Linear #153: Don't Hire "SaaS" Customer Success Leaders, Teamshares to IPO via SPAC, Stop Checking The AI Box
One vSaaS breakdown. One biz story. One 'how to'. In your inbox once a week.
Today’s newsletter is sponsored by Unit, the leading embedded finance platform for vertical SaaS.
Become the financial OS your customers rely on. With Unit’s Ready-to-Launch solutions, you can embed capital, banking, and bill pay in weeks - zero build required. Trusted by platforms like Bill.com, HoneyBook, and Homebase.
Learn more at unit.co
Alright, let’s get to it…
Stop Hiring “SaaS” Customer Success Leaders
I’m about to tell you something that most recruiters won’t: That “SaaS’“ Customer Success leader with decades of experience you’re about to hire? They’re probably not going to work out.
I know how this sounds. You’ve just raised your Series A. You’re scaling from twenty customers to two hundred. Everyone’s telling you that you need a “world-class” Head of Customer Success. So you go on LinkedIn, find someone who spent ten years at Salesforce or HubSpot managing a book of accounts, has “scaled CS from ten million to one hundred million ARR” on their resume, and speaks fluent SaaS metrics.
You make the offer. They accept. Six months later, your churn is up, your customers are confused, and your new CS leader is frustrated because “these customers just don’t get it.”
Here’s what happened: You hired someone who knows SaaS, but doesn’t know your customer. And in vertical markets, that’s backwards.
Why horizontal SaaS CS doesn’t translate
Let me paint you a picture of what customer success looks like at a horizontal SaaS company. Your customer is a VP of Marketing at a tech company. They have a team. They have a budget. They’re sophisticated software buyers who’ve implemented fifteen different SaaS tools. They understand onboarding, training, adoption metrics, and feature releases. When your CS manager hops on a quarterly business review, they’re speaking the same language.
Your CS leader’s job is to drive product adoption, identify expansion opportunities, manage renewals, and escalate issues to support. It’s a well-defined playbook. There are frameworks. There are best practices. There are benchmarks for what “good” looks like.
Now let me paint you a different picture. Your vertical SaaS sells to HVAC contractors. Your customer is a fifty-three-year-old business owner who started as a field technician twenty years ago. He works seventy-hour weeks. He’s never used Salesforce. He’s suspicious of “the cloud” because he got burned by QuickBooks Online three years ago. His previous software was literally a filing cabinet and a spiral notebook.
When your shiny new SaaS CS leader schedules a “QBR to review adoption metrics and identify expansion opportunities,” this contractor has no idea what you’re talking about. He just wants to know if his technicians are using the damn thing to log their jobs, and whether he can finally stop doing payroll manually.
See the problem?
The credibility gap
Here’s what I learned the hard way at CourseKey. We were selling to Trade Schools. These are people who’ve spent twenty to thirty years as a blue collar professional.
Early on, I hired a CS leader from a horizontal SaaS company. Smart person. Great resume. Knew all the frameworks. Within two months, I started hearing feedback from our customers: “Your new person is nice, but they don’t really understand anything about our business. They look lost on every call.”
And they were right.
When a customer called with a question about how to handle attendance tracking for consortium courses where students take classes at multiple field sites but need consolidated reporting for financial aid, my CS leader had no frame of reference. They could troubleshoot the software, sure. But they couldn’t speak to anything industry-related.
We lost credibility. And in vertical markets, credibility is everything.
The breakthrough came when I hired someone different. She had spent twelve years working at a Trade School. Her job had been implementing all the technology, running the change management initiatives, ensuring the projects were successful. She’d lived through multiple different software migrations. She understood the pain intimately.
When she talked to Trade Schools, they immediately knew she got it. She spoke their language. She understood their constraints. She knew why certain features mattered and others didn’t. Our retention went up twenty percent plus over the next 24 months.
That’s when I learned the rule: In vertical SaaS, industry credibility beats SaaS expertise every single time.
The perfect vSaaS CS profile
So what should you actually look for when hiring your Head of Customer Success for a vertical business? Here’s the profile that works.
