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Experimentation and Start-up Performance: Evidence from A/B Testing

Author

Listed:
  • Rembrand Koning

    (Harvard Business School, Harvard University, Boston, Massachusetts 02163)

  • Sharique Hasan

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Aaron Chatterji

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708; National Bureau of Economic Research, Cambridge, Massachusetts 02138)

Abstract

Recent scholarship argues that experimentation should be the organizing principle for entrepreneurial strategy. Experimentation leads to organizational learning, which drives improvements in firm performance. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of testing business ideas. Our results provide the first evidence on how digital experimentation affects a large sample of high-technology start-ups using data that tracks their growth, technology use, and products. We find that, although relatively few firms adopt A/B testing, among those that do, performance improves by 30%–100% after a year of use. We then argue that this substantial effect and relatively low adoption rate arises because start-ups do not only test one-off incremental changes, but also use A/B testing as part of a broader strategy of experimentation. Qualitative insights and additional quantitative analyses show that experimentation improves organizational learning, which helps start-ups develop more new products, identify and scale promising ideas, and fail faster when they receive negative signals. These findings inform the literatures on entrepreneurial strategy, organizational learning, and data-driven decision making.

Suggested Citation

  • Rembrand Koning & Sharique Hasan & Aaron Chatterji, 2022. "Experimentation and Start-up Performance: Evidence from A/B Testing," Management Science, INFORMS, vol. 68(9), pages 6434-6453, September.
  • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:9:p:6434-6453
    DOI: 10.1287/mnsc.2021.4209
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    References listed on IDEAS

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    2. Hunt, Richard A. & Townsend, David M. & Lerner, Daniel A. & Brownell, Katrina M., 2024. "Pivot, persist or perish? Knowledge problems and the extraordinarily tight boundary conditions of entrepreneurs as scientists," Journal of Business Venturing Insights, Elsevier, vol. 21(C).
    3. Felipe A. Csaszar & Harsh Ketkar & Hyunjin Kim, 2024. "Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors," Papers 2408.08811, arXiv.org.
    4. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Oct 2024.

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