product-market fit analysis

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 the challenge of identifying product substitutability in retail settings. We propose a direct, statistically grounded measure based on co-purchase behavior—specifically, pairwise association metrics (e.g., phi coefficient or lift) derived from co-occurrence frequency matrices—bypassing indirect modeling paradigms rooted in utility theory or embedding-based representations. Our approach ensures computational efficiency, semantic transparency, and strong interpretability. By integrating hierarchical clustering, it enables unsupervised discovery of compact, highly cohesive substitute clusters. Experiments on real-world data from a pharmacy-retail chain demonstrate that our method significantly outperforms baseline approaches and effectively discriminates substitutability from complementarity. The resulting quantifiable, actionable insights support practical applications in category management and shelf-space optimization.

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.

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.

Small and medium-sized enterprises (SMEs) lack the resources and technical expertise to deploy conventional media-mix modeling (MMM), particularly amid tightening privacy regulations that exacerbate attribution challenges. Method: This paper introduces Robyn—a novel, open-source m/MMM framework designed specifically for SME advertisers. It features a modular, “plug-and-play” architecture integrating Bayesian time-series modeling (via PyMC/Stan), automated hyperparameter optimization, Adstock response modeling, and a scalable Python implementation—ensuring both interpretability and organizational deployability. Contribution/Results: Robyn systematically addresses data sparsity, prior bias, and cross-functional collaboration barriers. It reduces modeling turnaround from weeks to hours, enabling multi-channel attribution and budget allocation optimization. The framework has been deployed at scale across over 1,000 SMEs and within Meta’s advertising ecosystem, undergoing continuous iteration and real-world validation.

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.

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.

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.

This work addresses the limited capacity of existing online recruitment platforms to analyze multidimensional attribute relationships and hierarchical market structures. We propose an interactive visual analytics system designed for both job seekers and HR professionals, which, through a coordinated multiple-view design, enables the first integrated exploration of cross-hierarchical, multidimensional hiring data—spanning from macro-level industry trends to micro-level emerging roles. The system integrates data aggregation, pattern recognition, and multi-scale visualization techniques to support comprehensive labor market analysis. Case studies demonstrate its effectiveness in revealing regional salary distributions, characterizing industry evolution trajectories, and accurately identifying high-demand emerging positions.

This study addresses severe congestion in the interview stage of academic statistics faculty hiring, which obscures candidates’ true preferences and leads to mismatches and unfilled positions. The authors model interview assignment as a statistical ranking problem under uncertainty, innovatively integrating market design with statistical learning. Candidates report their preferences over job attributes via a standardized questionnaire, while departments estimate offer probabilities and expected utilities using application materials and historical data. A confidence-calibrated pairwise utility ranking mechanism is then employed to select interviewees. This mechanism provides statistical guarantees, incentivizes truthful preference revelation, and enhances matching stability. Empirical evaluation using data from U.S. statistics departments demonstrates that the proposed framework substantially improves both match rates and quality while significantly reducing hiring failure rates.