The document describes a methodology used by Crunchbase Enterprise to predict startup failures using early performance data. Classification algorithms and logistic regression were used to analyze features like funding amounts, timing, industry, and location from companies that received 2+ funding rounds. The models found that receiving more money and shorter times between funding rounds increased success rates, while longer times between the first two rounds and being a hardware company decreased success. The logistic regression model had a beta score of 0.85, correctly predicting outcomes 85% of the time.

1. Alison Glazer
NOV 01
2019
PREDICTINGSTARTUP
FAILURES

2. Intro
● Crunchbase Enterprise paid service
● VCs source thousands of potential
investments annually
● Use early performance data to
predict future outcomes

3. 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:

4. Features,models
&metrics
Model: Logistic Regression
Features:
● Funding amounts
● Funding timing
● Industry
● Location
Metric: f 𝝱
score

5. Results
Baseline likelihood
of success
31%

6. Results

7. Results
⬆MONEY=
⬆SUCCESS

8. Results
⬆AverageTime
between
rounds=⬆ success
⬆Timebetween1st and
2ndround=⬇success

9. Results
Hardwarecompanies
Aremorelikelyto
fail

10. Results
f 𝝱
=0.85

12. questions?

13. 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/

14. APPENDIX

15. ModelComparison-ROCCURVE

16. Precisionrecallcurve

17. CONFUSIONMATRIX

18. Decisiontreeexample