market research

This study addresses the challenge of extracting business-interpretable item association rules from retail transaction data to support precision marketing, shelf-space optimization, and inventory management. To bridge the gap between statistical discoverability and operational actionability, we propose a novel rule filtering and prioritization framework that jointly considers statistical significance (via support, confidence, and lift) and managerial feasibility (through domain-specific semantic mapping). Our method integrates Apriori and FP-Growth algorithms, incorporates a three-dimensional rule evaluation scheme, and enables interactive rule visualization. Evaluated on a real-world supermarket dataset, the framework identified 327 high-value, actionable association rules. Deployment yielded an 18.6% increase in cross-buying rate and a 22.3% improvement in promotional response rate, empirically validating its practical effectiveness and scalability for retail analytics.

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.

Traditional questionnaires struggle to simultaneously capture qualitative depth and quantitative structure, limiting comprehensive understanding of complex social phenomena. This study proposes a dynamic survey platform powered by large language models (LLMs) that, for the first time, enables real-time semantic clustering of open-ended responses. Through an interactive feedback mechanism, users can rate, rank, and reflect on these clusters, generating visual reports that integrate qualitative insights with quantitative analysis. Innovatively embedding LLMs within a closed-loop data collection framework, the approach facilitates dynamic comparisons between individual perspectives and group-level trends. Empirical validation across two field studies involving 93 participants demonstrates that the platform significantly enhances data richness and user engagement compared to conventional survey tools, while effectively fostering collaborative sensemaking.

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 addresses the limitations of traditional B2B customer segmentation approaches, such as the RFM model, which rely on singular metrics and struggle to capture the complexity and dynamics of business interactions. To overcome this, the authors propose a dynamic, multi-criteria segmentation framework that extends RFM by incorporating stability and growth dimensions. The framework aligns with strategic business objectives through an adaptive Analytic Hierarchy Process (AHP) and integrates multivariate time series clustering with a graph consensus model to enable temporal segmentation. Evaluated on data from over 3,000 manufacturing enterprises, the approach demonstrates strong temporal robustness and significantly enhances the precision of customer strategy formulation through preference-driven dynamic clustering.

Contemporary BI dashboards lack a structured, iterative optimization framework, hindering their evolution from exploratory tools to robust decision-support systems. Method: This study proposes a feedback-driven, gap-analysis–informed four-stage iterative methodology, integrating a six-element data narrative framework—encompassing goals, context, insights, evidence, actions, and impact—and implements it in Power BI via DAX metric optimization and collaborative peer review. Contribution/Results: The framework demonstrably enhances narrative coherence and explanatory power. Empirical application uncovered critical issues: significantly lower gross margin for furniture (6.94% vs. 13.99% for technology), profitability erosion beyond a 20% discount threshold, and $1.35M in unrecovered freight costs—substantially improving decision accuracy. This work makes the first contribution of embedding structured narrative design directly into the BI dashboard iteration lifecycle, yielding a reusable, methodologically grounded framework.

Process mining has increasingly emphasized technical dimensions while neglecting human and organizational factors, leading to a growing disconnect between analytical insights and practical implementation. Method: Grounded in a sociotechnical perspective, this paper proposes “process analytics” as a novel paradigm, developing a multidimensional framework that integrates analytical processes, organizational context, and stakeholder engagement. Through an inductive–deductive conceptual modeling approach, the framework is theoretically validated and contextualized using real-world enterprise cases. Contribution/Results: This work provides the first explicit, structured definition of process analytics, overcoming traditional process mining’s algorithmic bias and governance neglect. It emphasizes the co-evolution of analytical activities and organizational practices. The resulting scalable framework has been empirically validated in large-scale enterprise process automation initiatives, demonstrating both theoretical rigor and practical applicability.

Facing persistent declines in customer satisfaction among small- and medium-sized enterprises (SMEs) in the IT services sector—and intensifying competitive pressure from rivals delivering superior service at lower costs—this study proposes an integrated service quality assessment framework combining Lean Six Sigma and the SERVQUAL model. Methodologically, it systematically unifies the DMAIC methodology, SERVQUAL’s five-dimensional gap analysis, and Six Sigma’s data-driven statistical process control techniques to establish a quantifiable, traceable pathway for service improvement. Empirical validation demonstrates that the framework precisely identifies five root causes of customer dissatisfaction, increases customer satisfaction significantly, and reduces customer acquisition cost by 18.3%. This work bridges a critical methodological gap in service quality management: the absence of a quantitatively rigorous, end-to-end integrated approach—from diagnostic assessment to closed-loop optimization. It offers SME IT service providers a novel quality management paradigm that balances theoretical rigor with practical implementability.

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.

This study investigates the impact of service robots on customer behavior and staff interactions in a real-world retail setting, with a focus on key service metrics such as store entry conversion rates. Over a 12-day field experiment in a bedding store, three conditions—no robot, robot-only, and robot with a fixed installation—were alternately deployed. Employing an explanatory sequential mixed-methods design, the research integrates video-based behavioral annotation, service funnel quantification, and semi-structured employee interviews. Findings reveal that the robot significantly increases pedestrian stopping rates—especially when paired with a fixed installation—but concurrently suppresses staff engagement, store entry, and purchase conversion, uncovering a paradoxical “demand closure at the doorway” mechanism. The study concludes with actionable deployment strategies for service robots, offering both theoretical insights and practical guidance for high-touch service environments.