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Today, connected software-intensive products permeate virtually every aspect of our lives and the amount of customer and product data that is collected by companies across domains is exploding. In revealing what products we use, when and how we use them and how the product performs, this data has the potential to help companies optimize existing products, prioritize among features and evaluate new innovations. However, despite advanced data collection and analysis techniques, companies struggle with how to effectively extract value from the data they collect and they experience difficulties in defining what values to optimize for. As a result, the impact of data is low and companies run the risk of sub-optimization due to misalignment of the values they optimize for. In this paper, and based on multi-case study research in embedded systems and online companies, we explore data collection and analysis practices in companies in the embedded systems and in the online domain. In particular, we look into how the value that is delivered to customers can be expressed as a value function that combines different factors that are of importance to customers. By expressing customer value as a value function, companies have the opportunity to increase their awareness of key value factors and they can establish an agreement on what to optimize for. Based on our findings, we see that companies in the embedded systems domain suffer from vague and confusing value functions while companies in the online domain use simple and straightforward value functions to inform development. Ideally, and as proposed in this paper, companies should strive for a comprehensive value function that includes all relevant factors without being vague or too simple as is the case in the companies we studied. To achieve this, and to address the difficulties many companies experience, we present a systematic approach to value modelling in which we provide detailed guidance for how to quantify feature value in such a way that it can be systematically validated over time to help avoid sub-optimization that will harm the company in the long run. |
