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Crunchbase Signals Predicting Above Average Acquisition Price of Startups

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Annual venture capital investments have topped $48 billion in 2014 according to Crunchbase. Since 2007, the average successful US startup raised $41 million and exited at $242.9 million. Previous analyses by Crunchbase indicate a strong correlation between larger exits and companies that raised more money in their funding rounds. Using Crunchbase data we analyzed a collection of variables that may be significant in predicting an above average acquisition price for a startup.

Published in: Technology

Crunchbase Signals Predicting Above Average Acquisition Price of Startups

  1. 1. SIGNALS PREDICTING ABOVE AVERAGE ACQUISITION PRICES OF STARTUPS MIT Sloan School of Management Michelle Villagra and Victoria Young
  2. 2. VC Investments Top $48B in 2014 Objective: Find signals that can predict the likelihood of startups being successfully acquired at an above average acquisition price of $43M. Michelle Villagra and Victoria Young | MIT Sloan
  3. 3. Q: What are predictive signals in startup data? Michelle Villagra and Victoria Young | MIT Sloan Annual Venture Capital investments have reached its highest level in over a decade. What are some signals that can predict the likelihood of startups being successfully acquired at an above average acquisition price?
  4. 4. Project Scope | Available Data Startup Info Time Financial Name Acquiring Company Number of Employees (at the startup) Founding date Funding date Created Variables: Years until acquisition Acquired after 2000 Number of years in business Acquisition amount Total funding raised Short term assets (cash) Created Variables: Funding to cash/price Available Crunchbase data on companies that were acquired before July 25th, 2013. Michelle Villagra and Victoria Young | MIT Sloan
  5. 5. Hypothesis: Amount of $ Raised Is Key Signal Michelle Villagra and Victoria Young | MIT Sloan Since 2007 the average successful US startup raised $41 million and exited at $242.9 million with a strong correlation between larger exits and companies that raised more money. AboveAverageMedianAcquireFactor ~ raised_amount + founded_year + total_money + acquired_year + YearstoAcquire + Years2Funding + YearsInBiz + raised_amount + funded_year + AcquiredAfter2000 + Funding2Cash
  6. 6. Methodology | Approach Michelle Villagra and Victoria Young | MIT Sloan Data Extraction: We pulled data from the Enigma Database as .csv files. Data Organization (A) Formatting: Cleaned up money amounts that had symbols and letters mixed in with numbers (i.e. $10M USD to 10,000,000) (B) Cleaning: Removed N/As and duplicates to avoid skewing results (C) Integration: Merged 3 separate .csv files (D) Feature Expansion: Created new variables based on existing data (i.e. “Time In Business = 2013 - Founding Year)
  7. 7. Analysis | Strategy Michelle Villagra and Victoria Young | MIT Sloan ● Analysis: Logistic Regression, CART, Random Forest, Clustering ● Baseline: Average median acquisition price since 1996 is $43M*, 71.4% of startups in data have been acquired over this amount. ● Dependent variable: Binary, Above Average Acquisition (>$43M) ● Independent variables: Founding year, amount of funds raised, total money, acquired year, number of years in business, number of years from founding to acquisition, number of years until funded, funded year, number of years acquired after 2000, ratio of funds raised to acquisition amount. *WilmerHale VC Report
  8. 8. Challenges | Analysis Michelle Villagra and Victoria Young | MIT Sloan Accurate Data: Over 30,000+ observations (combined in 3 separate csv files) were available but many of those observations had N/As or zeroes, meaning we could not interpret whether or not that data point was accurate. Also, when merging based on the start-up name, many were only listed in one csv file so we lost many observations during the merge process. Consistent Data: The data was inconsistent across the variables we wanted to include in our model. For example, we had many N/As. We also ended up having to remove observations because of formatting issues and many were non US. For example money was represented in different units leading to a major consistency issue.
  9. 9. Models | Accuracy Michelle Villagra and Victoria Young | MIT Sloan Baseline Data Log .714 .728 CART .714 .877 Random Forest .714 .755 Clusters N/A N/A We ran a logistic regression, CART, Random Forest, and clustering in order to look for the best model for identifying predictive signals. Because our number of observations became very limited after we merged different data sets, the accuracy for our random forest was lower than expected. Ultimately our CART performed best.
  10. 10. Models | Results Michelle Villagra and Victoria Young | MIT Sloan Based on our dataset, the CART model was best in overall accuracy. The tree produced by the CART model lets us prioritize the factors that are most important as predictive signals of a startup’s success, setting a benchmark of $12M in funding raised as being predictive of an above average acquisition amount with an 87.7% accuracy.
  11. 11. Models | Results Michelle Villagra and Victoria Young | MIT Sloan Cluster 1: Relatively young companies, funded around 2009 and raised ~$5M and acquired within 8 years of founding. Cluster 2: Younger companies, funded around 2009 and raised ~$7M and acquired within 7 years of founding. Cluster 3: Older companies, funded around 2008 and raised ~$13.5M and acquired within 12 years of founding and with $63M in short term assets.
  12. 12. Data | Visualizations Michelle Villagra and Victoria Young | MIT Sloan
  13. 13. Data | Visualizations Michelle Villagra and Victoria Young | MIT Sloan
  14. 14. Data | Visualizations Michelle Villagra and Victoria Young | MIT Sloan
  15. 15. Moving Forward | Next Steps Michelle Villagra and Victoria Young | MIT Sloan Model Testing & Optimization: Now that we have reached some baseline metrics and significance in accuracy, we need to continue testing the model to optimize it and incorporate newly available variables over time by getting access to more valid observations as well as incorporate new variables into our models to test for significance. Analysis To Action: In order to make any analysis actionable, we would need to conduct additional research by expanding the amount of observations, adding additional variables to test for significance including revenue, number of customers, App Annie download data, key investors, year over year growth, etc.
  16. 16. THANK YOU! MIT Sloan School of Management Michelle Villagra and Victoria Young

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