Lecture notes

1. STARTUPS
DATA
DECISIONS
Sami Can Tandoğdu
@sctandogdu
www.sctandogdu.com
November 20th 2014

2. Who am I?
• TED – Bilkent
• Corporate Life – PwC
• Entrepreneurship – Fikrimuhal, AçıkDemokrası
• Director of Finance – PUBLIK
• Freelance Consultant
• Currently working for Soostone, a NYC based artificial intelligence
/ machine learning startup

3. What is data driven
decision making?

4. Data driven decision
means depending on
data on every single
decision you make for
your startup

6. First of all lets call it data
informed decision making
Never underestimate the your
intuition!

7. Cognitive bias is your
worst enemy!

8. Anchoring
Relying on the initial
information way too much

9. Case of the Entreprenuer that did not
calculate his target market size
•An entrepreneur was talking about his partnership with
a flagship SF startup
•His business was built around this new startup, which
was quite big and growing double digit each year
•He was always talking about SF startups revenue and
growth, neglecting his own metrics
•We calculated his company’s target market
•Global market size was below 100 mn $

10. Confirmation
Point of view bias: thinking that
the world around you is the real
world

11. Bandwagon Effect
Popularity bias: Follow the herd
mentality

12. Further reading
Check
https://en.wikipedia.org/wiki/List_of_cognitive_biases

13. Be careful while building
your Decision
Mechanism

14. AND Gate decisions
Director Other members Outcome
YES NO NO
NO YES NO
YES YES YES
NO NO NO

15. Perfect AND Gate:
İş Bankası

16. OR Gate decisions
Director Other members Outcome
YES NO YES
NO YES YES
YES YES YES
NO NO NO

17. ME Gate decisions
Director Other members Outcome
YES NO YES
NO YES NO
YES YES YES
NO NO NO

18. NO Gate decisions

19. Autocracy vs Democracy

20. Come up with a theory!
Act like a scientist, try to collect
data around your theory

21. Case: 1-1 QnA Site
•A market place like product, helping people with
problems with meet problem solvers
•They were worried about the quality of answers
•They added a public comment section – BIG MISTAKE!
•They allowed comments on Youtube and Fbook – NO!
•They created a sentiment analysis system focusing on
answers to spot out ‘negative vibe’
•Found a correlation between negative vibes and
complaints
•Build a strategy guiding problem solvers

22. You don't need big data,
you need meaningful
data

26. Latent metrics vs
predictive metrics

27. Further reading
Check
http://a16z.com/2014/09/05/why-amazon-has-no-profits-
and-why-it-works/

28. Vanity Metrics
Metrics that don’t give you any
kind of insight

29. Finding the truth in data
140.0%
120.0%
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
Budget realization
Jan Feb Mar Apr May Jun Jul Aug Sep Nov

30. Actionable Metrics
Metrics that you can act on!

31. If there is a correlation use that to
your advantage!
50
45
40
35
30
25
20
15
10
5
0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
Number of meeting and sales to meetings ratio
Jan Feb Mar Apr May Jun Jul Aug Sep Nov
# of meetings Sales to meeting ratio

32. Building decision models
It’s not that complex!

33. Your best friends

34. HR: Hiring with data
• Try to define the position
• Understand the requirement for this position
• Hard skills (education, experience)
• Soft skills (company culture)
• Quantify - create a scoring card
• Reduce cognitive bias
• Conduct interviews with multiple candidates
• Conduct multiple interviews with each candidate
• Review your model each 6 month
• Have you done the right hiring?
• Does your model work?

35. Thanks!
Sami Can Tandoğdu
@sctandogdu
www.sctandogdu.com
Find the presentation @
http://www.slideshare.net/SamiCanTandogdu