strategic account management

This study addresses the critical challenge of predicting and interpreting customer data-sharing behavior in open banking environments. Methodologically, it proposes a hybrid data-balancing strategy integrating ADASYN (adaptive synthetic sampling) and NearMiss undersampling—applied for the first time to highly imbalanced sharing-behavior data (N = 3.2 million)—and constructs an XGBoost prediction model coupled with a dual-path interpretability framework combining SHAP (for global feature attribution) and CART (for local, rule-based explanations). Key findings identify mobile transaction frequency and credit card usage behavior as the most influential drivers of data sharing. Experimental results demonstrate high predictive accuracy—91.39% for inbound and 91.53% for outbound sharing predictions—significantly outperforming baseline models. The approach delivers actionable, quantitatively grounded insights into causal determinants of sharing behavior, thereby supporting financial institutions in optimizing data governance, product design, and competitive strategy formulation.

To address the limited interpretability of e-commerce customer churn models and their inadequate support for fine-grained retention decisions, this paper proposes a three-stage analytical framework integrating explainable AI (XAI), survival analysis, and RFM-based behavioral segmentation. Methodologically, it synergistically combines SHAP-based feature attribution, the Cox proportional hazards model, and dynamic RFM clustering to enable churn root-cause interpretation, optimal intervention timing prediction, and precise identification of high-risk customers. Its key contribution lies in the first systematic integration of explanation-driven attribution analysis, temporal risk modeling, and behaviorally heterogeneous segmentation—overcoming the opacity limitations of conventional black-box models. Empirical evaluation demonstrates that the framework significantly enhances both predictive transparency and intervention efficacy: customer retention improves by 12.7%. It thus delivers verifiable, actionable, and data-driven decision support for personalized retention strategies.

研究通过整合CRM、MDM和CKM构建CRI框架，使用统计分析方法探讨了提升客户参与度的关键因素，发现CRM和CKM是主要驱动力。

To address challenges in commercial management system development—including poor alignment between process models and execution platforms, low model reusability, and suboptimal development efficiency—this paper proposes a metamodel-based Model-Driven Development (MDD) approach. We design an evolvable and extensible business process metamodel framework and introduce a staged model transformation mechanism supporting QVT/ATL, enabling automated adaptation of extended BPMN models to diverse execution platforms. Crucially, we deeply integrate MDD into BPM system construction, establishing business models as the authoritative source governing system behavior. Experimental evaluation demonstrates significant improvements in development productivity and model consistency, robust cross-platform model reuse, and validates the metamodel’s effectiveness and flexibility in extended application scenarios such as resource management and customer relationship management.

This study addresses the trade-off between cost and service quality in multichannel customer service by modeling the entire service process as a gated system. It jointly optimizes decisions across three levels: strategic (channel deployment), tactical (staffing and AI allocation), and operational (real-time scheduling). Leveraging operations research, dynamic modeling, and numerical simulation, the work derives a structured optimal request-handling policy and uncovers a counterintuitive insight: judicious deployment of AI chatbots not only enhances service efficiency but also significantly improves service quality, thereby achieving simultaneous optimization of cost and customer experience.

Traditional revenue forecasting approaches struggle to uncover the underlying customer behavioral drivers—such as customer acquisition, repeat purchase rates, and average transaction value—that influence revenue dynamics. To address this limitation, this work proposes the Customer-Based Multi-Task Transformer (CBMT), which uniquely integrates multi-task learning with a Transformer architecture to jointly model customer behavioral metrics and total revenue through shared representations. Furthermore, CBMT incorporates a downstream alignment mechanism to enhance both interpretability and predictive accuracy. Empirical evaluation on real-world customer transaction panel data demonstrates that CBMT outperforms existing methods across 23 out of 24 evaluation metrics, achieving a 30% reduction in total sales prediction error compared to the strongest baseline and significantly surpassing single-task models employed by 74.3% of firms.

This work addresses the limitations of traditional enterprise messaging systems, which rely heavily on external connectors and consequently suffer from fragmented data between messages and CRM records, achieving only eventual consistency through brittle synchronization tasks that hinder seamless automation and unified state management. To overcome these challenges, the authors propose and implement a platform-native messaging architecture that treats messages as first-class CRM entities. By leveraging platform events, asynchronous delivery, multi-tenant decoupling, and standard database object models, this approach deeply integrates the entire message lifecycle into CRM transactions, workflows, and reporting systems. The architecture has been successfully deployed at scale across multiple industries—including healthcare, sales, and field service—demonstrating significant improvements in system consistency, scalability, and automated integration capabilities.

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 article introduces a metamodel for the Business Model Canvas (BMC) using the Unified Modelling Language (UML), together with a dedicated Domain-Specific Modelling Language (DSML) tool. Although the BMC is widely adopted by both practitioners and scholars, significant challenges remain in formally modelling business models, particularly with regard to explicit specification of inter-component relationships, while preserving the simplicity that characterises the BMC. Addressing this tension between modelling rigour and practical relevance, this research adopts a Design Science Research approach to formally specify relationships among BMC components and to strengthen their theoretical grounding through an adaptation of the V 4 framework. The proposed metamodel consolidates BMC relationships into three core types: supports, determines, and affects, providing explicit semantics while remaining accessible to end users through graphical tooling. The findings highlight that formally specifying relationships significantly improves the interpretability and consistency of BMC representations. The proposed metamodel and tool offer a rigorous yet usable foundation for developing DSML-based BMC tools and for enabling systematic integration of the BMC into widely used software and enterprise modelling environments, thereby bridging business modelling and enterprise architecture practices for both academics and practitioners.

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.