"Stakeholders when I send data to PowerBI: 🥱
Stakeholders when I send data to Google Ads: 💃"
Let’s be honest: too many data teams stop at nice-looking dashboards. They deliver charts, not change. Reports, not revenue. But some teams go beyond. They power decisions, automate actions, and drive business outcomes. One of the clearest examples of this? Zalando’s 200 million percent (!) revenue growth in just three years.
Yes, you read that right.
From €6 million in 2009 to €1.2 billion by 2012 (and then doubling again over the next two years), Zalando went from startup to Europe’s dominant fashion e-commerce player, leaving incumbents and online peers like ASOS in the dust. And the not-so-secret weapon behind this explosion? Data - used the right way.
Why You Should Care
If you’re a data analyst, engineer, scientist, or leader, this matters. Because most data teams want impact. But very few know how to get there.
This newsletter breaks down:
- Why most data teams get stuck at the bottom of the maturity ladder
- The four levels of data maturity and why skipping them breaks everything
- How Zalando's data team avoided these traps and helped build one of Europe’s most iconic startups
- What your team can do today to climb that ladder, step by step
The Problem: The Dashboard Trap
Most data teams make one of two mistakes:
- They start at level one and stop there.
- They try to skip straight to level four.
Let’s be clear: both approaches fail.
Here’s what the four levels of data maturity actually look like:
1 Descriptive – What happened?
→ Low effort, low impact.
→ Lives in dashboards. Easy to do. Easy to ignore.
2 Diagnostic – Why did it happen?
→ Higher effort, higher impact.
→ Can be done with Excel/Google Sheets or AI agents sitting on top of a clean data foundation.
3 Predictive – What will happen next?
→ High effort, high impact.
→ Uses machine learning, forecasting, and proactive AI agents.
4 Prescriptive – What should we do about it?
→ Highest effort, highest impact.
→ Data directly powers operational decisions (think: feeding LTV predictions into ad platforms).
A lot of teams try to jump from 1 → 4.
It doesn’t work. Why?
- No clean data foundation
- No trust in the numbers
- No shared understanding of the drivers
- No feedback loops to learn from results
The best data teams? They don’t skip steps. They build strong foundations and move up the ladder - fast.
Zalando: A Growth Story Built on Data
Let’s rewind. Between 2009 and 2012, Zalando went from €6M to €1.2B in revenue. That’s 200M% growth in three years. And by 2014, they doubled it again.
Competitors were still figuring out e-commerce. Zalando? Already dominating.
Yes, they had lots of funding. But many companies had cash. Very few turned it into a machine. Zalando did - and the engine was data.
The “Free Returns” Gamble
Zalando became famous for one thing: unconditional free returns.
Back then, this was radical. And customers loved it. But free returns? Extremely expensive.
So how did they make it work?
They could only offer this feature because of two data-driven capabilities:
- Accurately predicting customer lifetime value (LTV), factoring in return behavior.
- Spending marketing budgets based on predicted LTV, not first-order revenue.
Let’s break this down.
The Old Way: Revenue-Based Attribution
Before Zalando, most companies calculated marketing ROI like this:
- A customer orders €100 worth of products.
- That order came from a Google Ad.
- Conclusion: “Google Ads made us €100. Let’s spend more there.”
Two big problems:
- Return Rates – With return rates of up to 70%, LTV could be way below €100.
- Single Touch Attribution – Customers don’t convert after one click. Real journeys span multiple touchpoints (display ads, search, email, etc.).
The Zalando Way: Predictive Marketing Spend
Here’s what Zalando’s data team did differently:
1. Predicted true LTV per user → Factored in expected return rates, repeat behavior, retention curves.
2. Modeled multi-touch attribution. → Measured the influence of each touchpoint in a customer journey. → Stopped crediting the last click only.
3. Fed predictive data directly into ad platforms. → Instead of just viewing dashboards, Zalando pushed CLTV predictions into Google Ads. → This let them spend confidently and scale fast.
4. Automated decisions based on data. → That’s Level 4 data maturity: prescriptive analytics in action.
Their competitors? Still looking at dashboards.
The Bottom Line: Dashboards Don’t Drive Growth. Decisions Do.
Zalando didn’t win because they had more data. They won because they used data to make better, faster, and smarter decisions - automatically.
Their data team wasn’t stuck building dashboards.
They were:
- Predicting returns
- Modeling lifetime value
- Calculating multi-touch attribution
- Automating marketing spend
And that’s what you need to aim for.
If your data ends in Power BI, it’s the end of the road.
If your data fuels decisions, it’s the beginning of real impact.
So the question is: Where is your data going: into dashboards, or into decisions?
In my Masterclass From Dashboard Factory to Strategic Partner , I am teaching all the frameworks you need to build data teams that move from Level 1 to Level 4. I was leading the data team of Rocket Internet - the venture builder behind Zalando's growth - from 2010 to 2014 and packed all my insights into this class.
See you inside the masterclass or in the next episode next week!
Cheers,
Sebastian
