A Theory of Factors Affecting Continuous Experimentation (FACE)

TL;DR

This paper presents FACE, a theory identifying key organizational and product factors influencing the success of continuous experimentation in companies, based on empirical case studies.

Contribution

It introduces a novel theory of factors affecting CE, derived from multi-case studies, to help organizations assess and improve their experimentation practices.

Findings

Processes and infrastructure impact CE effectiveness

User problem complexity influences CE success

Incentive structures affect CE adoption and outcomes

Abstract

Continuous experimentation (CE) is used by many companies with internet-facing products to improve their software based on user data. Some companies deliberately adopt an experiment-driven approach to software development while some companies use CE in a more ad-hoc fashion. The goal of the study is to identify factors that explain the variations in the utility and efficacy of CE between different companies. We conducted a multi-case study of 12 companies involved with CE and performed 27 interviewees with practitioners at these companies. Based on that empirical data, we then built a theory of factors at play in CE. We introduce a theory of Factors Affecting Continuous Experimentation (FACE). The theory includes three factors, namely 1) processes and infrastructure for CE, 2) the user problem complexity of the product offering, and 3) incentive structures for CE. It explains how these factors affect the effectiveness of CE and its ability to achieve problem-solution and product-market fit. Our theory can be used by practitioners to assess an organisation's potential for adopting CE, as well as, identifying factors which pose challenges in gaining value from CE practices. Our results also provide a starting point for further research on how contextual factors affect CE and how these may be mitigated.