Nick Broekema

Getting you inbound leads through Content Design

I ran an agency for 6 years and invisibility nearly killed it (twice).  

We 100% relied on word-of-mouth marketing:

- Leads from friends and family (not even kidding)
- Leads from former and current clients
- Leads through competitors

We took on what we could get.

Sometimes we skyrocketed. Often we struggled.

The feast and famine cycle was real.

And it was painfully frustrating.

As a business owner, I didn't have:

- Time and focus on growth
- Time to take care of my team
- Time to generate leads myself

I was consistently hustling and putting out fires.

When I pivoted in 2022, I chose a different strategy:

→ Creating content to build a reputation and presence
→ Creating content to show the world my offering
→ Creating content to generate leads every week

At least, that was the plan.

It took me months to do it:

I started with text posts. Then I added visuals. Then I added carousels. Then I 'designed' my content. Every upgrade increased my growth and revenue.

This led me to craft a new solution:

Content Design for LinkedIn. 

Since then, I've helped clients build recognizable brands that attract and sell: 

- Matt Barker 2X’d his post views, attracted higher paying clients, and 2X’d product sales within 2 months of working together.

- Mollie Cox made $16k (next to her full-time job) within 2 months of working together.

- Pascalle Bergman's revenue went up by 73% after working together.

If that sounds good, there 2 ways I help founders, coaches, and creators attract leads on LinkedIn: 

1. Content Design 1:1 coaching to attract your ideal audience
2. My Content Design cohort (see Featured for waitlist)

Check out my Featured section 💪

Brand Scaling Consultant

Important question: 

Do people on Facebook also say

“This isn’t LinkedIn”

When posts are too professional?

Content design to attract your ideal audience on LinkedIn

I'm Dutch and I have 87,100 followers on LinkedIn. 

A few stats I read about Dutch LinkedIn users: 

- 2,582 people have over 5k followers
- 1,179 people have over 10k followers
- 346 people have over 25k followers
- 109 people have over 50k followers
- 47 people have over 100k followers

Last week, I ranked #34 in the Top 50 Most Influential LinkedIn Users in the Netherlands.
 
I also found out I’m ranked #3 in Favikon's list of Top 10 Growth Experts in the Netherlands.
 
This makes me pretty cool Dutch person right? 

Well, last week I was skiing with friends most of whom I’ve known since the age of 12.
 
I got tagged in those two lists in the same week and told a few of them.

Time with friends can be very grounding. 
 
Most smiled but had no idea what I was talking about.
Another friend said "ah ok but can you get beers".
Another friend didn't even hear what I said. 

You see, a mention in these lists is flattering. 

But 99% of all people have no clue what this is all about. 

We're all creating and yapping in our own bubble. 

And that's okay. 

As for my friends: 

They were the first people who supported me back when I had 900 followers. 

Carry on.

Client: "I think we should cancel my group event"

Me: "Why do you want to do that?"

Client: "I'm afraid I won't sell any seats"

Me: "okay let's zoom out" 

The reality was that she had: 

- sent 1 email (with more to go)
- 7 days to go until kick-off
- 500+ people on a waitlist
- Great people in her corner
- Targeted LinkedIn content
- A great email strategy
- A goal of selling 10 seats 

And a massively valuable group program. 

We already got her to €30k-€40k months.

No way we were cancelling. 

So she kept shipping. 

After 1 day: 

"I SOLD ONE"

The day after: 

"I SOLD 8 SEATS". 

Yesterday (kick-off day): 

"I SOLD 31 SEATS"

You see, it’s tempting to give up so you can’t fail.
 
But what if you saw it as a test instead?

Worst case: you learn to improve. 

Best case: you sell out. 

Either way, you win.

LinkedIn in 2026 feels different.

After 3.5+ years of posting, here’s what I’m seeing.
 
1. AI made writing easier but it didn't make it better. My feed is flooded with generic “best practices” and comment sections full of AI summarizing posts. People are tired of polished mediocrity. I think we'll see a silent human-written renaissance: raw experience, bold opinions, real stories. If you’ve been outsourcing your brain to AI, try writing without it. You’ll feel how weak that muscle became (I noticed it and I only used it for proofreading).