First, they should have spent five to ten years working in your target industry. Not selling to the industry. Not consulting to the industry. Actually working in it. If you’re building software for restaurants, they should have managed restaurants.
This isn’t negotiable. You need someone who’s felt the pain your customers feel.
Second, their role within that industry should have involved implementing technology. They were the person who rolled out the new POS system at their restaurant group. They were the project manager who implemented the new project management software at their construction company. They were the clinical informatics specialist who deployed the EHR system at their hospital.
This matters because they understand both sides. They know the industry pain, and they’ve lived through the technology solution. They know what good implementation looks like. They know what makes adoption succeed or fail. They’ve seen the mistakes.
Third, they should have been responsible for ensuring that the technology was successful. Not just implementing it and walking away, but living with the consequences. They had to train users. They had to troubleshoot problems. They had to prove ROI to leadership. They had to deal with the field technician who refused to use the tablet, or the nurse who kept reverting to paper charts.
This creates empathy. They know what your customers are going through because they’ve been your customer.
Fourth, they should be a natural explainer and teacher. In vertical markets, your customers often aren’t sophisticated software users. Your CS leader needs to be patient, clear, and capable of meeting customers where they are. The ability to translate technical concepts into industry language is critical. If their selling to blue collar, they need to be a little rough around the edges. Have the ability to push back respectfully. To stand up for your technology and your team when the rubber meets the road.
And fifth, they should be curious about business outcomes, not just software metrics. A horizontal SaaS CS leader obsesses over DAU, feature adoption rates, and expansion revenue. A great vertical CS leader obsesses over whether the restaurant reduced their food costs, whether the contractor won more bids, whether the clinic saw more patients. They connect software usage to industry-specific business results.
Where to find these people
You’re not going to find this person on a traditional CS recruiter’s desk. They don’t have “Customer Success Manager” on their resume. They probably don’t even know that job title exists.
Here’s where to look. Start with your existing customers. Who at their organization was responsible for rolling out your software? Who trained everyone? Who became the internal champion? That person might be ready for a new challenge. I’ve hired multiple CS leaders this way, and all three were exceptional.
Check industry conferences and trade shows. The people speaking on panels about “digital transformation” or “technology adoption” in your vertical are exactly who you want. They have the credibility, the network, and the experience.
And don’t be afraid to hire someone who’s never had the “Head of Customer Success” title. If they’ve got the industry experience and the technology implementation background, you can teach them the SaaS frameworks. You can’t teach them ten years of industry knowledge.
What to teach them
Once you hire your industry-native CS leader, you do need to teach them the SaaS side. They probably don’t know what NRR means. They’ve never built a customer health score. They don’t have a framework for segmenting accounts by risk and opportunity.
That’s okay. This stuff is teachable. And I’ve found these folks learn the “SaaS” side of the house in <90 days. It’s not that hard. It’s much easier than the 10+ years of the industry they’ve been consumed by.
Set them up with courses on SaaS metrics. Have them talk to your investors about what metrics matter and why. Send them to a SaaS customer success conference.
They should be talking to customers in industry language and reporting to you in SaaS language. That’s the bridge you need them to build.
The bottom line
Stop hiring SaaS Customer Success leaders for your vertical business. They’re optimized for the wrong game. Horizontal SaaS is about managing software buyers who understand software. Vertical SaaS is about transforming industries one business at a time, often for customers who’ve never been that successful with software before.
You need someone who’s lived in your customers’ world. Someone who’s implemented technology and felt the pain of adoption. Someone who can translate between industry language and software language. Someone who measures success by business outcomes, not just software metrics.
Find that person. They won’t have the perfect resume. They won’t know all the SaaS buzzwords. But they’ll understand your customers in ways no amount of training can replicate.
And in vertical markets, that understanding is the difference between a customer success team that drives retention and one that drives churn.
Hire from the industry. Teach them SaaS. Watch your retention numbers transform.
You’ve got this.
Teamshares Goes Public via SPAC
Employee ownership changes everything.
Michael Brown knew it. Alex Eu knew it. Kevin Shiiba knew it.
When your employees own equity in the business, they show up differently. They care about margins. They think long-term. They build something that lasts.
