Companies are continuously improving their practices and ways of working in order to fulfill always-changing market requirements. As an example of building a better understanding of their customers, organizations are collecting user feedback and trying to direct their R&D efforts by e.g. continuing to develop features that deliver value to the customer. We (1) develop an actionable technique that practitioners in organizations can use to validate feature value early in the development cycle, (2) validate if and when the expected value reflects on the customers, (3) know when to stop developing it, and (4) identity unexpected business value early during development and redirect R&D effort to capture this value. The technique has been validated in three experiments in two cases companies. Our findings show that predicting value for features under development helps product management in large organizations to correctly re-prioritize R&D investments.

With agile teams becoming increasingly multi-disciplinary and including all functions, the role of customer feedback is gaining momentum. Today, companies collect feedback directly from customers, as well as indirectly from their products. As a result, companies face a situation in which the amount of data from which they can learn about their customers is larger than ever before. In previous studies, the collection of data is often identified as challenging. However, and as illustrated in our research, the challenge is not the collection of data but rather how to share this data among people in order to make effective use of it. In this paper, and based on case study research in three large software-intensive companies, we (1) provide empirical evidence that ‘lack of sharing’ is the primary reason for insufficient use of customer and product data, and (2) develop a model in which we identify what data is collected, by whom data is collected and in what development phases it is used. In particular, the model depicts critical hand-overs where certain types of data get lost, as well as the implications associated with this. We conclude that companies benefit from a very limited part of the data they collect, and that lack of sharing of data drives inaccurate assumptions of what constitutes customer value.

With continuous deployment of software functionality, a constant flow of new features to products is enabled. Although new functionality has potential to deliver improvements and possibilities that were previously not available, it does not necessary generate business value. On the contrary, with fast and increasing system complexity that is associated with high operational costs, more waste than value risks to be created. Validating how much value a feature actually delivers, project how this value will change over time, and know when to remove the feature from the product are the challenges large software companies increasingly experience today. We propose and study the concept of a software feature lifecycle from a value point of view, i.e. how companies track feature value throughout the feature lifecycle. The contribution of this paper is a model that illustrates how to determine (1) when to add the feature to a product, (2) how to track and (3) project the value of the feature during the lifecycle, and how to (4) identify when a feature is obsolete and should be removed from the product.

Once supporting the electrical and mechanical functionality, software today became the main competitive advantage in products. However, in the companies that we study, the way in which software features are developed still reflects the traditional ‘requirements over the wall’ approach. As a consequence, individual departments prioritize what they believe is the most important and are unable to identify which features are regularly used – ‘flow’, there to be bought – ‘wow’, differentiating and that add value to customers, or which are regarded commodity. In this paper, and based on case study research in three large software-intensive companies, we (1) provide empirical evidence that companies do not distinguish between different types of features, which causes poor allocation of R&D efforts and suppresses innovation, and (2) develop a model in which we depict the activities for differentiating and working with different types of features and stakeholders.

Background: Customer feedback is critical for successful product development. Software companies continuously collect it in order to become more data-driven. By understanding how these feedback data are collected, companies’ ability to accumulate and synthesize the learnings, and correctly prioritize product development decisions increases. Objective: The purpose of this study is to (1) provide an overview of the sources and feedback collection techniques, (2) demonstrate the impact that customer and product data have on product development, and (3) provide the open research challenges on this topic. Method: We performed a systematic literature review of customer feedback and data collection techniques, analyzing 71 papers on the subject taken from a gross collection of 1298.  Results: We (1) identify the different customer feedback techniques and sources where these data originate and summarize them in the “Customer Feedback Model”. Next, we show the (2) impact that the customer feedback has on the overall development process. Finally, we (3) conclude with future research challenges. Conclusions: Our research reveals a compelling set of feedback data collection techniques that can be used throughout the development stages of software products. The identified challenges, however, indicate that the use of feedback today is fragmented and with limited tool support.