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Open research questions in Customer churn and segmentation

25 unresolved questions extracted from the limitations and future-work sections of 422 Customer churn and segmentation papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Future research may focus on the development of Agentic AI-powered assistants to automate retail operations, including inventory monitoring, customer engagement, demand forecasting, and report generation.

    An Explainable AI Framework for Integrated Retail Analytics and Predictive Business Intelligence · 2026 · DOI

  • Theoretical and Practical Value of the Model interpretation”, At the theoretical level, this study has constructed an framework comprising “RFM clustering— integrated thereby prediction—SHAP XGBoost enriching the application system of interpretable machine learning in the field of marketing and filling a gap in research on the interpretability of clustering models.

    An Experimental Study on E-commerce Customer Churn Prediction Based on the Explainable XGBoost Algorithm · 2026 · DOI

  • Future research is recommended to incorporate stakeholder- based validation to assess the managerial and operational relevance of the identified segments, as well as to expand the feature set with more comprehensive customer attributes to enhance segmentation robustness and business interpretability. It is also important to note that the study is limited by the relatively small number of features used, which may restrict the richness of customer representation in the segmentation process.

    Customer Segmentation in an Internet Service Provider: A K-Means Case Study of Telecommunication Company<b></b> · 2026 · DOI

  • customer analytics platforms. Predictive models can help organizations identify customer purchasing behavior, forecast product demand, improve customer retention, and optimize marketing strategies. Intelligent analytical systems may also support and and digital shopping platforms Another important future direction is real-time customer analytics using streaming customer data generated from online commerce applications. Modern e-commerce systems continuously interaction data through browsing generate customer activities, payment transactions, product searches, and customer engagement records. Processing these real-time customer streams efficiently requires distributed processing frameworks and scalable cloud-based analytical platforms. Future research may focus on integrating Apache Spark Streaming, Databricks workflows, and real-time dashboard systems to improve customer behavior monitoring and business intelligence operations,. customer Scalability and customer data security are also important research challenges for future customer analytics systems. As customer datasets continue to grow rapidly, analytical frameworks must efficiently process large-scale transaction records and activities without affecting performance and reliability. In addition, customer analytics systems contain sensitive information such as payment details, browsing histories, and transaction records, requiring secure data management and controlled access mechanisms. Future analytical frameworks may incorporate advanced encryption methods, secure cloud computing models, and privacy-aware analytical techniques for secure customer data processing and business reporting,. Another promising research direction involves enhancing dashboard systems using intelligent visualization and automated reporting techniques. Future dashboards may include advanced analytical features such as real-time KPI interactive drill-down analysis, automated monitoring, business reporting, and insight generation. The integration of scalable cloud-based analytics, distributed advanced visualization techniques can significantly improve business intelligence and operational decision making within ecommerce organizations,. intelligent customer frameworks, processing and Furthermore, future research may focus on integrating conversational assistants intelligent analytical and © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution. https://ijsmt.org/, Email: [email protected] 8 International Journal of Science, Strategic Management and Technology Volume 02 Issue 06 June-2026 | ISSN: 3108-1762 (Online) | Impact Factor: 3.8 An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases dashboard systems within customer analytics platforms for improved user interaction and automated business insights. These intelligent systems may help business analysts interpret customer behavior data more efficiently and support faster data-driven decision making. Therefore, the continuous development of scalable data engineering technologies, distributed analytical frameworks, and intelligent dashboard systems will play a significant role in the future of e-commerce customer analytics and digital business intelligence,. VI.

    A Survey on E-commerce Customer Behavior Analytics: Challenges, Insights and Tools · 2026 · DOI

