to work for Netflix — we have to embrace this. So I’m completely thrilled that you have done so. Thank you so much, and we totally agree.

Right — if a firm, or an individual regardless of their role, isn’t thinking about how to deploy these things now, but more importantly how it changes the definition of their role and how it changes the medium by which they deliver value, then they’re going to be left behind by the people who realize, oh, it’s not the code part that delivered value, it was — I don’t know — the problem-solving part that delivered value. We totally agree.

And out of this whole thing came the fact that we always say necessity is the creator of all kinds of things. Last year I actually built an app that helps people create resumes and cover letters in their voice, with their actual experience, so that it doesn’t embellish, it doesn’t go out of the boundaries — and that’s my new business. So that’s how that happened. I’m really grateful, I’m glad to hear that you guys have embraced it in such a way.

Amazing. Thank you. Thanks so much for sharing, and thanks everyone else for being a part of this community. As always, we’ll see you guys in the next episode on YouTube, inside the community. Make sure you’re out there hiring — whether it be through Howdy or otherwise — that you’re thinking through who you’re hiring through, that you are investing in your people, and you’re investing the time it takes to adopt this technology, because there will be winners and losers from this, and I for one want to be on the winning side, as I hope everyone here does as well. So until next time, keep calm and AI on. We’ll see you guys on the other side. Cheers, y’all.