SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market
6 references·3 citing·41 second hop
An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market (2023)An Exploration of Clustering Algorithms f…Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020)Predictive big data analytics…Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data (2018)Customer segmentation using o…Customer Segmentation using K-means Clustering (2018)Customer Segmentation using K…Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making (2018)Benefit-based consumer segmen…Big Data technologies: A survey (2017)Big Data technologies: A surv…An Environmental Perspective on Clothing Consumption: Consumer Segments and Their Behavioral Patterns (2017)An Environmental Perspective …Topic modelling through the bibliometrics lens and its technique (2025)Topic modelling through the b…Enhancing Customer Segmentation Through Factor Analysis of Mixed Data (FAMD)-Based Approach Using K-Means and Hierarchical Clustering Algorithms (2025)Enhancing Customer Segmentati…Customer segmentation in e-commerce: a context-aware quality model for comparing clustering algorithms (2024)Customer segmentation in e-co…Market Segmentation: Conceptual and Methodological Foundations (1997)Fast fashion: response to changes in the fashion industry (2010)Identifying careless responses in survey data. (2012)Human Perceptions of Recycled Textiles and Circular Fashion: A Systematic Literature Review (2020)Sustainable Retailing (2020)The impact of shopping motivation on sustainable consumption: A study in the context of green apparel (2021)Big Data : The Management Revolution (2013)Beyond the hype: Big data concepts, methods, and analytics (2014)Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014)A survey towards an integration of big data analytics to big insights for value-creation (2018)Big data adoption: State of the art and research challenges (2019)Disaster resilience through big data: Way to environmental sustainability (2020)Case Study Research: Design and Methods (1984)Case study research: Design and methods (1986)Mixed Methods Research: A Research Paradigm Whose Time Has Come (2004)Market segmentation using high-dimensional sparse consumers data (2019)Segmentation of Coffee Consumers Using Sustainable Values: Cluster Analysis on the Polish Coffee Market (2019)Analisis Data Artikel Sistem Pakar Menggunakan Metode Systematic Review (2020)Product Differentiation and Market Segmentation as Alternative Marketing Strategies (1956)Market Segmentation: A Review (1987)The Process of Customer Engagement: A Conceptual Framework (2008)Key trends in business-to-business services marketing strategies: Developing a practice-based research agenda (2020)Customer segmentation by web content mining (2021)A review on customer segmentation methods for personalized customer targeting in e-commerce use cases (2023)Perspectives in supply chain risk management (2006)Application of machine learning techniques for supply chain demand forecasting (2007)Big data analytics in logistics and supply chain management: Certain investigations for research and applications (2016)At the Epicenter of COVID-19–the Tragic Failure of the Global Supply Chain for Medical Supplies (2020)A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality (2023)Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility (2023)Multiple Attribute Decision Making (1981)K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data (2022)The impact of digital transformation on supply chains through e-commerce: Literature review and a conceptual framework (2022)Software survey: VOSviewer, a computer program for bibliometric mapping (2009)Interrater reliability: the kappa statistic (2012)Citation-based clustering of publications using CitNetExplorer and VOSviewer (2017)RFM ranking – An effective approach to customer segmentation (2018)Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods (2024)Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda (2023)Deep Learning in Finance: A Survey of Applications and Techniques (2024)Optimizing Marketing Strategies with RFM Method and K-Means Clustering-Based AI Customer Segmentation Analysis (2024)
this work references cited by second hop

References

  • 2017Big Data technologies: A survey994 cited
  • 2020Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities505 cited
  • 2018Customer Segmentation using K-means Clustering186 cited
  • 2017An Environmental Perspective on Clothing Consumption: Consumer Segments and Their Behavioral Patterns175 cited
  • 2018Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data164 cited
  • 2018Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making89 cited

Cited by

  • 2025Topic modelling through the bibliometrics lens and its technique32 cited
  • 2025Enhancing Customer Segmentation Through Factor Analysis of Mixed Data (FAMD)-Based Approach Using K-Means and Hierarchical Clustering Algorithms14 cited
  • 2024Customer segmentation in e-commerce: a context-aware quality model for comparing clustering algorithms12 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work