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  • Harvard Data Science Review

Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide

By: Iavor Bojinov and Somit Gupta
  • Format:Electronic
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Abstract

In the past decade, online controlled experimentation, or A/B testing, at scale has proved to be a significant driver of business innovation. The practice was first pioneered by the technology sector and, more recently, has been adopted by traditional companies undergoing a digital transformation. This article provides a primer to business leaders, data scientists, and academic researchers on business experimentation at scale, explaining the benefits, challenges (both operational and methodological), and best practices in creating and scaling an experimentation-driven, decision-making culture.

Keywords

A/B Testing; Experimentation; Data-driven Culture; Product Development; Innovation and Invention; Digital Transformation

Citation

Bojinov, Iavor, and Somit Gupta. "Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide." Harvard Data Science Review, no. 4.3 (Summer, 2022).
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About The Author

Iavor I. Bojinov

Technology and Operations Management
→More Publications

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    Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation

    By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor I. Bojinov
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    Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development

    By: Daniel Yue, Paul Hamilton and Iavor Bojinov
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    On Ramp to Crypto

    By: Iavor Bojinov, Michael Parzen and Paul Hamilton
More from the Authors
  • Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor I. Bojinov
  • Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development By: Daniel Yue, Paul Hamilton and Iavor Bojinov
  • On Ramp to Crypto By: Iavor Bojinov, Michael Parzen and Paul Hamilton
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