SlideShare a Scribd company logo
Alison Glazer
NOV 01
2019
PREDICTINGSTARTUP
FAILURES
Intro
● Crunchbase Enterprise paid service
● VCs source thousands of potential
investments annually
● Use early performance data to
predict future outcomes
methodologyDATA:
● Crunchbase
● Companies founded within last decade with 2+ funding rounds
ASSUMPTIONS:
● IPO or M&A = SUCCESS | No funding in >2.5 years = CLOSED
IMPLEMENTATION:
● Classification Algorithms + Web App
TOOLS:
Features,models
&metrics
Model: Logistic Regression
Features:
● Funding amounts
● Funding timing
● Industry
● Location
Metric: f 𝝱
score
Results
Baseline likelihood
of success
31%
Results
Results
⬆MONEY=
⬆SUCCESS
Results
⬆AverageTime
between
rounds=⬆ success
⬆Timebetween1st and
2ndround=⬇success
Results
Hardwarecompanies
Aremorelikelyto
fail
Results
f 𝝱
=0.85
questions?
Sources
https://www.toptal.com/finance/venture-capital-consultants/state-of-venture-capital-industry-2019
https://seekingalpha.com/article/4203810-2-types-investing-trading-errors
http://www.industryventures.com/2017/02/07/the-venture-capital-risk-and-return-matrix/
http://www.angelblog.net/Venture_Capital_Funds_How_the_Math_Works.html
https://techcrunch.com/2017/05/17/heres-how-likely-your-startup-is-to-get-acquired-at-any-stage/
APPENDIX
ModelComparison-ROCCURVE
Precisionrecallcurve
CONFUSIONMATRIX
Decisiontreeexample

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