Data comes from

 

1. Where does the data come from in Zalando Size & Fit?

A Senior Data Analyst does not just analyse one table — they actively connect multiple data sources, such as:

1. Customer behavior data

From Zalando’s platforms:

• Clickstream: what customers click, which sizes they view, where they drop off

• Conversion funnel: view → add to cart → purchase → return

• Return reasons: “too small”, “too large”, “didn’t like the fit”
  → This is the core source to understand Size & Fit problems from the customer side

2. Product & assortment data

From product management systems:

• Brand, category, gender, material

• Size charts per brand

• Actual garment measurements
  → Used to analyse whether issues come from the product itself or customer behavior

3. Logistics & operations data

• Return flows

• Cost per return

• Lead times
  → To quantify the financial impact of Size & Fit problems

4. ML / Algorithm output data

From the Applied Science team:

• Size recommendation scores

• Fit prediction confidence
  → You analyse whether the models actually improve business outcomes

5. External data (when needed)

• Body measurement distributions by country

• Market sizing standards
  → To benchmark Zalando’s sizing against market norms

👉 The Senior Data Analyst’s role is to connect all these sources, not analyse them in isolation.

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2. What is the purpose of data analysis in this role?

Not just reporting, but answering strategic business questions.

A. Problem diagnosis (root cause analysis)

Example:

• Why is return rate rising for women’s jeans in France?

  • Bad size charts?

  • Wrong customer selection?

  • Weak size recommendations?
    → Data is used to find the real root cause, before any A/B test.

B. Prioritization of efforts

Zalando has millions of products — not everything can be fixed at once:

• Which category should be tackled first?

• Which brands generate the most returns?

• Which market gives the highest ROI if improved?

→ This is portfolio and impact analysis, not A/B testing.

C. Guiding product and ML direction

Before testing:

• Should the model focus first on jackets or shoes?

• Which features are likely to create the most impact?

→ Data guides where Product and ML should invest, not only evaluates afterwards.

D. Measuring business impact

After solutions are implemented:

• How much did returns drop?

• How much margin improved?

• How much logistics cost was saved?

→ This is financial translation, which fits your Finance background perfectly.

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3. Where does A/B testing fit in all this?

A/B testing is only one part of the full analytical cycle, not the whole job.

The correct Senior DA flow at Zalando is:

1. Explore & diagnose data

2. Form hypotheses

3. Design A/B tests

4. Analyse test results

5. Translate into business decisions

👉 Without steps 1 & 2, A/B tests become blind experiments.

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4. Why does the JD emphasize A/B testing so much?

Because Zalando is:

• Product-driven

• Strongly data-informed

• Requires causal proof for changes, not just correlations

But:
👉 A/B testing is a tool
👉 Data analysis is the core mindset of the role

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5. Why this fits your profile especially well

You are particularly strong here because:

• Finance + Data → you don’t just ask “is it different?”
  but “is it worth investing in?”

• Your Heineken & SCB experience trained you in:

  • Root cause analysis

  • Portfolio prioritization

  • P&L and risk impact

→ That is exactly what defines a Senior analyst, not just someone who runs queries.