So in 2019, they launched Teamshares with a beautiful thesis:
Buy small Main Street businesses whose boomer owners are ready to retire. Turn the employees into owners. Build financial products around them. Create generational wealth.
Six years later, the numbers look impressive on the surface:
87 businesses acquired
$484M in revenue
2,100+ employees across 29 states
$250M raised from top-tier VCs
Going public via SPAC at $746M enterprise value
But here’s where it gets interesting (and a little messy)...
When you dig into the SPAC docs, the battle scars show.
They’ve spent roughly $500M to generate $60M in asset-level EBITDA. That’s an 8x+ implied multiple for sub-$1M EBITDA businesses that most search funds wouldn’t even look at.
Their central team costs $41M annually. For context, Swedish compounder Röko manages a $670M revenue portfolio with just $5M in overhead at the holdco.
That’s a 10x difference.
And they’re burning $30M per year just on interest payments at ~20% rates.
The lesson?
I love what Teamshares is doing. Employee ownership matters. SMB succession is a massive problem worth solving.
America needs solutions for the 10+ million boomer-owned businesses that will change hands in the next decade. Employee ownership is a compelling answer. But it is very tough to do this when the capital you borrow is at 20% and you have 100+ FTE’s at the hold co.
They will work through it, and build a massive business. But you have to be so cautious of debt my friends. I’ve lived that pain and it’s a VERY TOUGH balancing act.
If you’d like to dig in on their announcement / deck here is the link.
Stop Trying To Check The “AI Box”
I’m going to say something that might get me uninvited to the next vertical SaaS conference: Most “AI-powered” vertical SaaS companies are BS.
There. I said it.
Walk into any VC pitch meeting in 2025 and you’ll hear the same script. “We’re building AI-native vertical SaaS for [insert industry].” “Our AI copilot transforms how [insert profession] works.” “We’re leveraging large language models to revolutionize [insert workflow].”
Then you dig into the product and discover it’s a chatbot wrapper. Or a basic GPT-4 integration that could’ve been built in a weekend. Or worse, it’s vaporware with “AI coming soon” on the roadmap.
Here’s what’s happening:
Founders have convinced themselves they need to “check the AI box” to raise money.
VCs have convinced themselves they need to “invest in AI” to stay relevant. And customers? They’re stuck with half-baked solutions that solve technology problems, not business problems.
This is backwards. Completely, utterly, dangerously backwards.
Let me take you back to 2019-2020. If you wanted to raise money for vertical SaaS back then, you needed to talk about “cloud-native architecture,” “mobile-first design,” and “API-first platform.” Those were the buzzwords investors wanted to hear. And you know what? Most of that was bullshit too.
Because guess what actually mattered for vertical SaaS success? Understanding the customer better than anyone else. That’s it. That’s the whole game.
Toast didn’t win restaurants because they had the best cloud architecture.
They won because they understood that restaurants needed fast checkout to reduce table turn time, easy menu updates so they could change prices without reprinting, integrated payments so everything lived in one system instead of three, and shift management to schedule staff efficiently. The technology stack was just the delivery mechanism for solving real problems.
Now replace “cloud-native” with “AI-powered” and we’re making the exact same mistake all over again.
Starting with problems, not technology
Here’s the framework that actually works..
First, find a painful, expensive, recurring problem. Not “an industry that could benefit from AI,” but a specific problem that costs your customer time, money, or sanity.
Think about the problems that led to billion-dollar companies:
Procore exists because construction project managers were drowning in paper RFIs, change orders, and documentation. Projects ran over budget because information lived in filing cabinets, not systems.
Veeva got built because pharma sales reps needed to detail doctors with FDA-compliant content while tracking interactions, and Salesforce couldn’t handle the regulatory complexity.
ServiceTitan emerged because HVAC technicians were running their businesses out of notepads and QuickBooks, and they desperately needed scheduling, dispatching, invoicing, and payment processing in one place.
Notice what’s missing from all of these? AI wasn’t the problem. Fragmented workflows were the problem.
Second, you build the simplest solution that solves the problem completely. Not the most impressive technology. Not the most “innovative” approach. The simplest thing that actually works.
