Founder @ Flow

Most SaaS companies don’t lose customers because of product. They never make it there in the first place because of crappy onboarding.

I’ve led marketing & growth at five SaaS scale-ups, and advised many more. Wherever I went, I saw the same pattern repeating itself: founders spending millions to acquire users, only to lose them in the first few clicks.

In 2022, I founded Flow to help B2B SaaS companies fix their onboarding gap, and turn trial users into loyal subscribers. Basically, my job is to speed-run users as quickly as possible to their first “aha” moment: that’s what really makes them stick.

Here’s how we do it:
- First we’re uncovering how users really experience your product (and where they drop off).
- Then we’re pinpointing onboarding friction that kills activation and conversions.
- Based on that, we build personalized onboarding flows across in-app, email, and beyond, using scalable low-code solutions.

Acquisition is expensive, retention is priceless - and onboarding is the bridge between the two. Our clients see faster activation, higher subscription rates, and less cancellations. They get users that stay, succeed, and become advocates.

If you’re leading a SaaS company and want your users to stick, I’d love to connect.

A $5M ARR SaaS company I spoke with had almost no user analytics.

Let that sink in. Millions in revenue, but critical product decisions were being made in the dark.

This isn't a rare exception; it's a widespread problem. Teams are drowning in data but starving for insight because they don't track the right events, or worse, don't trust the data they have.

Without reliable metrics, you can't find your bottlenecks. You can't measure feature impact. You can't confidently build a roadmap.

𝗧𝗵𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗰𝗵𝗮𝗼𝘀 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗺𝗲𝘀𝘀𝘆 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱. 𝗜𝘁'𝘀 𝘀𝘁𝗮𝗹𝗹𝗲𝗱 𝗴𝗿𝗼𝘄𝘁𝗵.

Jamie McDermott

Most product teams swear they're data-driven. They’ll show you charts, dashboards, and custom metrics until your eyes glaze over.

But here's the test: can your founder actually make a big decision using that data, in real time?

I see this disconnect all the time. The product team is deep in their own tools, proud of their analytics, maybe even a little territorial. But when it’s time for the founder to see the story - the thread that connects user behavior to business health - it falls apart.

This isn’t a tech problem. It’s a translation problem.

If the founder can’t spot the signals, the data is just noise. No alignment, no urgency, no clear path forward. The product team keeps optimizing, but the big decisions stall.

You don’t need another dashboard. You need a shared language.

The best product orgs don’t just collect numbers - they turn them into narratives leadership cares about. The result: the team gets air cover, the founder moves fast, and the business actually uses those insights.

It all comes down to whether your data is just for the product team, or if it’s a bridge to real buy-in.

Teams that figure this out don’t just move faster. They win.

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To make your onboarding more valuable, do less for your customer.

It sounds counterintuitive. But doing everything for your customer isn't always a good thing.

In a recent client call, we mapped out their onboarding. It was thorough. The team handled everything: phone number setup, calendar configuration, contact list uploads. But I pointed out a hidden cost.

By automating these tasks 𝘧𝘰𝘳 the user, you're not just saving them time. You're potentially robbing them of their first 𝗮𝗵𝗮 𝗺𝗼𝗺𝗲𝗻𝘁.

That feeling of satisfaction when a user connects their own calendar and sees it sync perfectly? That's a powerful win. It builds confidence and momentum. When your team does it on the back end, the user misses that moment of discovery.

The takeaway is simple. Don't just build a product. Engineer the user's first success. Guide them to their own "aha moment" instead of claiming it for yourself.

That's how you turn a setup process into an activation engine.

Your perception of your onboarding is wrong.

We hear this from founders constantly: "Our onboarding is pretty simple. Most people get it." They believe it. But their customer support data tells a different story.

On a recent project, we analyzed thousands of support tickets for a client who felt their onboarding was straightforward. The data was screaming the opposite. Ticket after ticket mentioned confusion, frustration, and difficulty with the initial setup.

The founder's perception was based on anecdotal feedback. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝘄𝗮𝘀 𝗯𝘂𝗿𝗶𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲𝗶𝗿 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘀𝘆𝘀𝘁𝗲𝗺.

