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Open research questions in Customer churn and segmentation

118 unresolved questions extracted from the limitations and future-work sections of 479 Customer churn and segmentation papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Sarinah records large transaction data, but most is used only to present sales reports and is rarely explored in-depth to understand customer behavior, customer value, or customer loyalty.

    Customer Segmentation using Data Mining Technique Method Case: Sarinah Department Store · 2026 · DOI

  • In this digitized era, implementing customer transaction data is essential, particularly in the case of department stores such as Sarinah, which have a wide range of customers whose purchasing habits vary widely.

    Customer Segmentation using Data Mining Technique Method Case: Sarinah Department Store · 2026 · DOI

  • The banking industry lacks effective and efficient marketing strategies. There is a need to leverage big data analytics-based technology to develop more effective and efficient marketing strategies. The study aims to fill this gap by examining the influence of data-based marketing strategies on marketer performance and CASA.

    Analysis of Hyperpersonalized Marketing Strategy on Marketer Performance and Potential for CASA Improvement · 2026 · DOI

  • The high-dimensional and temporal nature of e-commerce data. The need to balance profitability with customer satisfaction. The failure of traditional machine learning models in capturing temporal dependencies.

    Real-Time Adaptive Pricing in E-Commerce Using Deep Learning and Customer Demand Forecasting · 2026 · DOI

  • Traditional pricing methods are increasingly inadequate for handling the high-dimensional and temporal nature of e-commerce data. The core challenge in modern dynamic pricing is the sustainable balancing of profitability with customer satisfaction.

    Real-Time Adaptive Pricing in E-Commerce Using Deep Learning and Customer Demand Forecasting · 2026 · DOI

  • Traditional churn prediction methods rely on statistical techniques and rule-based models. These methods often struggle to capture complex, non-linear relationships within large and diverse datasets.

    Multi-domain subscriber churn prediction using ANN · 2026 · DOI

  • Traditional CRM systems rely on rigid segmentation rules and look backward rather than forward. There is a need for a more personalized and adaptive approach to CRM personalization. The paper identifies a gap in the current literature and practice of CRM personalization.

    A framework for hybrid CRM personalization: combining rule-based logic with machine learning predictions · 2026 · DOI

  • Future work could explore whether formalizing business constraints as soft penalties within the ML training objective improves resilience to con- cept drift, but this remains a speculative direction rather than a demonstrated methodology. This single- organization evaluation is a major limitation.

    A framework for hybrid CRM personalization: combining rule-based logic with machine learning predictions · 2026 · DOI

  • No single system simultaneously addresses contactless churn prediction, behavioral anomaly detection, profitability-weighted retention prioritization, and cloud-based production deployment. The proposed framework addresses this gap by integrating supervised churn prediction and behavioral anomaly detection.

    Customer Rentention and Profitability Analysis for the Banking Sector Using Machine Learning · 2026 · DOI

  • Future work will explore the integration of LSTM-based temporal modeling for sequential behaviour analysis, federated to enable privacy-preserving multi-institution training, real-time Apache Kafka- based data streaming, and the extension of the framework to insurance and wealth management contexts where analogous churn dynamics apply.

    Customer Rentention and Profitability Analysis for the Banking Sector Using Machine Learning · 2026 · DOI

  • The fast-paced atmosphere of the fashion retail industry. The rapidly changing trends and customer base. The need for efficient inventory management and profitability.

    Purchasing Pattern Exploration in Fashion Retail using Transaction Data · 2026 · DOI

  • The lack of understanding of consumer purchasing behavior in the fashion retail industry. The need for actionable insights to support management and marketing decisions.

    Purchasing Pattern Exploration in Fashion Retail using Transaction Data · 2026 · DOI

  • To implement and test the proposed framework in a real-world setting. To explore the use of other machine learning algorithms and techniques to improve the predictive accuracy of the framework. To examine the impact of the framework on customer satisfaction and loyalty in the mobile payment industry.

    A Study on Customer Satisfaction Analysis of Mobile Payment (UPI) Applications · 2026 · DOI

  • Traditional survey methods have limitations in measuring customer satisfaction. A comprehensive framework is needed to capture both objective and subjective user interactions and provide actionable insights for service providers.

    A Study on Customer Satisfaction Analysis of Mobile Payment (UPI) Applications · 2026 · DOI

  • There is a gap in providing good customer experience in supermarkets due to massive customer contacts and poor navigation. Little has been done to apply conversational AI to real-life situations in supermarkets.

