customer experience

Practitioners face significant challenges in effectively transforming customer feedback data into actionable software improvements. Method: This study proposes an end-to-end, data-driven improvement framework that systematically integrates feedback collection, multidimensional metric design, descriptive and inferential statistical analysis, interactive visualization dashboards (UX prototypes), and cross-departmental change-enabling mechanisms. Contribution/Results: The framework’s key innovation lies in the deep integration of statistical inference with user experience design, enabling a closed-loop feedback system for real-time insight generation and collaborative decision-making. Empirical evaluation demonstrates substantial improvements in feedback processing efficiency and response accuracy; product teams can rapidly identify high-priority enhancement opportunities using evidence-based insights. The results validate both the feasibility and practical efficacy of data-driven software evolution in industrial settings.

This study investigates the causal mechanisms through which UX design influences mobile app user retention and paid conversion. Leveraging a mixed-methods approach—including systematic literature review, large-scale behavioral modeling, statistical inference, and iterative A/B testing—we rigorously validate the impact of core UX levers: intuitive navigation, visual consistency, performance optimization, and closed-loop feedback. Crucially, we quantify, for the first time, the marginal commercial contribution of personalized UX interventions. We introduce an interpretable, causally grounded UX–business metric association model that elucidates design-to-behavior pathways. Empirical results demonstrate that optimized UX increases 7-day retention by 23% on average and boosts paid conversion by 18%. The work delivers a reusable, measurable, and attribution-aware design decision framework for UX-driven product iteration, bridging the gap between human-centered design and quantifiable business outcomes.

This paper addresses two key UX challenges in B2B customer segmentation: (1) sales experts’ difficulty interpreting unsupervised clustering outputs, and (2) the absence of effective human-AI collaborative explanation mechanisms. Targeting global manufacturing enterprises, we propose a domain-expert cognitive-load-driven interactive machine learning (IML) explainability paradigm. To our knowledge, this is the first work to deeply integrate IML into industrial-scale B2B segmentation—combining K-means and DBSCAN clustering, multi-source business data integration, and an expert feedback–driven closed-loop evaluation framework. Our interactive prototype significantly improves experts’ comprehension efficiency and trust in model outputs, achieving 92% acceptance among sales professionals. Furthermore, we distill a reusable, industrial-grade IML UX design guideline. This work provides both a methodological foundation and a practical exemplar for trustworthy deployment of unsupervised ML in real-world B2B settings.

Large global software enterprises (e.g., Dell Technologies) face persistent challenges in continuously integrating customer feedback within Agile and DevOps practices, resulting in requirement misalignment and delivery deviations. To address this, this study proposes and empirically validates a customer feedback closed-loop integration model grounded in action research. For the first time, the model systematically connects demand validation, feedback analysis, and release decision-making across an organization exceeding 10,000 employees, integrating Agile/DevOps principles with multidimensional metrics—including defect escape rate, deployment frequency, product adoption rate, and customer satisfaction (CSAT). Empirical evaluation demonstrates a 40% reduction in customer feedback response cycle time, a 35% increase in critical product deployment frequency, and statistically significant improvements in defect interception rate and CSAT. This work contributes a scalable, reusable methodology and empirical evidence for customer-centric continuous delivery in large-scale software organizations.

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

This study addresses the challenge faced by resource-constrained software startups lacking user experience (UX) expertise in efficiently developing user-centered minimum viable product (MVP) prototypes. To bridge this gap, the authors propose StartFlow, a lightweight method that uniquely integrates wireframes and user flows into a unified “wireflow” representation. StartFlow guides non-UX teams through a structured three-step process—feature organization, prototype construction, and closed-loop validation based on usability heuristics—to iteratively refine MVPs. Empirical results demonstrate that teams employing StartFlow produce prototypes that are clearer, better aligned with user stories and business rules, and exhibit significantly fewer usability flaws. Expert evaluations further confirm the method’s high usability and strong potential for broad adoption in early-stage software development contexts.

As AI agents become deeply integrated into core enterprise workflows, designing effective human-AI interaction to enhance user experience, foster adoption, and support user-centered decision-making has emerged as a critical challenge. This study addresses this issue through a mixed-methods approach, combining qualitative interviews and quantitative experiments to systematically investigate interaction patterns between humans and AI agents in business contexts and identify key design elements that shape user experience. Grounded in empirical findings, the work proposes a set of human-AI interaction design guidelines tailored for commercial environments, along with a quantifiable evaluation framework. These contributions offer both theoretical grounding and practical guidance for development teams seeking to optimize and deploy large-scale human-AI collaborative systems.

This study addresses the challenges UX designers face in data visualization due to limited domain knowledge and tool expertise. It presents the first systematic comparison of three guidance approaches—static presentation, scrollytelling, and chatbot-based interaction—in authentic design tasks, proposing three core design dimensions for effective visualization guidance: narrative structure, visual content layout, and navigation flexibility. Through a controlled experiment, surveys, and in-depth interviews with 25 UX designers and students, the research evaluates both performance and user experience. Findings indicate that interactive guidance (either scrollytelling or chatbot) significantly enhances adherence to design conventions and user engagement, while also providing clearer instructions compared to static guidance. However, no significant differences emerged between the two interactive modalities, and all groups demonstrated comparable levels of visualization comprehension.

This study addresses the mismatch between assumed and actual user reading behaviors in dashboard design, where existing approaches often presume a fixed component viewing order. Through a mixed-methods investigation involving 18 designers and 16 users, the work systematically identifies and quantifies six key factors influencing interaction sequences: layout, visual salience, semantics, functional role, interactivity, and user context. Integrating interviews, behavioral logs, and sequential analysis, the research uncovers both consistent patterns and diverse strategies in how users navigate dashboards, revealing representative navigation pathways. These findings provide an empirical foundation and actionable design insights for computational modeling of dashboard comprehension and the development of intelligent guidance systems that adapt to real user behavior.

This work addresses the challenge that open-source software developers often struggle to empathize with users due to a lack of contextual background, while existing issue-tracking tools prioritize technical details over user perspectives. To bridge this gap, the authors propose PersonaFlow—a lightweight, extensible tool that automatically generates editable user personas from repository artifacts such as issues and pull requests, seamlessly integrating them into the issue-reporting interface. A user study (N=13) demonstrates that PersonaFlow effectively fosters user-centered developer behaviors: most participants revised their understanding of reported issues, and more than half proactively incorporated empathetic language, tailored explanations, or elevated issue priority in their responses. These findings validate the efficacy of simultaneously supporting affective connection and pragmatic decision-making in developer workflows.