A Sentiment-Driven Churn Management Framework Using CRM Text Mining and Performance Dashboards

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@article{1710061,
      author = {Ololade Shukrah Abass, Oluwatosin Balogun, Paul Uche Didi},
      title = {A Sentiment-Driven Churn Management Framework Using CRM Text Mining and Performance Dashboards},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {5},
      pages = {251-271},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710061.pdf},
      abstract = {Customer churn remains a critical challenge for businesses, directly impacting revenue and long-term profitability. This study proposes a Sentiment-Driven Churn Management Framework that integrates CRM text mining, natural language processing (NLP), and performance dashboards to enhance customer retention strategies. By analyzing unstructured customer interactions emails, chat logs, service tickets, and social media comments this framework extracts sentiment trends, identifies early signs of dissatisfaction, and correlates emotional tone with churn probability. Leveraging advanced sentiment analysis algorithms and CRM-integrated data pipelines, the system quantifies positive, negative, and neutral sentiment trajectories across individual and aggregated customer profiles. These insights are visualized through interactive dashboards that highlight high-risk segments, customer sentiment evolution, service experience metrics, and churn risk scores in real-time. The framework adopts a layered architecture consisting of data ingestion, sentiment scoring, churn prediction modeling, and performance visualization. Using supervised learning techniques and feedback loops, it enables adaptive refinement of churn models based on evolving customer behavior. By aligning these insights with targeted intervention workflows, customer service teams can proactively address pain points, personalize outreach, and optimize retention campaigns. The dashboards also enable executive stakeholders to monitor KPIs such as sentiment-to-churn correlation, intervention response rates, and churn reduction ROI. Tested across multiple service-oriented businesses, the framework demonstrates improved churn prediction accuracy, enhanced visibility into customer dissatisfaction drivers, and more timely, data-driven responses. The integration of sentiment signals into churn analytics offers a more nuanced understanding of customer experience beyond transactional data alone. Furthermore, the dashboards support cross-functional decision-making by unifying marketing, service, and executive insights in a single interface. This study contributes to the growing field of emotion-aware customer analytics and emphasizes the strategic role of text mining and dashboard-driven intelligence in customer lifecycle management. Future research may explore multilingual sentiment analysis, real-time social listening integration, and the use of generative AI for automated response generation.},
      keywords = {Sentiment Analysis, Churn Management, CRM Text Mining, Customer Retention, Performance Dashboards, Natural Language Processing, Emotion Analytics, Customer Experience, Predictive Modeling, Service Personalization.},
      month = {November},
  }