2. Pattern interruptors are popular. Short brain dumps. Parody. Rage bait. Random but relatable takes that cut through the noise. People engage because it feels different in a sea of sameness. The problem is you can’t force originality or humor. I'd test these formats but you arguably need to add other formats for positioning and sales.

3. The fundamentals will always win. Tell stories that make the right people think “that’s me.” Show proof so they see you as the go-to. Demonstrate results so they trust you. And learn to convert conversations into revenue. I wasted a lot of time chasing shiny trends before realizing this is the only mix that compounds.

4. LinkedIn behaves like the stock market. You see others win, you take the plunge, results don’t come instantly, you quit. But time in the market beats timing the market. I needed 12 months to hit 10k, another 12 to hit 30k, and after 3.5 years I’m at 87k. Profit followed the same curve. Treat it like "Dollar Cost Averaging": small consistent contributions until momentum kicks in. 

5. Smaller accounts are thriving while bigger ones feel the squeeze. I remember when 500-700 likes per post felt normal. After crossing 50k, volatility kicked in and reach became less predictable. Many large accounts struggled or disappeared, while 1k-20k accounts grew fast. My assumption: platforms reward beginners first, then make you earn it. If you’re "smaller", ride that wave. 

6. The growth you see around you isn’t representative of your own. Bigger accounts have different goals: audience growth, newsletter subs, content partnerships, launching communities and consulting offers. Most spend their entire day here (even though they say they don't). Most entrepreneurs and solopreneurs here sell services. Don’t compare your account growth to theirs.

This is not a statement against AI, the algorithm, or trends.

They CAN be your ally. 

It's about staying in the game even when it sucks.

I gave Claude access to my Gmail and created a client deck in 2 minutes.

I'm an AI newbie but this was really easy. 

How it works:

1. Open Claude
2. Click "Connectors" (settings)
3. Connect Gmail + Gamma
4. Prompt Claude: "Find my discovery call notes from [client name]. Create a campaign proposal deck in Gamma."

(Notion = optionally)

Then Claude automatically:

→ Searches your inbox
→ Pulls the meeting notes
→ Builds deck with budget, timeline, deliverables

Finally make it look better: 

→ Open Gamma 
→ Set your fonts, colors, and illustration style
→ Save as Theme
→ Add "use my pre-saved style" to your prompt

Every proposal is targeted, on-brand, and personalized. 

Start building yours here: 

https://lnkd.in/eHzb-FzF

Brand partnership • Content design to attract your ideal audience on LinkedIn

Create personalized sales decks with Gamma

"How can I build a recognizable brand?"

Got that question in a cohort session last week. 

It's a bit of a chicken and the egg situation: 

1. You need a brand that CAN become recognizable
2. You need to share it enough to become recognizable

You see, people who follow me recognize my "green". 

But I initially started with a very ugly orange.

In 2022, I did everything wrong: 

- I tried to create and post a carousel daily
- I color-coded them (like people would care)
- I talked about 10 different topics in my content

Result: 

Zero clients and zero brand recognition. 

A lot happened since then. 

Jeremy Bennett invited me on his podcast show. 

We talked about how content design generated over 25 million impressions (and more). 

Go check it out ↓

Your brand isn't memorable.

Nick Broekema shows you how to fix it.

You're posting. 
You're showing up. 
But people scroll right past you.

You need to:

Pick your style. 
Stop chasing trends. 
Stop reinventing every quarter.

But!

There is a correct way to do this!
Don't take this part for granted.
You need a professional.

You can't just watch a YouTube video and figure this out.
It takes someone who understands the psychology. 
Someone who's devoted their life to this craft.

In this episode, Nick shares what works. 

Absolute must-watch!

Do you do your own design? 
Let us know in the comments.

Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

I saw a horror story on Reddit that highlights the biggest AI risk in 2026 (and beyond)

A company let an AI data agent answer leadership's business questions for three months.

The output was complete fabrication:

- Sales targetted territories based on fake revenue.
- CFO presented decks filled with incorrect metrics.
- Managers made decisions based on hallucinations.

If you plug AI directly into your data without providing sufficient context, it will simply guess the answer. 

It will sound confident, but it will likely be completely wrong.

