Can startups be designed with algorithms?

Working Paper (coming soon)

(with Rembrand Koning)

Prediction tasks are fundamental to startup team design. To create a high-performance team, founders, incubators and venture capitalists must use available information about potential team members to make predictions about how a new team might perform. Presently, team design in such contexts relies on limited information and simple heuristics. We introduce and evaluate a machine learning based framework for data-driven team design. Our empirical evaluation leverages data from the `Innovate Delhi’ bootcamp and field experiment. The structure of the experiment, which collected detailed data on 112 individuals and the performance of 117 teams in which they worked over three weeks, provides an opportunity to evaluate the proposed framework. We find that out-of-sample predictions can explain between 10% to 15% of the variation in the actual performance of teams. The best performing models use `social’ information about team members, specifically, their social networks and evaluations by peers on previous teams.

Experimentation and startup performance: The case of A/B testing

Working PaperĀ  (coming soon)

(with Rembrand Koning and Aaron Chatterji)

Recent work argues that experimentation is the appropriate framework for entrepreneurial strategy. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of experimentally testing business ideas. This paper provides the first evidence of how digital experimentation affects the performance of a large sample of high-technology startups using data that tracks their growth, technology use, and product launches. We find that, despite its prominence in the business press, relatively few firms have adopted A/B testing. However, among those that do, we find increased performance on several critical dimensions, including page views and new product introductions. Furthermore, A/B testing is positively related to tail outcomes, with younger ventures failing faster and older firms being more likely to scale. Firms with experienced managers derive more benefits from A/B testing. Our results inform the emerging literature on entrepreneurial strategy and how digitization and data-driven decision-making are shaping strategy.