by Alex Leeds Lead Data Scientist, Squarespace

1. SQUARESPACE
L O C A L
M O D E L S
O F
C U S T O M E R S
June 2017

2. What does data science
look like when it’s thriving?

3. A G E N D A
01 A Data Science Challenge
02 Foundations
03 Data
04 Results
05 What Can We Conclude?

4. Where - in what U.S cities - should
a company conduct targeted, localized
marketing?
The Challenge

5. 1. Identify cities where our company is doing well.
2. Gather location-specific data.
3. Determine drivers of performance.
4. Use these drivers to inform business strategy.
And Approach...

6. SQUARESPACE
F O U N D A T I O N S

9. Confidential and Proprietary Information
DATA DRIVEN ORGANIZATION

10. Data Collection
Data Science Practice
Qualitative Research
Data Engineering
Market / Media Testing

11. Data
Architecture

12. SQUARESPACE
D A T A

13. Where are customers located?
Local Marketing Metrics

15. Where are customers located?
Outcome Variables:
1. density: customers per relevant local population
2. growth: rate of local increase in customers
Local Marketing Metrics

16. What are the local sources of customers?
Local Marketing Metrics

18. What is the local performance of
marketing?
Local Marketing Metrics

19. City-level estimates
of advertising
efficiency...

20. What are additional local drivers?
Local Marketing Metrics

21. ● Young or old population?
● Internet use?
● Educational attainment?
● Employment by industry? Tech? Restaurants? Arts?
● Local business characteristics?
● Macro-econ factors: GDP growth? Wealth? Income?
Local Marketing Metrics

22. SQUARESPACE
R E S U L T S

23. What did we find?
● % of people 30 to 44 not younger
● % of people with graduate degrees
● number of food carts!

24. New York
Atlanta + Houston + Dallas
Philadelphia + Baltimore
West Palm Beach + Miami
City Clusters

25. 1) Focus on locations that our analysis predicts should do even
better than they have so far:
We’re calling this “slack”.
“slack” x “population” = “opportunity”
2) Focus on locations that are outperforming our expectations
AND still growing at a rapid rate.
3) (Ideally) pick cities from different clusters when we compare
them across the dimensions identified in the model.
How did we use these results?

26. SQUARESPACE
C O N C L U S I O N S

27. ● Customer location models
● Advertising attribution models
● Media mix models (spend input, sales output)
● Advertising efficiency models
● Customer engagement/loyalty models
● Customer lifetime value models
1. Data science models build on each other

28. ● Customer location models
● Advertising attribution models
● Media mix models (spend input, sales output)
● Advertising efficiency models
● Customer engagement/loyalty models
● Customer lifetime value models
2. Model diversity forces skill set diversity

29. ● Customer location models
● Advertising attribution models
● Media mix models (spend input, sales output)
● Advertising efficiency models
● Customer engagement/loyalty models
● Customer lifetime value models
3. …And interacts with organizational process
2. Model diversity forces skill set diversity

30. Data Collection
Data Science Practice
Qualitative Research
Data Engineering
Market / Media Testing

31. Data Collection
Data Science Practice
Qualitative Research
Data Engineering
Market / Media Testing
Implied...

32. SQUARESPACE
Q U E S T I O N S
Alex Leeds
aleeds@squarespace.com