The missing piece here is the foundational work; data modeling + metadata + governance that explicitly tells the AI:

"This is how we calculate Net Revenue."
"This is the definition of a qualified lead."
"This is how we measure churn."

It might not be the most exciting part of data management, but as we rely on AI to do more of the heavy lifting, it is absolutely essential.

HeyReach.io hit $10M ARR in 29 months. Here’s the data system we built to help them scale past $20M+ ARR.

From the outside, HeyReach.io looked like a rocket ship: 

→ 2,900+ monthly trials. 
→ 10-15% Month-over-Month growth. 
→ The Linkedin layer of the sales stack.

But internally, data was a big bottleneck. 

They were wasting hours every week manually stitching spreadsheets just to know how they were doing.

We partnered with them to install a permanent infrastructure: 𝗧𝗵𝗲 𝗦𝗮𝗮𝗦 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗦𝘆𝘀𝘁𝗲𝗺.

The 3 step execution plan:

𝟭. 𝗧𝗵𝗲 𝗔𝘂𝗱𝗶𝘁: 
We traced every trial, upgrade, and failed payment.

𝟮. 𝗟𝗼𝗴𝗶𝗰 𝗟𝗮𝘆𝗲𝗿
We coded their specific metrics definitions (not Stripe's defaults).

𝟯. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
We built a live command center for the C-Suite.

Before: conflicting numbers & manual work.
After: automated, real-time insights for all teams.

You can't run a $10M ARR company on guesswork. 

You need good data.

Swipe to see the complete breakdown.

(ps. check the comments for details on how we work ↓)

The $10M ARR data system we built for HeyReach

New €5,000 deal. 3 deals in pipeline. 6 calls. 

Here’s my 7-day update:

I spent most of the week doing outreach, no automation. 

I put quite some time writing personal messages. Response rate is fairly high (10-20%).

Had some good chats. Definitely works if you care.

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Based on these conversations, we now include a 1-day on-site build in our offer.

Meaning, we come to your office & build your data system together.

I’ve done this in the past. 

High-energy, fun, and speeds up the results.

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:
- finalizing new €5,000 deal.
- 3 open deals for new offer.
 
Calls (7 days):
- 5 calls with partners.
- 1 follow-up call with lead.

Substack (7 days):
- 97 new views.
- 8 new subs.

Linkedin (7 days):
- 28,887 new impressions.
- 101 new followers.

We're heading into the right direction I think.

Ready for the week ✌️

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

How we built Expandi's data system

I am looking for new tech partners to integrate into our $1M - $10M ARR client data stacks.

Context:

We build high-performance data systems. 

We are looking for best-in-class tools to implement for our customers and drive revenue.

🛑 Not looking for:

- generic lead gen or SEO services.
- dev shops / outsourcing agencies.
- "Waitlist only" products (must be live).

✅ I am looking for data & AI products in:

1. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲

- CDPs.
- reverse-ETL.
- pipeline orchestration.

2. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲

- AI agents.
- data enrichment.
- out-of-the-box ML models.

Or anything that turns data into revenue or operational speed.

Bonus: you have a BigQuery connector.

Plz do not DM me - drop it in the comments.

Here’s a format: 

[𝘯𝘢𝘮𝘦 𝘰𝘧 𝘵𝘰𝘰𝘭] + [𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘺𝘰𝘶 𝘴𝘰𝘭𝘷𝘦] + [𝘰𝘶𝘵𝘤𝘰𝘮𝘦]

If you fit the stack, I will DM you to see how we can collaborate.

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

7 dashboards I built for a $1,448,950 ARR B2B SaaS.

Most companies track vanity metrics.

We focus on the 3 core pillars of performance: 

1. Revenue.
2. GTM.
3. Product.

Here is the breakdown:

1. Revenue Performance

We don't look at just "Total ARR."
We break it down by MRR Movements:

- new business.
- net growth rate.
- net revenue retention (NRR).

2. The GTM Machine

We don't guess where leads were get stuck.
We visualize the full lifecycle:

- deal funnel.
- contact lifecycle.
- pipeline velocity.

3. Product & Support Health
  
We don't assume features were "useful." 
We measure it.

- time saved.
- feature adoption.
- support efficiency.

---

You can copy these dashboard layouts today.

