Michel Brok 🎯

Tracking Architects

Tracking Architects empowers eCommerce and funnel website owners to maximize revenue from their marketing efforts through advanced server-side conversion tracking and data analytics.

Eigenaar

Team6

Team6 helps FMCG and retail businesses to improve business performance with data. Our focus is to make strategies work by bringing ways of working, data and technology together.

Founder

A.S. Watson Group

Helping the marketing team to use data to 1) make better decisions in target audiences, offerings and content and 2) increase relevancy and therefore increase customer lifetime value. 

Results:

- Created and implemented the CRM Data Strategy
- Implemented cross-functional CRM data team
- Implemented the CRM data-driven process
- Implemented algorithms for optimizing promos

Data Lead CRM a.i.

Danone

Leading 2nd Digital Transformation wave of the Dutch CBU for all Danone brands (dairy, plant based, medical & baby). Build the data foundation to take better decisions and actions to drive business value for the CBU.  

* Developed the data strategy and roadmap together with senior management
* Build the cross-functional data team overseeing data analytics, data science, architecture and platform
* Managed the local data platform and architecture (Azure) implementation
* Managed the integration of all digital channels in Azure data lake
* Delivered 3 use cases: customer 360 together with CDP, Marketing Mix Modeling & MROI (causal attribution)

Head of Data

Leading the Digital Transformation for the Benelux BU with focus on improved customer experience. 

* Building the digital environment and BU digital capabilities
* Transforming marketing strategy and operating model
* Shape and build the marketing technology stack, integrate within marketing operations
* Delivering and activation of digital products & services
* Leading design, experience, digital product & engineering teams

Head of Digital

E.ON

Responsible for e-commerce and digital marketing for the residential, SME market and large enterprises.

* Development and implementation of the digital transformation roadmap for the Benelux cluster
* Development and execution of the digital marketing & e-commerce strategy to drive brand awareness, customer acquisition, retention and NPS 
* Marketing technology: implementation of the Adobe CMS and migration existing digital platform, implementation of the customer self-service portal and connection between digital platform and SAP
* Management and optimization of traffic channels  SEA, SEO, affiliates, retargeting campaigns, display advertising and online partners
* Management and optimization of webconversion with A/B testing, data analytics and UX-optimization
* Implementation of the E.ON self service portal to increase self service and drive NPS and decrease costs
* Implemented Lean wow and performance driven mindset
* Managing and coaching multi-disciplinary online team (4 fte)
* Operational excellence day-2-day optimization working method

Team Manager Online Marketing

Essent

Responsible for online sales and lead generation for residential and SME market.

	Defined and implemented annual online sales plan, defined targets, budgets and weekly sales forecast
	Accelerated performance based sales & lead generation by optimize traffic sources (display advertising, SEA, SEO, affiliates) and CRO for Essent.nl. Managed referral and price comparison websites
	Implemented marketing campaigns ATL/BTL in online channels in collaboration with marketing and sales department 
	P&L responsible for digital sales

Digital Marketing Manager

Tribal

Helping clients driving business growth with developing and executing online marketing strategy focussed on increasing website traffic and conversion optimization.   

	Development and implementation of new online advertising campaigns to increase numbers of leads and / or sales
	Managed and optimized existing SEA and SEO campaigns
	Aligned projects with usability and web analytics experts 
	Evaluated campaigns by analysis with Google Analytics
	Reporting to customers and team lead

Online Marketing Consultant

Lensvelt Medical

Improving company awareness and developing customer service proposition to support customer acquisition and relationships.


	Development and implementation operational marketing plan
	Coordinated and implemented marketing activities such as product / price promotions, trade fairs, managed and implemented online advertising campaigns, website optimization and e-mail marketing
	development of services products and related concepts and responsible for activation via website, sales channel and fairs

Marketing Specialist

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

Business & Agency Owners – Listen Up:

The #1 reason your ad campaigns hit a wall?

INACCURATE DATA.

It’s the frustration holding back ambitious advertisers like you — those who are ready to scale but can’t break through.

At this stage, you’re trying to lower ad costs and increase value. But if the data you rely on is off, making the right decisions becomes nearly impossible.

Here’s the real issue:

Inaccurate data doesn’t just impact your decisions — it also sabotages the advertising algorithm.

The better your data, the better the algorithm can identify your best customers.

But getting accurate data and conversion tracking right is easier said than done.

