From Features to Value: A Metric Planner for Linking UX and Business Outcomes

Abstract

Evaluating the impact of product features on user experience remains a challenging task, particularly when metrics are defined late, lack alignment with UX concerns, or are weakly connected to specific features. Although several quality models and metric frameworks have been proposed, few approaches support the systematic operationalization of metric planning during development, limiting practitioners’ ability to define actionable and well-aligned measurement strategies. This paper presents the Feature Metric Planner, a web-based interactive tool designed to operationalize feature-level metric planning. The tool enables the automatic generation of structured measurement plans from feature descriptions by leveraging a Large Language Model (LLM). These plans include suggested metrics, measurement tools, timing strategies, and explicit rationales, and are presented using a markdown-based format to support readability and integration into development workflows. An exploratory qualitative evaluation with six practitioners from research and industry indicates that the tool is perceived as useful for structuring metric planning early in the development process, improving alignment between feature design and UX-related metrics, and reducing the effort required to define measurement strategies. Participants particularly emphasized the clarity of the generated outputs and the usefulness of distinguishing between leading and lagging indicators at the feature level. Rather than establishing a validated measure of return on investment, the contribution of this work lies in operationalizing the structure of feature-level metric planning and in surfacing the design tensions that emerge when doing so. This work contributes (i) a feature-oriented approach to metric planning that structures, rather than proves, the link between UX design decisions and business outcomes, (ii) a functional tool that supports practitioners in defining structured measurement strategies, (iii) empirical insights into its perceived usefulness in real-world contexts, and (iv) a characterization of the design tensions—interpretability, contextual flexibility, and ethical accountability—that arise when LLMs are used to support human-centered metric planning.