Startups Data and Decisions
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Transcript of Startups Data and Decisions
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
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 $
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
Finding the truth in data
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Budget realization
If there is a correlation use that to your advantage!
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Number of meeting and sales to meetings ratio
# of meetings Sales to meeting ratio
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?