arXiv AI
Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement
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arXiv Machine Learning
Jul 2
From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research
arXiv:2607. 00512v1 Announce Type: new Abstract: Structural equation modelling (SEM) is widely used in survey-based business and information systems research to assess latent constructs and theory-driven structural relationships.
arXiv Machine Learning
Jul 28
Beyond Adoption Intention How Trust in Augmented Analytics Relates to Perceived Decision Quality Among Non-Technical BI Users
arXiv:2605. 20198v2 Announce Type: replace-cross Abstract: Augmented analytics has transformed how Business Intelligence (BI) systems support decision-making, shifting non-technical managers from manual analysis toward dependence on automated insights.
arXiv Computation and Language
Sep 10
CARRE: Counterfactual Action Retrieval and Reason Evaluation for Explainable Churn Prescription
arXiv:2609.09766v1 Announce Type: new Abstract: Churn models typically identify high-risk customers but do not specify which feasible retention action should be considered or why that action is appro...
arXiv Machine Learning
Jul 14
Data-Driven Telecom Marketing Optimization: A Machine Learning-Based Churn Prediction and Customer Segmentation Framework
arXiv:2607. 10260v1 Announce Type: new Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships.
arXiv AI
Aug 28
Explainable Artificial Intelligence for Customer Churn Prediction in Telecommunications: A Framework for CRM Integration
The paper presents a framework for integrating explainable AI into customer churn prediction for telecommunications. It benchmarks four classifiers—Logistic Regression, Random Forest, XGBoost, and LightGBM—on the IBM Telco Customer Churn dataset, finding comparable performance with Logistic Regression achieving the highest AUC-ROC and LightGBM the highest accuracy. Explanations are provided via SHAP and LIME at both global and instance levels, and a four‑layer CRM integration architecture is proposed to translate risk scores and attribution vectors into actionable retention strategies, projecting a 3.3–5.3 percentage point reduction in churn and $199K–$319K savings per campaign cycle.
arXiv AI
Sep 15
Beyond AI Literacy: A Structured Review and Exploratory Meta-Analysis of Measures for Competent Generative-AI Use
arXiv:2609.15624v1 Announce Type: cross Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We...