Accurately learning what customers value is critical for the success of every company. Despite the extensive research on identifying customer preferences, only a handful of software companies succeed in becoming data-driven at a scale that they aim for. Benefiting from novel approaches such as experimentation on top of the traditional feedback collection is very challenging, yet tremendously impactful when performed correctly.
In this thesis, we explore how software companies evolve from data collectors with ad-hoc benefits, to trustworthy data-driven decision makers at scale. We base our work on a 3.5 year longitudinal multiple-case study research with companies working in both embedded systems domain (e.g. engineering connected vehicles, surveillance services) as well as in the online domain (e.g. developing search engines, mobile applications).
The contribution of this thesis is twofold. First, we present how software companies learn from customers. Second, we show how they adopt and evolve experimentation in order to improve their data-driven capabilities.
With our work, we wish to empower software companies to become data-driven at scale by using the experience of companies that succeeded in this. Ultimately this should lead to better software products and services.