Simulating survival and profitability of startups
Despite today’s digitization hype, most problems rather reflect small data problems than big data problems. In many contexts, data collection efforts are still costly or even impossible. A prime example is early stage investing in startups. Almost all data that would be relevant for applying AI is kept private by business angels that are reluctant to open their data vaults out of fear to lose their competitive edge. And even if they do so, available data is often little and of low quality. Simulations may help in such circumstances by showing a novel approach in which we simulated survival and profitability of early-stage startups that on average outperform professional human investors.
Here are some startups or groups working on that subject:
Prof. Dr. Ivo Blohm is Assistant Professor for Data Science and Management at the Institute for Information Management at the University of St. Gallen. How to apply AI for small data problems? Simulating survival and profitability of startups.
We investigate whether digital traces can be used to predict early stage startup survival. Based on common survival factors from the entrepreneurship literature, we mined the digital footprints of 542 entrepreneurs and their ventures. Using a context-specific text mining approach, we performed a bootstrapping simulation in which we predict 5-year survival for different survival rates that range from 50% to 10%. Our results indicate that we can predict 5-year survival with an accuracy of up to 91%. With this study, we will provide an evidence-based taxonomy of digital traces for predicting early stage startup survival, identify the most important digital traces for doing so and benchmark our predictive approach against the actual investments of 339 business angels.
Predicting the success of a startup company , Vrushank Shah, Oklahoma State University; Dr Miriam Mc gaugh, Oklahoma State University
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