That’s where the Data-Driven Scaling Formula comes in — putting accurate conversion tracking at the core of your strategy.

The Data-Driven Scaling Formula empowers ambitious business owners like you to scale faster by leveraging the right data at the right time.

The formula is made up of three key parts:

	1.	Enhanced Performance Tracking: Best-in-class server-side tracking combined with smart data usage throughout the entire customer journey. This ensures both you and the advertising algorithm are making better decisions and taking smarter actions.

	2.	Precision Algorithm Training: Fueling your advertising algorithm with high-quality data to significantly improve targeting. Now, you’re only spending money on customers who bring real value.

	3.	Transform Data into Growth: Gain control over your business’s growth by understanding what works — and what doesn’t — in both marketing and sales.

Curious about how we’ve helped others scale smarter?

Send me a DM, and let’s chat!

-----

Hi, I’m Michel – The Conversion Tracking Guy.

With over 18 years in Digital Marketing, I’ve witnessed firsthand how advertising platforms and marketing systems are becoming increasingly data-dependent. Yet, proper conversion tracking and data architecture are often overlooked because they seem complex or costly.

I’ve helped over 250 business owners unlock the full potential of their marketing with server-side tracking and analytics. In almost every case, the results were clear: a significant boost in performance.

Let’s talk about how accurate data can transform your business.

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.

Ecom subscription sellers: 

Stop wasting your Meta Ads budget on returning customers.

Such an easy fix, yet almost nobody does it.

Every major Ecom platform already gives you the parameter you need, you just have to plug it into your tracking setup.

Once you tell Meta who’s new vs returning, everything changes:

- Cleaner attribution
- Less wasted spend
- Better audience signals

More budget going to actual new customers

Meta can’t optimize for what you don’t tell it, give it the right data, and it will find the right people.

Want help setting it up? 

You know where to find me ;)

Michel Brok

I train your ad algorithms to find your best buyers. Accurate conversion tracking + smarter buying signals = higher ROAS.

You’re launching the Black Friday campaign with sweaty palms…
…and your developer just deleted the tracking code 😭

Always check  tracking first before launching  new campaigns:

1. For Meta, use the Meta Pixel helper tool
2. Google Ads, check the Google Tag Assistent

Still not sure if everything is working?

DM me😀

Shopify Analytics sucks.
Tracking breaks.
And “Direct” suddenly looks like your best-performing channel.

But here’s the thing, it’s not Shopify’s fault, it’s Consent Mode.

When a user doesn’t accept cookies, Shopify simply stops tracking.

No pageviews.
No addtocarts.
No purchases.
No channel.

Which means your data becomes… well, kind of useless.

Another place where tracking seems broken 🤯

So what are your options?

1️⃣ GA4 server-side setup with Consent Mode
2️⃣ Triple Whale

Both have their pros and cons, ut they’ll give you a much clearer picture of what’s really happening, even when users don’t consent.

What are you using right now to close this gap?

Helping agencies deliver better client results — with accurate tracking, stronger algorithm signals, and actionable ad dashboards

People don’t want to spend a dime on conversion tracking.
But they’re happy to invest in “algorithm training.”

Most advertisers still think tracking is just a fix.

It’s not, it’s the foundation for growth.

Better tracking doesn’t just lead to better decisions....
it sends better signals to the algorithm about who buys and who doesn’t.

Imagine being able to tell Meta or Google exactly who to target, that’s what accurate tracking really does.

Curious how accurate your tracking really is.
You know where to find me. 😉

“Our Google Ads spend doubled… but sales didn’t.”

Not funny when you’re managing a €600K budget.

Turns out, Google was seeing fake purchases.
So it kept spending like crazy because performance looked great.

What happened?
After a website update, purchase events started firing twice.

The algorithm thought it was crushing it.

That’s why you always audit your tracking after any major change:

- Review tracking codes
- Test if purchases fire correctly
- Monitor data closely the first few days

Nothing’s more painful than spending big…
and realizing you’ve been optimizing on fake results.

Proud that TrackingArchitects is one of Stape’s Top Partners.
And it’s more than just a badge.

Because my clients now know:

> I’ve done more than enough tracking implementations to handle any setup
> They can rely on an extra pair of expert eyes when accuracy matters most
> I follow a structured process to deliver the highest quality

It’s always great to be recognized, 
but even better when it helps my clients scale with confidence.

