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?

"I'm just overwhelmed" a cohort member said. 

A few weeks ago I did a session on copywriting. 

We had just discussed content ideation. 

"But how do we write the meat?" was asked. 

Hooks can be easy. What comes after can be hard. 

There's 1 framework you can use for ANY post.

(Not saying you always should)

It covers 3 vital content buckets:  

- Professional stories around your business
- Educational content that showcases your expertise
- Promo content that (soft) sells your offer

I call it SCART: 

→ Statement
→ Context
→ Action
→ Results
→ Trigger

How it works: 

1. You pick one option per layer
2. You fill in from top to bottom
3. You use your own experiences

Use it to write yourself or as a brief for AI. 

Keeps your content relevant for ICPs AND peers. 

PS. I'm cooking up something I've never done before (ore next week). Get on the waitlist:

https://lnkd.in/eaPTDU8b

Content design to attract your ideal audience on LinkedIn

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

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 | a data science team in your pocket for B2B SaaS.

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.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

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.

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

How we built Expandi's data system

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 👊

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

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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