    Persuasive Marketing Intelligence in Supermarket Kiosk Agents: Extending an Intelligent Receptionist System for Context-Aware Recommendations · 2026 · DOI

  • in-store customer should be processes without interaction interruptions. The operational scalability of the system is supported with the ability to become a complementary tool assisting the staff instead of substituting them. As the implementation shows, an intelligent intent classification that is combined with structured retail information may make the processes more efficient and help in the optimization of revenues in a physical retail context. The rest of this paper addresses related work, system the architecture, methodology, implementation, and performance evaluation of the suggested framework of the conversational kiosk. details the of through systems are aimed at enhancing the flexibility and interactivity with customers the use of automation technologies. Service robots based on the use of large language models are also presented in the environment of the shopping mall to make a better user interaction and give a contextual assistance. These applicability of systems portray conversational AI in offline retail environments. increasing the language model and NLP-based customer service systems have gone a step higher to enhance automated query processing through ranking pre-trained system. The literature has also investigated AI customer service software in the localized settings including healthcare and online shopping platforms where it has shown to be more user-satisfied and efficient with intelligent automation,. response Moreover, chat agents that cater to the needs of ecommerce settings have revealed that automated chat systems can be useful in answering questions raised by customers as well as to aid in online-shopping choices,. Most of such implementations however are more of virtual or online providing than the physical setting supermarket.

    Persuasive Marketing Intelligence in Supermarket Kiosk Agents: Extending an Intelligent Receptionist System for Context-Aware Recommendations · 2026 · DOI

  • Future research can explore the application of other clustering algorithms for customer segmentation. Future research can investigate the use of K-Means clustering for other business analytics tasks.

    Customer Segmentation Using K-Means Clustering for Business Analytics · 2026 · DOI

  • The study identifies a gap in the literature regarding the application of K-Means clustering for customer segmentation. The study aims to address this gap by demonstrating the effectiveness of K-Means clustering.

    Customer Segmentation Using K-Means Clustering for Business Analytics · 2026 · DOI

  • To further evaluate the effectiveness of the RL-based approach in different retail settings. To explore the application of the RL-based approach in other domains.

    Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights · 2026

  • The gap between costly but accurate trajectory data and oversimplified heuristic approximations. The limitations of traditional heuristics in capturing realistic customer behaviour.

    Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights · 2026

  • © 2026, JOIREM |www.joirem.com| Page 6 ISSN (O) 3107-6696 Journal Publication of International Research for Engineering and Management (JOIREM) Volume: 04 Issue: 5 | May-2026 ISSN (O) 3107-6696 Although the proposed AI-Based Intelligent CRM system successfully integrates customer management functionalities with predictive analytics, several enhancements can be incorporated in future versions to further improve intelligence, scalability, and business automation capabilities. One major direction for future work is the development of advanced AI-driven lead scoring and recommendation mechanisms. While the current Lead Conversion Prediction Model estimates the probability of converting a lead into an opportunity, future systems can incorporate hybrid machine learning and deep learning models capable of generating personalized recommendations for sales actions, customer engagement strategies, and opportunity prioritization. Another important enhancement involves the integration of sentiment analysis and Natural Language Processing (NLP) and emails, techniques. Customer support analyzed to determine communication records can be customer sentiment and behavioral intent. Such analysis would allow organizations to identify dissatisfied customers, predict churn risks, and provide proactive support strategies. tickets, The proposed system can also be extended through multi- channel communication integration. Future versions may support communication platforms such as Email, WhatsApp, systems directly within the CRM SMS, integration would enable centralized environment. This communication management customer engagement workflows. and chatbot automated and Future work may additionally focus on real-time analytics and event-driven CRM automation. Instead of relying solely on static dashboard reports, streaming analytics and real-time notification systems could be incorporated to instantly monitor lead activity, customer interactions, and sales pipeline changes. Such capabilities would significantly improve organizational responsiveness and operational efficiency. Another promising area involves AI-powered customer recommendation and behavioral prediction systems. By analyzing historical customer interactions and purchasing patterns, the CRM system could recommend suitable products, services, or tailored to individual customer profiles. This would contribute to personalized marketing and improved customer retention. follow-up strategies cloud-native From a technical perspective, future implementations may support and microservices architecture to improve scalability and distributed processing.

    AI Based Contact Extraction for CRM · 2026 · DOI

  • The rapid proliferation of digital retail channels has generated vast repositories of consumer behavioral data. Organizations need to derive actionable intelligence through advanced analytical frameworks. Fewer than 30% of mid-sized retailers have implemented predictive analytics at scale.

    Predictive Modeling and Customer Segmentation Through Data Analytics · 2026 · DOI

  • There is a notable absence of studies that integrate RFM, K-Means, and Random Forest within a unified pipeline. There is a lack of studies that validate this framework on large-scale real-world retail datasets. There is a need to contextualize findings with industry practitioner experience.

    Predictive Modeling and Customer Segmentation Through Data Analytics · 2026 · DOI

  • Integration across heterogeneous enterprise systems may require substantial configuration effort. Model interpretability remains a concern, especially in decision settings where users require clear justification for AI-generated outputs. The current evaluation relies on simulated enterprise scenarios rather than live operational environments.

    Design and implementation of generative Artificial Intelligence–driven automation for enterprise customer relationship management decision support systems · 2026 · DOI

  • To evaluate the framework in live operational environments. To address model interpretability concerns. To explore the application of the framework in various enterprise environments.

    Design and implementation of generative Artificial Intelligence–driven automation for enterprise customer relationship management decision support systems · 2026 · DOI

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118 open questions have been extracted from the limitations and future-work passages of 479 Customer churn and segmentation papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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