While innovation, such as development of new features, is critical for any organization, it is hard to get right. In both our case companies, the selection of ideas is usually driven by previous experiences, and very often the process becomes politicized and based on peoples’ opinions. To address this, we present the Hypothesis Experiment Data-Driven Development (HYPEX) model. Our model is an alternative development process that helps companies shorten the feedback loop to customers. The model supports companies in running feature experiments and advocates development of small parts of features that are continuously evaluated with customers. In our study we validate the model in two software development companies. Although the companies involved in the study have not yet completed a full experiment cycle, we see that feature experiments are beneficial for improving at least four activities within the companies: (1) data-driven development (the ease of collecting customer feedback allows for a real-time connection between the quantified business goals of the organization and the operational metrics collected from the installed customer base), (2) customer responsiveness (the ease of collecting customer feedback allows product management to respond rapidly and dynamically to any changes to the use of the products, as well as to emerging customer requests), (3) R&D efficiency (the ease of collecting customer feedback gives the development teams a real-time goal and metrics to strive for and provides focus for their work), and (4) R&D accuracy (the ease of collecting customer feedback enables the development teams to align their efforts with what the customers appreciate the most). The HYPEX model is a development process that helps software development companies move away from building large chunks of functionality with little feedback from customers and instead continuously validate with customers that the functionality under development is of value to customers.