4. 4
Example for data-informed
decision making that made
Zalando successful:
Why do we make returns as
easy and convenient as
possible for customers?
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5. 5
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How might we enable data-informed decision making
at scale in an organization with > 10k employees?
6. 6
Forces of gravity in
a big organization:
• Fragmented tracking
• Data silos
• Unclear data quality
• Time-consuming access
to data and tooling
• Missing analytical rigour
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7. 7
Our answer:
Foundation of Product
Analytics to bring
data-informed decision
making to the next level
across Zalando
13. 13
Spark a fire:
Teams intrinsically get
into the flow of iterating
optimization cycles
when seeing the first
encouraging results
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14. 14
Have the right balance
between small + big ideas:
Don’t only do frontend
testing, think about bigger
experimentation
use cases that involve
backend testing
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15. 15
Think beyond data:
Use qualitative research to
get at the why. The value
is in the insights behind
data
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16. 16
Set expectations:
• Success looks like:
Increasing number of tests
and teams
• Only a part of tests with
significant results
• Nonsignificant results
don’t mean no learning
• Avoiding downlift easier
than generating uplift
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17. 17
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A true data and experimentation culture helps our
team build better products and move faster...