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 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.

This study addresses the practical challenge of aligning cross-functional teams around dual objectives: enhancing user value and driving business growth. We propose a hybrid methodology integrating the User Experience Research (UXR) Point-of-View (POV) pyramid with growth hacking practices, synthesizing qualitative insights (e.g., contextual interviews, ethnographic observation) and quantitative analytics (e.g., behavioral log analysis, A/B testing, data science modeling) into a reusable collaborative decision-making framework. Our key contribution is the first systematic integration of UXR’s strategic-level POV with the growth funnel metrics framework, enabling empirical alignment and validation between user needs and business goals. Deployed in a product serving over one million users, the approach guided core feature iterations and yielded a 18% improvement in key UX metrics, alongside a 12% increase in DAU and a 9.5% uplift in conversion rate—demonstrating a positive coupling mechanism between user value creation and commercial growth.

Traditional statistical methods struggle to uncover the underlying motivations behind tourist behavior and interest evolution within attraction networks. Method: We propose the first integrated analytical framework that jointly models semantic features (via LDA topic modeling of textual digital footprints) and structural features (via visit-sequence graph modeling), enhanced by graph neural networks (GNNs) and multi-source trajectory clustering. This enables interpretable tourist interest profiling and cross-city mobility mapping. Contribution/Results: Evaluated on real-world tourism datasets, our approach achieves a 23.6% improvement in interest cluster identification accuracy. It significantly advances understanding of how tourist preferences form and shift over time and space. By bridging semantic intent with spatiotemporal behavioral structure, the framework establishes a novel paradigm for intelligent tourism recommendation and collaborative destination management.

HCI has long evaluated qualitative research through a positivist lens, overemphasizing quantifiable metrics and neglecting its interpretive nature. Method: Drawing on epistemological critique, this paper systematically distinguishes positivist and interpretivist paradigms, exposing the fundamental limitations of quantification in understanding human behavior. It then proposes the first non-quantitative quality assessment framework specifically designed for HCI qualitative research. Contribution/Results: The framework introduces five interpretivist quality criteria—credibility, transferability, dependability, confirmability, and resonance—grounded in qualitative logic rather than numerical standards to ensure rigor and contextual appropriateness. It shifts evaluation away from positivist assumptions toward interpretivist principles, enhancing methodological fidelity to qualitative inquiry. The framework has been preliminarily adopted in the ACM Transactions on Computer-Human Interaction (TOCHI) and CHI conference review guidelines, marking a substantive step toward an interpretivist reorientation in HCI methodology.

This study addresses the challenge of efficiently analyzing large-scale, open-ended user feedback, which hinders effective user experience evaluation and product iteration. The authors propose a novel approach that integrates multi-label classification with generative artificial intelligence (GenAI) to automatically perform topic labeling, summarization, and sentiment analysis of user comments. This work represents the first effort to synergistically combine multi-label supervised learning with GenAI for user feedback analysis. Importantly, the findings reveal that sentiment analysis alone is insufficient as a proxy for explicit satisfaction measurement. By enabling scalable and fine-grained insights into user experience, the proposed method offers a robust technical pathway for practitioners and researchers seeking to derive actionable intelligence from unstructured feedback data.

Existing computational tools for qualitative data analysis often fall short in effectively supporting causal exploration due to insufficient contextual awareness, limited trustworthiness, or overly complex outputs. To address these limitations, this work proposes QualCausal, the first interactive causal analysis system grounded in user research–driven design principles. Developed through formative user studies, QualCausal integrates context-aware processing, cognitive scaffolding, and explainability mechanisms to facilitate efficient exploration and validation of causal hypotheses within qualitative datasets. The system enables researchers to extract causal relationships, construct interactive causal networks, and examine findings through coordinated multi-view visualizations. User evaluations demonstrate that QualCausal significantly reduces analytical burden, provides robust cognitive support, and prompts critical reflection on how computational tools can be meaningfully integrated into social science research practices, thereby bridging the gap between computational assistance and qualitative inquiry paradigms.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

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 challenge of translating user research (UXR) data into strategically impactful insights within complex developer tooling contexts—such as AI agents, command-line interfaces, and error messaging—where traditional approaches often fall short. To bridge this gap, the work proposes a mixed-methods research framework that triangulates qualitative and quantitative data to produce high-confidence findings. Central to this approach are three structured “playbook cards”—Paradigm Shift, Explainability as Trust, and Friction Cost—that transform technical observations into compelling, irrefutable business narratives. By operationalizing a reusable pipeline from raw insight generation to strategic viewpoint formulation, this framework significantly enhances the influence and persuasive power of UXR in technology product decision-making.