Michel Brok 🎯

Marketing Automation & Data Expert | Helping retail & CPG to drive customer engagement at scale.

You’re scaling your ads,  but you don’t trust the numbers you’re seeing.

And here's the real problem: 

- If your tracking is wrong, your signals are wrong.
- If your signals are wrong, the algorithm learns the wrong buyers.
- And if the algorithm learns the wrong buyers…

scaling becomes impossible.

Most people think it's just a 'tracking' issue. It's not.
It's an 'algorithm' training issue. 

What I do:

I train your ad algorithms to find your best buyers by fixing the data they learn from and upgrading the signals they receive.

When signals are complete, consistent and accurate, your ads stop guessing and start learning.

What this looks like:

- Accurate conversion tracking across Meta, Google, TikTok & Pinterest
- Smarter buying signals (customer data, click IDs, first-party events)
- Server-side setups (Stape Top Partner)
- Better matching, higher Event Quality, cleaner attribution
- Eliminating signal loss caused by browsers, consent mode & plugins

What clients typically see:

- 20-50% more Meta sales
- 13–21% more Google Ads sales
- Lower CAC
- More stable scaling
- Decisions you can actually trust
- ROAS that finally makes sense

If this sounds familiar:

- Your dashboards don't add up
- You feel like you're scaling blind
- Meta says one thing, Shopify says another
- Results swing for reasons you can't explain

Send me a message.

Co-Founder

AldrichX is a data-driven marketing agency that helps 6+ figure fashion eCommerce brands to grow their business whilst they can keep focussing on building their brands.

Sebastian Hewing 🚀

Founder Data Action Mentor | Swiss Army Knife for Data & Analytics | Ex-Rocket Internet

Over the last 18 years I have built and led data teams across all continents - except Antarctica. ☃️

But my first data leadership job almost broke me:

It was 2010.

At 29 years old, I led the data team of the world’s largest venture builder (Rocket Internet).

Rocket Internet scaled companies such as:

→ Zalando (Europe's leading online fashion platform)
→ Delivery Hero (a leading food delivery platform)
→ HelloFresh (the world's leading meal kit delivery company)
→ Lazada (the Amazon.com of SouthEast Asia, exited to Alibaba)
→ Jumia (the first African company on the NYSE).

I saw those companies and data teams grow from tiny startups into massive, global market leaders.

And even though we built data teams for 7 companies in 15 countries, I struggled...

... hard.

→ We were drowning in ad-hoc requests instead of shaping strategy.
→ We were shipping dashboards instead of outcomes.
→ And ultimately stakeholders didn’t trust what we built.
→ And my influence, impact and personal growth hit a ceiling. 

Over the following years, I became a trusted advisor to Founders and CEOs by building winning data teams for 50+ companies.

The one thing I realized over all those years after leaving Rocket Internet: 

I was lacking an operating system for building winning data teams.

So, I created it.

The Data Team Adjusted Lean Canvas Framework transforms how data teams create value based on best practices from:

- data engineering
- product management
- marketing 
- behavioral science
- and entrepreneurship.

In my Data Strategy Masterclass, I have helped hundreds of growth-oriented data experts from 40+ countries become more influential by applying my framework and by building impactful data teams in the age of AI.

Favorite testimonial: “I got more value from your $350 course than a $3,000 University of Berkeley executive education course.”

→ Find out more at http://www.dataactionmentor.com/masterclass-business-impact  ← 

P.S.: My biggest passion besides Data, AI & Entrepreneurship is traveling. After 2 years of "digital nomading", I now have a home-base in Taipei, but I typically spend 5-6 months per year traveling. I have been to 100+ countries and territories.

Founder & Mentor

Solve all your Data- & Analytics Challenges and connect with experienced Data & AI Professionals for a price and time investment that every startup can afford

“Let’s define a data strategy.”

Translation: let’s argue about tools for 6 months.

Here’s the thing nobody wants to admit:

Most data strategies are just about data stacks.
Not actual strategy.

If you want a strategy that survives the next reorg, AI hype wave, or exec sponsor change - start here:

1. Problem: What are we solving?
2. Users: Who are you serving?
3. UVP: What's our data team's secret sauce?
4. Solution: What are we building and what are we NOT building?
5. Distribution: How will we get this in front of users without chasing them down?
6. Systems: What helps this scale without breaking us?
7. Outcomes: What will users actually do differently?
8. Costs: What’s the real investment? (Spoiler: it’s not just the software)
9. People: How will we grow a team that wants to stick around?
10. Vision: What guides our decisions when priorities clash?

Answer those 10?

You’ve got a data strategy.

Ignore them?

You’ve got a dashboard backlog and a stack no one uses.

♻️ Repost if you've ever watched a “data strategy session” turn into a Power BI vs Tableau debate.

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on data strategy: https://lnkd.in/grKYfwDM

Sebastian Hewing

Pragmatic Data Strategist | Outcome-driven data strategy that earns your seat in leadership decisions

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