How’s your tracking going? 👀

70% of your Google Ads leads are spam.
Real problem? You keep feeding Google… spam data.

So Google thinks those fake leads are exactly what you want, it finds you more of them.

Meanwhile, the good leads?
Google has no idea what they look like.

Here’s the fix:

- Mark qualified leads in your CRM
- Setup offline conversion tracking 
- Activate Enhanced Conversions
- Gather data in Google Ads 

Make the switch when everything looks good and feed Google with signals from high quality leads. 

Better signals = better leads

Still getting mad when I see this. 

You're spending 100k monthly on Ads and using a free tracking plugin 🤯 

Audited an e-commerce brand this week.
Meta was their ONLY growth channel.
Big budget. Big ambitions.

But they were only sending 60-70% of their data back to Meta.

Why?

The Facebook app in Shopify was not tracking all data. 

And then they wondered why:

> ROAS was unstable
> Scaling didn’t work
> Meta was “inconsistent”

The algorithm can’t optimize on blind spots (and it's getting worse).

So we fixed it:

+ Implemented a real data layer
+ Added server-side tracking
+ Passed email, phone, location
+ Captured click IDs

Result? 20-30% more conversions. Instantly.

DON’T save on tracking please 🙏

Meta and Google Ads numbers seem off… and you’ve already tried everything:

- Free plugins
- Agency experts
- YouTube tutorials
- Cheap freelancers
- In-house developers
- A fresh Google Tag Manager setup

But still, after 6 months you’re staring at numbers that make zero sense whilst your competitor absolutely crushing it.

Let's end this frustration once and for all.

Hire a tracking expert or agency, ask them:

1. How many times the've done the setup you need
2. How they measure accuracy and how to optimize for
3. If they have a 'second-pair-of-eyes' mechanism
4. What their monitoring process is

Time to stop doubting and start scaling with confidence.

Sebastian Hewing 🚀

Data Action Mentor

Building masterclasses and communities for data leaders and future data leaders who want to create outsized business impact with data & AI.

Sebastian Hewing Consulting

🚀 I build impactful, AI-first data infrastructure for VC/PE-backed consumer companies.

Previous clients include Zalando, Lazada, Global Fashion Group (Dafiti.com, Lamoda.ru, Jabong.com, TheIconic.com.au, Zalora.com), Linio, Jumia, Lieferando, eBay, Telefonica o2, HUK Coburg, Axel Springer, Lendico, OCC Assekuradeur, Creadi AG, Boerse Stuttgart, Bergfreunde.de / Backcountry.com, Sanity Group, Takeaway.com, Wasoko, Westwing, Beat81 and more

Owner

Locafox GmbH

Raised EUR 10+M from established Investors such as Holtzbrinck Ventures to digitize local retail and build Europe's largest omni-channel shopping marketplace

- Built up Business Intelligence and Analytics Team from Scratch
- Established a true Data Self Service Environment (Around 90% of Analytics Requests could be handled independently by Functional Departments)
- Managed and Led Market Entry to Spain
- Spain contributed 1/3 of the Total Revenues after 6 Months

Director Business Intelligence & International

Rocket Internet SE

Rocket Internet is the world's largest Internet incubator. Our team has been building online companies since 1999 and has created over 100 market leading companies in 50+ countries, dozens of which have been exited successfully. Examples: Zalando, Lazada, Delivery Hero, HelloFresh.

- Implemented and customised 7 Data Warehouses (DWHs) for Rocket Internet’s global portfolio
companies.
- Product Owner for Analytics Applications such as split-testing, catalogue
sorting algorithms, product recommendation algorithms, marketing attribution
modelling and predictive modelling.
- Built and led the global Data Team. Disciplinary lead for 10 central
employees and functional lead for 40+ employees in 7 Portfolio companies.
- Increased Revenue per Visitor of 3 Portfolio companies by 10-12% with the
help of a custom-built product recommendation algorithm.
- Management of the central Analytics Budget for 50+ Portfolio companies.

Head of Business Analytics

SHS VIVEON AG

One of Europe's leading boutique consulting firms for Data Analytics, Data Warehousing and Customer Management.

- Managed a variety of projects with focus on customer analytics; e.g. customer
segmentation, churn prevention and development of customer scorecards.
- Accountable for the development of the Multi-Channel Marketing practice area,
specializing in the field of Marketing-Performance-Monitoring and -Optimisation.
- Fast career progression, receiving semi-annual promotions.

