Science AI Journal
Business, Management and Accounting · Research topic

Open research questions in Customer churn and segmentation

116 gap statements mined from Customer churn and segmentation papers in our 7.9M-paper local library, which holds 689 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

Choosing where to publish on Customer churn and segmentation? See the ranked Economics journals.

What the literature leaves open

  • 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

  • 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.

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

  • Future work could explore whether formalizing business constraints as soft penalties within the ML training objective improves resilience to concept 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

  • 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 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 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.

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

  • The need for a reliable prediction model for booking likelihood. The lack of a comprehensive comparison of machine learning models for booking likelihood prediction.

    A Comparative Study of Machine Learning Models for Airbnb Booking Likelihood Prediction in Singapore with GridSearchCV Optimization · 2026 · DOI

  • Customer retention has become a critical challenge for modern businesses due to increasing competition and changing customer behavior. Existing systems do not provide personalized customer retention strategies based on customer risk levels and behavioral segmentation.

    AI Driven Customer Churn Prediction and Retention Recommendation System · 2026 · DOI

  • Future studies can explore the application of the hybrid analytical approach employed in the study to other industries and contexts. The development of more sophisticated machine learning models and algorithms can enhance segmentation accuracy and support targeted marketing strategies.

    Uncovering Customer Archetypes in Direct-to-Consumer Apparel: A K-Means Clustering Analysis of DMart Sales Data · 2026 · DOI

  • Traditional segmentation approaches are often insufficient to capture multidimensional consumer behavior. There is a need for advanced analytical techniques to understand consumption patterns.

    Consumer Segmentation in Household Appliances Market Using K-Means Clustering · 2026 · DOI

  • Further research can be done to validate the results using external data. The study can be extended to include other factors that affect customer behavior and loyalty.

    Analisis Kepuasan Pelanggan terhadap PT.XYZ dengan Menggunakan Metode Recency, Frequency, Monetary (RFM) dan K-Means Clustering · 2026 · DOI

  • PT XYZ lacks customer segmentation based on transaction data. There is a need for a method to segment customers based on transaction behavior.

    Analisis Kepuasan Pelanggan terhadap PT.XYZ dengan Menggunakan Metode Recency, Frequency, Monetary (RFM) dan K-Means Clustering · 2026 · DOI

  • The need for a holistic strategic imperative for customer experience personalization. The gap in the integration of advanced AI-driven analytics and ethical dimensions of data utilization for customer experience personalization.

    Strategies for customer experience personalization in the context of retail digitalization: methodological aspects · 2026 · DOI

  • The study acknowledges the limitation of using a single-item indicator to measure satisfaction, which may not fully capture its multidimensional nature. The study notes the potential cultural bias of focusing on fitness centres in Spain. The study identifies the need for future research to explore more advanced artificial intelligence techniques, such as deep learning or natural language processing.

    Machine learning prediction of customer satisfaction in fitness centres · 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.

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

  • The study is limited to a specific retail format and dataset. The study does not provide a generalizable result for all retail formats and geographic regions.

    Türkiye’de Özel Etiketli Markalar için Premium Konseptli Mağaza Yayılımı: File Market Örneği ve Makine Öğrenmesi Yaklaşımı · 2026 · DOI

  • The study suggests that future research should focus on analyzing different retail formats and geographic regions. The study recommends that future research should investigate the impact of other variables, such as marketing strategies and competitor analysis, on store expansion.

    Türkiye’de Özel Etiketli Markalar için Premium Konseptli Mağaza Yayılımı: File Market Örneği ve Makine Öğrenmesi Yaklaşımı · 2026 · DOI

  • To explore the application of the RL-based approach in other domains.

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

  • The sample size was limited to 370 respondents. A convenience sampling method was adopted due to accessibility and time constraints.

    Consumer Segmentation in Household Appliances Market Using K-Means Clustering · 2026 · DOI

  • The company needs data-driven marketing strategies and efficient operational planning.

    Clustering and Sales Prediction Using K-Means and Simple Linear Regression · 2026 · DOI

  • Extend the framework using multivariable and interaction-based approaches, - Compare the present results with ordinal models, - Integrate customer evaluations with operational logistics indicators, - Apply more advanced multivariable methods, such as factorial ANOVA, generalized or ordinal regression, multilevel models, or structural equation modeling…

    Data-Driven Decision Making in Logistics: An Integrated Predictive–Inferential Framework for Operator Selection · 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

  • 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

  • Future research should complement this classification approach with regression-based machine learning models - Exploring more advanced artificial intelligence techniques, such as deep learning or natural language processing, to analyse unstructured data - Integrating data from wearable devices, connecting and tracking real-time usage of equipment installed…

    Machine learning prediction of customer satisfaction in fitness centres · 2026 · DOI

Most-cited papers in Customer churn and segmentation

Most recent work

Find a gap in your own Customer churn and segmentation sub-topic

This page shows what the Customer churn and segmentation literature already flags as unresolved. To narrow it to your specific question, search the Research Gap Finder: the search is free with a free account and lists the papers closest to your topic first. Unlocking that topic (50 credits, charged once) fills the comparison table from our 7.9M-paper local library and writes the gaps from its rows.

Open the Research Gap Finder →

Related topics in Business, Management and Accounting

Job Satisfaction and Organizational Behavior
1,827 open questions
Environmental Sustainability in Business
1,128 open questions
Corporate Social Responsibility Reporting
1,099 open questions
Entrepreneurship Studies and Influences
1,094 open questions
Sustainable Supply Chain Management
978 open questions
Consumer Behavior in Brand Consumption and Identification
806 open questions
Management and Organizational Studies
794 open questions
International Business and FDI
619 open questions
Open questions raised by papers listed on this page include: (1) There is a need to leverage big data analytics-based technology to develop more effective and efficient marketing strategies (“Analysis of Hyperpersonalized Marketing Strategy on Marketer Performance and…”, 2026); (2) Future work could explore whether formalizing business constraints as soft penalties within the ML training objective improves resilience to concept drift, but this remains a speculative direction rather than a demonstrated methodology (“A framework for hybrid CRM personalization: combining rule-based logic with…”, 2026); (3) The lack of understanding of consumer purchasing behavior in the fashion retail industry (“Purchasing Pattern Exploration in Fashion Retail using Transaction Data”, 2026); (4) To further refine the analytics of mobile payment satisfaction, future research should explore several promising avenues (“A Study on Customer Satisfaction Analysis of Mobile Payment (UPI) Applications”, 2026); (5) The lack of a comprehensive comparison of machine learning models for booking likelihood prediction (“A Comparative Study of Machine Learning Models for Airbnb Booking Likelihood…”, 2026). 116 gap statements have been mined from Customer churn and segmentation papers in our 7.9M-paper local library, which holds 689 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 1,100,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? Acceptance odds for top, mid and open/emerging journals, in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck for prior publication across 7 sources and our local library.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

How Pre-Check worksHow AI Review works

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.