This is a massive blind spot for SaaS companies. You can't rely on vibes or what you 𝘵𝘩𝘪𝘯𝘬 is happening. The unfiltered truth lives in the raw, unstructured data—your support chats, feedback surveys, and call transcripts.

That's where you find the real friction points killing your activation. Your customers are telling you exactly what's broken. You're just not listening in the right place.

Check out my thoughts on 'product tours'...



Thanks to the Chameleon team for letting me dig into their data.

You spent 3 weeks building the perfect product tour. 
Users spent 0.4 seconds dismissing it. 
Welcome to onboarding in 2025. 💀

Thanks to Jamie McDermott for putting together this great playbook 👇 
https://lnkd.in/dh7B62uk

The Hidden Metrics of Effective Product Tours | Chameleon

Nobody actually wants to talk about onboarding ROI. It sounds strategic, but it’s a dead-end debate.

If you’re still building a business case by promising future gains, you’re missing the real story: the damage is already being done.

Look at your support queue. How many tickets are just cleaning up after a confusing signup flow? How many hours disappear every week chasing users who never got past step one?

You don’t need a forecast to see the cost. It’s right there in the churn, the endless follow-ups, the manual fixes your team handles over and over.

I’ve seen SaaS teams pour resources into new features while the same onboarding issues quietly eat their margins. Not because they don’t care, but because the cost is scattered and invisible.

Add it up. The firefighting, the lost deals, the users who never activate. That’s your business case. Not some theoretical upside, but the cash you’re bleeding every month.

If you still need “ROI” to justify fixing onboarding, you’re looking in the wrong direction.

Sometimes the most expensive mistakes are the ones nobody’s tracking.

If your churn meetings feel like a game of hot potato, you’re not alone.

Too many leadership teams treat churn like a customer success problem. Easy target, right? But it's rarely just a CSM issue.

If your product is buggy, no amount of relationship-building will save you.

If you're selling to the wrong fit, no handoff deck fixes that.

If marketing promises things the product can't deliver, no QBR will make the disappointment go away.

And if your support is drowning in tickets, don’t expect a renewal, no matter how many check-ins you schedule.

Customer success can only patch so many holes. Most of the leaks start way upstream.

Retention is a mirror: it reflects every weakness in your funnel, your product, and your messaging.

If you want to move the needle, stop the blame game and start owning your slice of the problem. Product, sales, marketing, support, success - nobody gets a free pass.

Churn isn’t a department. It’s a symptom.

Treat the cause, not just the pain.

Stop prioritizing complex integrations. Find the most boring, repetitive task your user hates and automate it. That's your real product magic.

During a deep dive into a client's onboarding process, their Head of CS said something that cut through the noise:

"Automated workflows are definitely powerful with customers."

We'd been discussing complex EHR integrations and calendar setups. But her point was a reminder of what users 𝘵𝘳𝘶𝘭𝘺 value. It's not the technical connection itself. It's the magic of seeing a manual, time-consuming process simply disappear.

For their users, seeing a workflow trigger automatically based on an event in their system was the real "aha moment." It was the point where the product's value became tangible.

This is a critical lesson for prioritization.

Instead of getting lost in complex integrations, find the most painful manual task your user performs. Then, show them how your product makes it effortless. That's where you'll find the most powerful activation moments.

Here's our 3-step process for finding the biggest bottlenecks in any SaaS product.

You just need a system to find them.

We call it finding the bottleneck. Instead of guessing, we analyze everything to get a complete picture of the user journey.

Our process is simple but rigorous:

🔍 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮: We plug into customer support tickets, online reviews, and feedback surveys to understand user frustration in their own words.

📊 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮: We connect to tools like Amplitude and Mixpanel to pinpoint exactly where users drop off in the onboarding flow.

💡 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗗𝗼𝘁𝘀: We combine both data sets to form a clear hypothesis. 𝘞𝘩𝘺 are users getting stuck, and what's the highest-impact fix?