Consultant

InsightAsia Research Group / DemandROMI (JV)

built up a pan-Asian consulting division focusing on Marketing & Sales Performance

Accenture

conducted emprical study about success factors of Multi-Channel Management in Banking as part of final thesis

Business Analyst

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

I'm a mix of data engineer, entrepreneur, product manager and marketer. 🛠️🚀🧑‍💻🎯

I help tomorrow's data leaders create outsized business impact by building AI-first, 10X data teams.

-> Find out more at www.dataactionmentor.com

In the last 17+ years, I worked as a Data Practitioner and Data Leader for Startups, Corporates, and in Consulting across all continents. 

As Head of Business Analytics at Rocket Internet, the world's largest venture builder, I built and led Data & Analytics Teams and Infrastructure for 7 portfolio companies in 15 countries.

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) and Jumia (the first African company on the NYSE). 

As a consultant and advisor to Founders and CEOs, I built or re-built the first data team and data infrastructure for 50+ leading consumer companies.

P.S.: My biggest passion next to Data & AI is Traveling. I live on the beautiful island of Taiwan 🇹🇼 but I typically spend 5-6 months per year traveling to hang out with my awesome global clients. 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

Putting together a bunch of tools is NOT a data strategy!

A data strategy is this: 👇 (refined over 18 years)

1. A problem to solve

Start by uncovering and deeply understanding stakeholder problems (not with tools and data stacks!)

2. A human who has the problem

Learn about the jobs-to-be-done your users are facing. Sit on their lap, if necessary.

3. A unique value proposition (UVP)

Clearly communicate why your users should drop Excel to use your services.

4. A solution

Craft solutions that solve your user’s problem(s) and take away their anxiety to try them (be accessible)

5. A distribution strategy

The best solution is worthless if your stakeholders don’t know about it. Let the world know why your data team rocks!

6. A systems strategy

The best data teams spend more time building systems than being a slave in a system. Don’t be a dashboard factory! Build a problem solving factory!

7. Outcomes

Most data teams only produce output - tickets solved, dashboards built yada yada. You need to produce dream outcomes for your stakeholders!

8. Cost Structure

A necessary puzzle piece, but a lot of data teams worry about it too much. Worry more about creating value than reducing cost!

9. People Strategy

Have a hiring and people management strategy that reflects your principles and values.

That’s all.

Easy peasy, lemon squeezy, right? 🍋

♻️ Repost if you’ve felt the pain of building dashboards while your stakeholders export to Excel.

👉 And join 3,000+ data leaders who read my free newsletter for actionable tips on building impactful data teams in the AI-era: https://lnkd.in/grgzCkUf

Building impactful data teams for the AI-age | Data Strategist & Engineer | Advisor to C-Level in VC/PE-backed companies | Solopreneur & Travel Addict (100+ countries & territories) 🌏

The best CMOs would crush it running a data team.

Because they get three things many data leaders struggle with:

1. Speed over perfection
2. Business impact over tooling
3. Influence over insight

I used to think marketing is all gut feeling and creativity.

But the best CMOs I’ve worked with proved me wrong.

→ They were fast.
→ They were analytical.
→ And they were obsessed with business impact.

Tomorrow, I’m breaking down the 6 traits that make top CMOs unstoppable - and how data teams can borrow those traits to become true business partners instead of dashboard factories.

Here’s a taste of what I’ll cover:

✅ How marketers act with speed despite messy, conflicting data
✅ Where data teams lose trust and how to fix it
✅ And what it really takes for data teams to earn a seat at the table

If you want to influence the business, you need to understand how high-performing marketers operate.

And tomorrow I’ll show you exactly how.

I’m sharing with 3,000+ data leaders how CMOs win - and how you can adopt their playbook to earn real influence as a data leader.

📩 Sign up for my FREE newsletter if you don’t want to miss it:
https://lnkd.in/g3WJTejm

They don’t hate dashboards.

They hate feeling stupid.

You built the scalable pipeline. The modular dbt models. The layered dashboards. The forecasting model.

And what do they do?

👉 “Can you export that to Excel?”

You think:

“Why do they ignore everything we built?”

But here’s a better question:

“Why did we build something they don’t want to use?”

Let’s be honest.

The villain isn’t the stakeholder.

It’s the complexity we’ve come to worship.

