The document discusses the importance of understanding users through data in product development, emphasizing the difference between customers and users and the necessity of prioritization based on return on investment. It outlines when to use quantitative research, stressing the need to confirm hypotheses and track user behavior while cautioning that correlation does not imply causation. It also suggests strategies for deriving hypotheses and emphasizes the need to visually present data and avoid confirmation bias in implementing findings.

1. Understanding Users
through Data
Ankur Sharma

2. @ankurdinesh
About me
● Director - Products
● 11+ years of building & scaling products from scratch
● Help startups on growth, platform issues and product.
● Alumni:

3. @ankurdinesh
What customer wants?
● You ≠ User
● Competition is not always right
● Limited resources; prioritisation needs based on ROI
● You can’t build for everyone

4. @ankurdinesh
When to use quantitative research?
● Seek to confirm a hypothesis about a phenomena
● To decide what to build next
● When you want to identify “What” (not Why)
● When you want to test one variable change theory
● When you want to track user behavior at scale

5. @ankurdinesh
Correlation does not imply causation

6. @ankurdinesh
Correlation does not imply causation

7. @ankurdinesh
Correlation does not imply causation

8. @ankurdinesh
Exercise:
1. Students going to premium B-schools get higher
salaries on an average
2. People who smoke are found to have higher level of
stress.
3. People who have kids are mature.
4. The higher you go, the colder it would become.
Correlation does not imply causation

9. @ankurdinesh
Strategy to derive hypothesis
● Surveys
● A/B Testing
● Fake door Testing
● Following Patterns
● Looking for variation & validate via qualitative
analysis

10. @ankurdinesh
Applying the findings
● Present data visually
● Steer clear of confirmation bias
● Implement and re-test