This document discusses customer analytics at Zalando and outlines the 5 stages of data grief: collection, consolidation, interpretation, activation, and validation. It describes some of Zalando's analytics capabilities like brand and category recommendations. It also notes the challenges of data collection from over 100 microservices using various technologies and the process of moving to a more standardized up-front data design. Data consolidation involves bringing siloed data together, while interpretation is done by dedicated analysts and those embedded in business teams. Testing and product evaluation help activate the data.

1. Customer Analytics
@ Zalando

3. • Brand & category recommendations
• Purchase prediction
• Spend prediction
• Customer intent
• Other miscellaneous magic

4. The 5 stages of data grief:
1. Collection
2. Consolidation
3. Interpretation
4. Activation
5. Validation

5. Data Collection

6. A Hot Mess…
…and 100+ microservices
using god-knows-what

10. Up-front data design
{
"category": "business",
"default_statistic": null,
"enrichment_strategies": ["metadata_enrichment"],
"name": "order.ORDER_RECEIVED",
"owning_application": "order-service",
"partition_key_fields": ["order_number"],
"partition_strategy": "hash",
"schema": {
"schema": "{ "properties": { "order_number": { "type":
"string" } } }",
"type": "json_schema"
}
}
order.ORDER_RECEIVED

11. The data producer uses the
data they produce:

12. Data Consolidation

13. A warm mess…

14. Data Interpretation

15. • Dedicated analysts
• Embedded in teams
• Evaluating products
• Standardising KPIs
Product Analytics Department

16. A/B/…/Z Testing
• Multivariate testing
• Configurable via UI

17. Data Activation

18. “Build it and they will come.”

19. “Build it and they will come.”

21. • Ad-hoc / intuition-driven
• Directed / automated

22. Data Validation

23. 😢