→ We throw AI at problems a simple SQL statement would solve.
→ We hide insight behind dropdowns, tabs, and filters - then wonder why nobody clicks.
→ We act like we’re building for Google.
→ But we’re a 200-person scale-up with 3 analysts and a CEO who checks Slack at 6am.

Complexity looks smart.

But it kills trust.

Simplicity is the real flex.

♻️ Repost if your CEO’s favorite BI tool has a green X icon.

👉 And join 3,000+ data leaders who read my FREE weekly tips on building impactful data teams in the AI-age:
https://lnkd.in/gHvQuqMc

“Our data stack has 16 tools.”

But stakeholders still don’t trust a single number.

It’s like owning 16 pairs of shoes and still feeling like you have nothing to wear.

You don’t have a tooling problem.

You have a clarity problem.

Data leaders keep falling into the same trap I once fell into:

→ Hire more engineers
→ Buy the fancy tech
→ Integrate all the data
→ Build a beautiful, scalable pipeline

…and still get weekly “WTF is this number?” emails from the CEO.

Here’s why:

🧠 You start with a tech stack, not with your users’ actual problems
🧩 You build infrastructure, but no one knows how it maps to business goals
🛠️ You design systems your team loves but your users hate

All of that makes your data team technically impressive and practically invisible.

Because trust isn’t built in dbt.

It’s built in moments like:

- “Ah, I get it now.”
- “That’s exactly what I needed.”
- “This matches what I see in Hubspot.”

If your data products don’t trigger these reactions, it doesn’t matter how “modular”, “real-time”, or “compliant” they are.

The goal isn’t to impress your peers.

The goal is to help your stakeholders live easier, calmer, better lives.

Want to become a true business partner with your data team?

👉 Join 3,000+ data leaders who read my free newsletter for weekly  tips on building impactful data teams in the AI-era:
https://lnkd.in/gETTkzRd

♻️ Repost if you’ve ever watched a stakeholder open Looker… and immediately go back to Excel.

The “single source of truth” is NOT a myth.

I used to think it was a fantasy too.

One clean, trusted dataset that aligns every team.

Like:
→ unicorns 🦄
→ dragons… 🐲
→ ...and matching revenue numbers across teams.

But here’s what changed my mind:

I realized most metric debates aren’t about the data.

They’re about perspective.

The “truth” is built from:
→ A measure: e.g. ARR
→ A filter: e.g. New ARR
→ A dimension: “Finance ARR”: invoice date, “Sales ARR”: deal closed date.

Sales wants to show momentum → “Closed deal date”

Finance wants precision → “Invoice date”

Same Measure.
Same Filter.
Different dimension.

Here's how I think about the single source of truth today:

Treat your metrics like Legos. Provide Measures, filters and dimensions in a single source of truth.

Then every domain can build their version from the same bricks.

You get governance + agility. 

Metrics aren’t magical. They’re just combinations.

♻️ Repost if you’ve ever lost sleep over Revenue definitions that almost matched

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era:
https://lnkd.in/gJUCHAGz

Everyone wants to use AI to reduce headcount.

But here’s the inconvenient truth no one wants to hear:

👉 The biggest driver of bloated data teams isn’t lack of AI tools - it’s lack of foundations.

→ If your single source of truth is chaos…
→ If definitions and metrics change depending on who you ask…
→ If data governance resembles a police checkpoint (or doesn’t exist…)

Then AI won’t make you lean.

You’ll just end up automating the mess and hiring more people to clean it up.
Let’s get specific.

Here's a realistic, right-sized setup for a late-stage scale-up, assuming solid foundations:

- 173 Users (consume data via dashboards & reports)
- 17 Super Users (can self-serve simple insights) → 10:1 ratio
- 6 Analysts / Data Scientists (handle complex questions) → 3:1
- 2 Analytics Engineers (curate data for analysis) → 3:1
- 1 Data Engineer (builds the technical platform) → 2:1
- 1–2 Product Owners / Data PMs (the glue across domains)

That’s 11 people in the data org, or 5.5% of employees.

Let’s recap:

🚫 AI won’t save you from foundational debt
✅ AI can amplify your impact - if your data isn’t broken
Otherwise?

You’ll be automating confusion and scaling chaos.

♻️ Repost if someone just told you AI will replace analysts and fix your data problems magically.

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gVEHHj_T

Sebastian Hewing

10x ROI with data & AI foundations for VC/PE-backed companies | Hands-on Data Strategist | Solopreneur & Travel Addict (100+ countries & territories) 🌏

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