  • context of label assigned Although the combination of Recency, Frequency, and Monetary (RFM) analysis with the K­Means algorithm has been extensively discussed customer segmentation (ASLANTAŞ et al., 2023; Djun et al., 2024; Ikotun et al., 2023; Jamunadevi et al., remain unresolved, especially in the Indonesian MSME context. First, most prior studies treat CLV as a post hoc to clusters after clustering is complete, rather than as a feature included from the outset, so that the economic value dimension does not directly shape the structure of the resulting segments. Second, the majority of customer segmentation studies in Indonesia are conducted on relatively small datasets, typically fewer than 1,000 customers, or rely on public datasets such as UCI Online Retail from the United Kingdom, which do not the characteristics of adequately represent Indonesian MSMEs. Third, only a few studies have conducted a sensitivity analysis of the discount rate used in CLV calculations, even though Gupta et al. (2004) showed that small changes in this parameter can substantially affect customer value estimation. Finally, the segmentation results from many studies have into an actionable not been internal framework analytical capabilities can implement directly. translated that MSMEs lacking Based on these gaps, this study proposes an segmentation customer integrated framework that combines RFM analysis with CLV calculated as the discounted historical net sales used as the fourth feature in the K­Means feature space, with K­Means++ initialization. With this approach, the resulting segments reflect not only transactional patterns (recency, frequency, monetary value) but also the realized economic value each customer has contributed to the business. The framework is applied to a real dataset from MSME X, consisting of 19,126 transactions, of which 4,310 are member transactions belonging to 472 unique registered customers, recorded over a full one­year period (January–December 2023). The optimal number of clusters is determined by combining the Elbow Method and the Silhouette Coefficient. At the same time, Z­score standardization is applied to prevent variables with large ranges, such as the Monetary and CLV, from dominating clustering process. The segmentation results are tiers into then Research in Education, Technology, and Multiculture | 2 four customer interpreted Rizkyandita et al., CLV­Enhanced RFM Framework for Customer... the (Platinum, Gold, Silver, Bronze), each paired with a specific retention strategy. Conceptually, theoretical this approach builds on foundations of RFM (Alves Gomes & Meisen, 2023), CLV (Gupta et al., 2004), and data mining for customer segmentation (Han et al., 2012; Khajvand & Tarokh, 2011), and extends the practice reported in prior Indonesian studies (Marisa et al., 2019; Matz & Hermawan, 2020). This study is directed to answer the following three research questions: RQ1. How can Customer Lifetime Value be effectively integrated as an input feature in the K­Means clustering process so that the resulting segments simultaneously reflect the transactional behavior and realized economic value of customers? RQ2. What segmentation structure comes up when the CLV­enhanced RFM framework is applied to a real Indonesian MSME and dataset, economic characteristics distinguish each of the resulting segments? and what behavioral from the very beginning of RQ3. What concrete retention and loyalty strategies can be derived the resulting segmentation that are feasible for MSMEs operating with limited analytical capabilities? In this paper, much focus is placed on from a three main contributions. First, methodological standpoint, this study extends the classical RFM framework by integrating CLV as an input feature into K­Means, so that the dimension of realized economic value contributes to the formation of the segment structure from the clustering process.

    CLV-Enhanced RFM Framework for Customer Segmentation in Indonesian SMEs Using K-Means Clustering · 2026 · DOI

  • © 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

  • 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 should investigate the application of this framework to longitudinal data to model segment transitions over the customer lifecycle, and explore the comparative performance of advanced clustering algorithms — including Gaussian Mixture Models and density-based methods such as DBSCAN — on comparable retail datasets.

    Customer Segmentation Using K-Means Clustering for Business Analytics · 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

  • To further refine the analytics of mobile payment satisfaction, future research should explore several promising avenues. • First, future work should investigate the integration of "virtual mirroring" techniques within the customer support teams handling UPI disputes. By measuring the communication patterns of support agents through semantic analysis and mirroring it back to them, platforms could potentially trigger changes in employee behavior that directly increase end-user satisfaction (Gloor et al., 2021). • Second, the analytical framework should be expanded to study cross-border mobile payment infrastructures. As © 2026 The Author(s). Published by IJCOPE Journal. Website: https://ijcope.org/ 5 International Journal of Creative and Open Research in Engineering and Management ISSN: 3108-1754 (Online) Volume 02 Issue 05 May-2026 | Impact Factor: 3.5 digital economies become increasingly interconnected, understanding the network externalities and trust factors that drive international mobile payment acceptance will require more sophisticated, cross-cultural causal models (Ajao et al., 2023). Exploring how digital resilience varies across different regulatory and cultural environments will be crucial for the global expansion of these payment technologies (Alhassan & Butler, 2021).

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

  • While the current avenues for future enhancement exist: Hybrid Model Integration: Future research could explore hybrid architectures that combine LSTMs with Reinforcement Learning (RL) to allow the system to learn optimal pricing policies through direct interaction with the market environment. Multi-Objective Optimization: Further refinement of the trade-off between profitability and satisfaction can be achieved through multi-objective optimization techniques to handle more complex constraint scenarios. Sentiment Analysis Integration: Incorporating real-time sentiment analysis from social media (e.g., Twitter/X) using BERT could provide deeper insights into consumer perception and price fairness. Advanced Explainability: Implementing tools like SHAP or EBM would increase the transparency of the LSTM's decision-making process, fostering greater trust among business stakeholders.

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

  • In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

    Predictive quality model for customer defects · 2024 · DOI

  • Research at the intersection of these two fields is scarce and there is a need for conceptual work that (1) provides an overview of opportunities to use BDA for CXM and (2) guides management practice and future research.

    Customer experience management in the age of big data analytics: A strategic framework · 2020 · DOI

  • A recurring obstacle is that product descriptions in such sources are short, noisy, and abbreviated, with no standard product code, so each item must first be mapped to a consumption classification (e.

    Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling · 2026

  • Offer premium content, reinforce loyalty programs Re-engagement campaigns, reminders Encourage deeper usage, upselling Gamification, habit-building…

    Leveraging gradient boosting machine learning models to predict customer churn in digital health platforms · 2026 · DOI

  • It is more reasonable to use the traditional algorithms if each continuous data variable has target benchmark(s), whereas the k-median clustering algorithm achieves good modeling results when benchmark information is lacking.

    Exploring Construct Measures Using Rasch Models and Discretization Methods to Analyze Existing Continuous Data · 2024 · DOI

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25 open questions have been extracted from the limitations and future-work passages of 422 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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