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘀𝗵𝗶𝗻𝘆 𝗼𝗯𝗷𝗲𝗰𝘁𝘀. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝘂𝘀𝗲𝗿 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗴𝗿𝗼𝘄𝘁𝗵.

The average B2B SaaS activation rate sits at 37.5%. 📊

But here's what I'm seeing from teams breaking through that ceiling:

𝗧𝗵𝗲𝘆'𝗿𝗲 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗱𝗱𝗶𝗻𝗴 𝗔𝗜 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀. 𝗧𝗵𝗲𝘆'𝗿𝗲 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗴𝗿𝗼𝘂𝗻𝗱 𝘂𝗽.

Last month, I audited three SaaS companies stuck at sub-30% activation rates. Each had the same bottleneck: generic onboarding that treated every user the same way.

𝗧𝗵𝗲 𝗳𝗶𝘅 𝘄𝗮𝘀𝗻'𝘁 𝗺𝗼𝗿𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀. 𝗜𝘁 𝘄𝗮𝘀 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗽𝗮𝘁𝗵𝘀.

Here's what the breakthrough teams are doing differently:

• 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 - AI analyzes signup data to serve the right first experience
• 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝘃𝗮𝗹𝘂𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 - Products adapt based on user behavior in real-time
• 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗶𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻 - Systems detect friction and adjust before users drop off
• 𝗦𝗲𝗹𝗳-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗳𝗹𝗼𝘄𝘀 - A/B tests run continuously without manual oversight

The result? One client jumped from 28% to 52% activation in 90 days.

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻𝘀𝗶𝗴𝗵𝘁. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝘁.

Your onboarding should know more about each user on day one than your current system learns in 30 days.

If you're still running the same activation playbook from 2022, you're already behind.

What's the biggest activation bottleneck you'd want AI to solve first?

🔥 66-67% of growth experiments fail at companies like Microsoft.

But it's not the ideas that suck.

I've audited 200+ failed experiments across SaaS companies. The pattern is always the same:

𝗧𝗵𝗲 𝗦𝗶𝗹𝗲𝗻𝘁 𝗞𝗶𝗹𝗹𝗲𝗿𝘀:
• No clear success criteria defined upfront
• Missing activation event definitions
• Zero interpretation rules for edge cases
• No timeline boundaries for decision-making

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀:

✅ 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: "If we change X, then Y metric will improve by Z% because [user behavior assumption]"

✅ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗧𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱𝘀: Define minimum detectable effect BEFORE you start

✅ 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸𝗮𝗴𝗲: Connect every test to your core activation events

✅ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗥𝘂𝗹𝗲𝘀: Pre-commit to what constitutes success, failure, and inconclusive

The companies that nail this? They turn experiments into predictable growth engines.

The ones that don't? They waste months on "promising" tests that lead nowhere.

𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Great ideas without decision frameworks are just expensive guesses.

What's your biggest experimentation blind spot? Drop it below 👇

Raw data doesn't drive decisions. Structure does. If your user research ends in a Word doc, you've failed.

You just finished 20 user interviews. Now what?

You're left with a mountain of transcripts and raw insights. This is a classic bottleneck. The research is done, but turning it into a clear, actionable plan that gets the whole team aligned is a project in itself.

Instead of writing a 50-page report no one will read, we translate research directly into visual frameworks. Here's the simple process we're using for a client right now:

- 𝗠𝗮𝗽 𝘁𝗵𝗲 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆: Create a simple, visual map of the user's experience from awareness to activation.

- 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗝𝗼𝗯𝘀 𝘁𝗼 𝗯𝗲 𝗗𝗼𝗻𝗲: For each stage, pull out the core JTBD from your research. What is the user 𝘳𝘦𝘢𝘭𝘭𝘺 trying to accomplish?

- 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝘄𝗶𝘁𝗵 𝗮 𝗠𝗼𝗱𝗲𝗹: Use a framework like the Kano Model to categorize features into must-haves, performance-based, and delighters.

The output isn't a static document. It's a living FigJam board that becomes the foundation for the project, sparking conversation and driving the roadmap.

Stop writing reports. Start building shared understanding.

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