business and market analysis

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.

Current large language models (LLMs) exhibit dual limitations in enterprise competitive analysis: insufficient access to real-time commercial knowledge and inadequate multidimensional competitive cognition, leading to strategic decision bias. To address this, we propose a multidimensional business-element-guided framework specifically designed for competitive analysis. Our approach innovatively integrates interpretable business dimensions—such as market positioning, product competitiveness, and technological trends—explicitly into LLM reasoning. It combines prompt-engineering-driven multi-faceted cue injection, structured domain-knowledge alignment, and a dual-track evaluation mechanism integrating quantitative metrics and qualitative assessment. Empirical evaluation on real-world tasks demonstrates that our method improves key judgment accuracy by 23.6% and analytical consistency by 31.2% over baseline models, significantly enhancing the credibility and operational feasibility of strategic recommendations.

Conventional time-series models struggle to capture the underlying mechanisms governing market dynamics. Method: This paper proposes a mechanistic dynamical model grounded in ordinary differential equations (ODEs), explicitly embedding product competitiveness and consumer behavior into a nonlinear dynamical system. Inspired by ecological population interactions—such as predator–prey dynamics—the model unifies key market processes: market-share evolution, new-product adoption, technology refresh cycles, and product obsolescence. Contribution/Results: Unlike black-box autoregressive approaches, the proposed model achieves both strong interpretability and robust extrapolation capability. It significantly outperforms traditional statistical methods in both dynamic forecasting accuracy and depth of mechanistic insight. By formalizing market competition as a structured, differentiable dynamical system, it provides a testable theoretical framework for uncovering and validating the fundamental drivers of competitive market evolution.

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.

This paper addresses the insufficient characterization of nonlinear dynamics—particularly saturation effects—in advertising effectiveness modeling. We propose a unified framework integrating statistical physics and marketing dynamics, pioneering the application of phase-transition theory and symmetry principles to consumer behavior modeling. This yields a universal advertising response equation that extends beyond the applicability limits of classical Michaelis–Menten and Hill models. Through nonlinear dynamical systems analysis, scaling-law derivation, and parametric sensitivity analysis, the model accurately reproduces diverse empirical response curves. It quantitatively uncovers the synergistic interplay among marketing efficacy, response sensitivity, and behavioral sensitivity. The resulting framework provides an interpretable, generalizable theoretical foundation and quantitative tools for advertising budget allocation and strategy optimization.

This study addresses how platform imitation of successful third-party products in its own marketplace may undermine sellers’ incentives to innovate. The authors develop a Stackelberg game-theoretic model in which the platform, as the leader, makes strategic entry decisions, while sellers, as followers, optimize their exploration–exploitation trade-offs. In the single-seller setting, the optimal policy is derived using the Gittins index; in the multi-seller case, deep reinforcement learning is employed to analyze equilibrium behavior. This work presents the first integration of the Gittins index with deep reinforcement learning, systematically uncovering the incentive compatibility mechanism between platform entry strategies and seller innovation. The theoretical findings align with empirical observations from real-world platforms such as Amazon and Google Play, offering rigorous support for policies aimed at preserving market innovation and diversity.

This study addresses the challenge faced by online platforms in selecting products under capacity constraints, where a trade-off exists between exploring new items with unknown quality and maximizing short-term revenue. The authors propose a product selection model grounded in online learning, combinatorial bandit theory, and dynamic programming, incorporating social learning feedback. They show that the optimal policy exhibits a simple threshold structure: new items should always be displayed alongside top-performing products, and the number of items explored in parallel depends solely on the aggregate potential of new items, independent of their individual purchase probabilities. This strategy substantially reduces cumulative regret and reveals inherent limitations of standard approaches—UCB tends to over-explore, while Thompson Sampling often under-explores—thereby offering both theoretical insights and practical guidance for new product recommendation on digital platforms.

This study investigates whether a platform firm should integrate or maintain an independent brand following the acquisition of a same-side competitor in a two-sided market, using Uber’s acquisition of Postmates as a case study. Innovatively incorporating age–period–cohort (APC) decomposition into merger analysis, the authors combine large-scale consumer receipt data with a difference-in-differences (DiD) design and find that conventional DiD substantially underestimates the total effect of the merger. Results indicate a significant decline in Postmates user spending post-acquisition, with a partial shift to Uber Eats but larger reallocations to DoorDash and Grubhub. Maintaining Postmates as a distinct brand mitigates user attrition. Notably, low-engagement multi-homing users exhibit high behavioral stickiness and remain largely unaffected by the merger.

This study addresses the lack of systematic evaluation of large language models’ (LLMs’) economic decision-making and resource management capabilities, which are often overlooked in favor of semantic performance. To bridge this gap, the work proposes the first multi-agent supply chain simulation framework incorporating economic competition mechanisms, wherein LLMs assume the role of retailers operating under budget constraints. Agents participate in procurement auctions, dynamic pricing, and role-aware marketing slogan generation, with full transaction trajectories recorded. Evaluations across 20 open- and closed-source models employ multidimensional metrics encompassing economic profit, operational efficiency, and semantic quality. Results reveal that only a minority of models consistently achieve capital appreciation, while most—despite comparable semantic competence—fail to surpass breakeven, exhibiting a pronounced “winner-takes-all” dynamic that challenges conventional LLM evaluation paradigms.

This study examines how corporate social responsibility (CSR) activities influence consumer choice and product sales through corporate image enhancement, focusing on the South Korean instant noodle market. Method: We innovatively construct a media-text-based corporate favorability index and, for the first time, link it with CSR media exposure intensity; causal effects are identified via panel data regression. Contribution/Results: CSR-driven favorability improvements exert a statistically significant positive effect on brand sales, with a marginal effect equivalent to a ~60% increase in advertising expenditure. For Ottogi—a leading firm—CSR-related image enhancement translates into an average annual increase of 23.7 million units (+6.7%) in flagship product sales. The findings provide micro-level empirical evidence on the economic value of CSR, demonstrating its efficacy as a high-return brand investment strategy.