But if the underlying data system isn't set up well, the numbers will be wrong.

Check them below 👇

7 Dashboards I Built For a $1,448,950 ARR B2B SaaS

I see a massive shift in growing B2B SaaS.

They move away from default reports (Stripe, HubSpot, ChartMogul)...

to building a Data Warehouse.

Why?

Because generic dashboards are built on generic assumptions.

As soon as your:

- billing.
- pricing.
- sales motion.

...is different from the default, these dashboards break.

A Data Warehouse ensures your data is accurate, automated, and trusted.

But don't take my word for it. 

Here are the 4 specific things founders love about the switch:

1. Custom logic > default metrics

Tools like Stripe have rigid definitions. They don't know your business context. 

We build custom logic so the data reflects the real world.

“Stripe was showing 20k in churn, but in reality, it was just two failed payments... If we cannot agree on what happened, how can we make a decision?"

— Ilya Stojkovski, CRO at HeyReach.io

2. Flexibility 

Most tools lock your data. If something breaks, you're dependent on them to fix it. 

We build a warehouse that you can maintain and extend yourself.

“What I really like is... that it's set up in a way that we can extend it ourselves... we're not dependent on one person or company.” 

— Simon Paul, CTO at Rentman

3. Escaping "Spreadsheet Monday"

Many teams spend their Monday manually cleaning CSVs. 

We automate the pipelines so you go from manual data entry to instant analysis.

“On Mondays, it took us 6 hours of manual entry... Our data warehouse saves us alteast 2 to 3 hours every week.” 

— Tim, Head of Finance at Level.works

4. Customer journey analysis

As you grow, data gets scattered. 

Marketing is in one tool. 
Product is in another. 

We bring these sources together to measure true ROI.

“Before, we were looking into multiple data sources... Now we can see which specific channel brings us not only signups, but actually brings us LTV.” 

— Glenn, CEO at Expandi

---

If data is a priority in 2026, we can help.

What you'll get:

1. aligned growth metrics across teams.
2. data warehouse built on best practices.
3. focused set of dashboards for decisions.

You own everything. We leave after 12 weeks.

Check the comments for the details. 👇

Founder: "I want an AI agent to do my ARR forecasting."

Me: "Show me your data infrastructure."

The reality check:

If your data is scattered across Stripe, HubSpot, and Google Sheets...

You don't need more AI. You need a Data Warehouse.

To build a "AI-Native" business, you have to fix your messy data first.

The old way:

❌ marketing calculates spend in spreadsheets.
❌ product counts users in Mixpanel.
❌ revops tracks ARR in Stripe.

→ Problem: when data is disconnected, AI lacks context.

Here's why that matters:

1. Conflicting definitions.
  
Marketing defines a "Lead" differently than Sales. The AI doesn't know which logic to trust.

2. Hallucinations.

When an agent can't find a single source of truth, it guesses. Guessing = hallucination.

3. Token bloat.

Your AI has to burn tokens trying to understand three conflicting data sources instead of answering the question.

The new way:

✅ centralize data in a Data Warehouse.
✅ add context + definitions.
✅ deploy AI agents on top.

→ Result: consistent, accurate, and cheap answers.

An AI agent is only as smart as the data it feeds on.

If you can't trace a customer from ad click to renewal, neither can your AI.

Fix the data first.

The agents come second.

Most dashboards & reports fail for one specific reason.

It rarely has to do with the tool or the data.

The problem is much simpler: metric definitions.

A dashboard full of metrics means nothing if no one agrees on what they mean.

I see this pattern constantly:

❌ sales trusts a spreadsheet over the dashboard.
❌ finance / Product report conflicting churn %.
❌ "active user" has 3 different definitions.

Before you build the dashboard, build the Metrics Dictionary.

It can be a simple Google Sheet that lists out your metrics + descriptions.

Here are the columns we include in it:

1. unit → is it €, $, %
2. filters → how to slice the data
3. calculation → how it's calculated
4. department → who cares / uses it
5. metric owner →who is accountable
6. refresh rate → how often it updates
7. description → what the metric means
8. data source → where the data comes from

I attached the template we use in the comments.

Copy it, tweak it, use it. ✌️

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

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Thomas Varekamp

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The Battle

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