STARTUPS DATA

DECISIONSSami Can Tandoğdu

@sctandogdu

www.sctandogdu.com

November 20th 2014

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

What is data driven decision making?

Data driven decisionmeans depending on data on every single

decision you make for your startup

First of all lets call it data informed decision making

Never underestimate the your intuition!

Cognitive bias is your worst enemy!

Anchoring

Relying on the initial information way too much

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 $

Confirmation

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

world

Bandwagon Effect

Popularity bias: Follow the herd mentality

Checkhttps://en.wikipedia.org/wiki/List_of_cognitive_biases

Further reading

Be careful while building your Decision

Mechanism

AND Gate decisions

Director Other members Outcome

YES NO NO

NO YES NO

YES YES YES

NO NO NO

Perfect AND Gate: İş Bankası

OR Gate decisions

Director Other members Outcome

YES NO YES

NO YES YES

YES YES YES

NO NO NO

ME Gate decisions

Director Other members Outcome

YES NO YES

NO YES NO

YES YES YES

NO NO NO

NO Gate decisions

Autocracy vs Democracy

Come up with a theory!

Act like a scientist, try to collect data around your theory

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

You don't need big data, you need meaningful

data

Latent metrics vs predictive metrics

Checkhttp://a16z.com/2014/09/05/why-amazon-has-no-

profits-and-why-it-works/

Further reading

Vanity Metrics

Metrics that don’t give you any kind of insight

Finding the truth in data

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

140.0%

Jan Feb Mar Apr May Jun Jul Aug Sep Nov

Budget realization

Actionable Metrics

Metrics that you can act on!

If there is a correlation use that to your advantage!

0

5

10

15

20

25

30

35

40

45

50

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

Jan Feb Mar Apr May Jun Jul Aug Sep Nov

Number of meeting and sales to meetings ratio

# of meetings Sales to meeting ratio

Building decision models

It’s not that complex!

Your best friends

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?

Thanks!

Sami Can Tandoğdu

@sctandogdu

www.sctandogdu.com

Find the presentation @

http://www.slideshare.net/SamiCanTandogdu