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Technical note
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Reference no. SIN-0057-E
Published by: IESE Business School
Originally published in: 2019
Version: 14 February 2019
Revision date: 11-Nov-2019
Length: 8 pages
Data source: Generalised experience

Abstract

When addressing customer needs through new offerings, it is essential to carry out hypothesis-driven experimentation and develop the kind of products that consumers really want. Often what you think customers want diverges substantially from what customers actually want. This technical note introduces and explains the essentials of hypothesis-driven experimentation, which is predicated on the idea of systematically and iteratively reducing uncertainty about the relevant customer problems and the most effective solutions to these problems. The goal of this technical note is to explain the role of experimentation and constant learning in the development and management of products and services in the digital age. Each step is explained thoroughly: from gathering assumptions, translating these into hypotheses, testing them with various quantitative methods and finally synthesizing the lessons learned. Principles for putting hypothesis-driven experimentation into practice are introduced.

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Abstract

When addressing customer needs through new offerings, it is essential to carry out hypothesis-driven experimentation and develop the kind of products that consumers really want. Often what you think customers want diverges substantially from what customers actually want. This technical note introduces and explains the essentials of hypothesis-driven experimentation, which is predicated on the idea of systematically and iteratively reducing uncertainty about the relevant customer problems and the most effective solutions to these problems. The goal of this technical note is to explain the role of experimentation and constant learning in the development and management of products and services in the digital age. Each step is explained thoroughly: from gathering assumptions, translating these into hypotheses, testing them with various quantitative methods and finally synthesizing the lessons learned. Principles for putting hypothesis-driven experimentation into practice are introduced.

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