At CourseKey, Trade Schools needed to track student attendance for federal compliance. They were doing it on paper, than manually entering it into the system of record. Our first solution? A basic mobile app that automated student check-in based on their location. It then automatically synced that data to the system of record. No computer vision. No AR. Nothing fancy.
Was it revolutionary? No. Was it valuable? Absolutely. We got to ~$1M ARR with that simple solution. Once we were in ~100 Trade Schools we saw a whole host of other problems we could solve in a similar way. We went multi-product. Some worked, some didn’t. But eventually we launched our own system of record.
You should only add complexity to solutions when it demonstrably improves outcomes. This is where AI can enter the picture, but only if it passes a brutal test.
Does this AI feature solve a problem that another solution couldn’t solve? Does it save the customer meaningful time or money? Does it work reliably enough that they’ll trust it? Does it justify the added complexity, cost, and support burden?
If the answer to any of those is “no,” don’t build it.
I’m not anti-AI. I’m all in on AI. But I’m anti-AI-theater.
There are places where AI creates genuine, measurable value in vertical markets, and they all share something in common: they start with a real problem.
Take medical billing companies processing insurance EOBs, those Explanation of Benefits documents. The problem is that humans manually read PDFs and enter data into billing systems. This takes hours and introduces errors. An AI solution using OCR and extraction models can pull structured data from unstructured documents. The outcome? Ten times faster processing, ninety percent fewer errors, meaningful ROI. This is real AI value.
Or consider restaurant inventory management. Restaurants either over-order and waste food, or under-order and run out of menu items. Machine learning models analyzing historical sales, weather, events, and seasonality can predict tomorrow’s demand with real accuracy. The result is fifteen to twenty percent reduction in food waste and fewer stockouts. Again, real AI value.
Or look at legal case management. Paralegals spend hours organizing discovery documents by relevance. Natural language processing models that understand legal context can auto-categorize these documents, delivering sixty percent time savings on document review. Real value.
Notice the pattern? In each case, we started with a specific, measurable problem.
Then we used AI as one tool in the solution. We didn’t start by asking “how can we use AI in legal tech?” We asked “what takes paralegals the most time?” and discovered the answer happened to be solvable with AI.
The questions that expose AI Theater
When someone pitches you an “AI-powered” vertical SaaS solution, you need to ask some hard questions. First, what would happen if you removed the AI? Would the product still solve the core problem? If yes, then the AI is nice-to-have, not the innovation. If no, you need to dig deeper.
Second, can you show me the before and after metrics for customers using versus not using the AI features? If they can show you time savings or error reduction, that’s real value. If they talk about “better insights” without numbers, that’s theater.
Third, what happens when the AI gets it wrong? If they say “it never gets anything wrong,” they’re either lying or haven’t launched yet. If they have a clear error-handling workflow, they’ve actually thought it through.
Fourth, did customers ask for this AI feature, or did you assume they wanted it? If customers asked for it, you’re probably solving a real problem. If you assumed they wanted it, you’re probably just checking a box.
The mistakes everyone makes
I see the same patterns over and over. Founders decide they’re going to build AI for some industry, then they go looking for problems to solve. This is like deciding to open a restaurant, then figuring out what cuisine to serve based on what kitchen equipment is trendy. It’s completely backwards.
The truth about vertical SaaS success
The best vertical SaaS companies are built on deep understanding of customers within a particular industry. Every founder I interview on VERTICALS is obsessed with their end customer and industry.
They win because they understand their customers’ problems better than anyone else and built solutions that worked. If AI helped them do that, great. If it didn’t, they moved on to what actually mattered.
Your customers don’t care about your technology stack. They care about whether you save them time, make them money, or reduce their customer churn. Everything else is just noise.
Start with the problem. Build an incredible solution. If AI is part of that, great. If not, who cares.
The vertical SaaS winners of the next decade won’t be the ones with the most sophisticated AI. They’ll be the ones who solved real problems while everyone else was checking AI boxes.
Have a product or service that would be great for our audience of vertical SaaS founders/operators/investors? Reply to this email or shoot us a note at ls@lukesophinos.com