Research graph
References from Uncovering Customer Heterogeneity Through Behavioral Segmentation: Linking Digital Service Usage Patterns to Purchase Behavior. Local targets link to admitted publications; unresolved targets remain external evidence.
10.3390/jtaer21050155
10.3390/jtaer21050155 · External reference
10.3390/jtaer21050146
10.3390/jtaer21050146 · External reference
10.3390/jtaer21050139
10.3390/jtaer21050139 · External reference
10.1109/ctems.2018.8769171
10.1109/ctems.2018.8769171 · External reference
10.3390/su14127243
10.3390/su14127243 · External reference
An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market
10.3390/analytics2040042 · 2023 · External reference
Enhancing Segmentation: A Comparative Study of Clustering Methods
10.1109/access.2025.3550339 · 2025 · External reference
Customer Segmentation of Multiple Category Data in E-Commerce Using a Soft-Clustering Approach
10.1016/j.elerap.2010.11.002 · 2011 · External reference
10.1109/isms.2010.48
10.1109/isms.2010.48 · External reference
10.1007/978-1-4615-4651-1_4
10.1007/978-1-4615-4651-1_4 · External reference
Customer Profiling, Segmentation, and Sales Prediction Using AI in Direct Marketing
10.1007/s00521-023-09339-6 · 2024 · External reference
Psychographic and Demographic Segmentation and Customer Profiling in Mobile Fintech Services
10.1108/k-07-2023-1251 · 2025 · External reference
An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation
10.1016/j.eswa.2021.116373 · 2022 · External reference
Dynamic customer segmentation via hierarchical fragmentation-coagulation processes
10.1007/s10994-022-06276-8 · 2023 · External reference
Profile Segmentation: Clustering Approach Based on Behavioral Patterns Extracted from Mobile Phone Data
10.1177/18761364251343210 · 2025 · External reference
A Dynamic Customer Segmentation Approach by Combining LRFMS and Multivariate Time Series Clustering
10.1038/s41598-024-68621-2 · 2024 · External reference
A New Methodology for Customer Behavior Analysis Using Time Series Clustering: A Case Study on a Bank’s Customers
10.1108/k-09-2018-0506 · 2021 · External reference
Clustering Short Temporal Behaviour Sequences for Customer Segmentation Using LDA
10.1111/exsy.12250 · 2018 · External reference
Customer Segmentation in the Telecommunications Industry
2010 · External reference
Customer Mobile Behavioral Segmentation and Analysis in Telecom Using Machine Learning
10.1080/08839514.2021.2009223 · 2022 · External reference
A Comparative Dimensionality Reduction Study in Telecom Customer Segmentation Using Deep Learning and PCA
10.1186/s40537-020-0286-0 · 2020 · External reference
A new family of power transformations to improve normality or symmetry
10.1093/biomet/87.4.954 · 2000 · External reference
A new method for determining the type of distribution of plant individuals
10.1093/oxfordjournals.aob.a083391 · 1954 · External reference
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
10.1016/0377-0427(87)90125-7 · 1987 · External reference
A dendrite method for cluster analysis
1974 · External reference
A cluster separation measure
10.1109/tpami.1979.4766909 · 1979 · External reference
Comparing partitions
10.1007/bf01908075 · 1985 · External reference
A stability based method for discovering structure in clustered data
2002 · External reference
Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis
10.3390/jtaer19030081 · 2024 · External reference
TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior
10.3390/jtaer18030070 · 2023 · External reference
Predicting customer behavior in telecommunications
10.1109/mis.2004.1274911 · 2004 · External reference
Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior
10.1108/jsit-10-2016-0061 · 2017 · External reference
A Review on Customer Segmentation Methods for Personalized Customer Targeting in E-Commerce Use Cases
10.1007/s10257-023-00640-4 · 2023 · External reference
How Can Algorithms Help in Segmenting Users and Customers? A Systematic Review and Research Agenda for Algorithmic Customer Segmentation
10.1057/s41270-023-00235-5 · 2023 · External reference
10.1007/978-1-4615-4651-1_4
10.1007/978-1-4615-4651-1_4 · ExternalCitation · doi-reference
Comparing partitions
10.1007/bf01908075 · ExternalCitation · doi-reference
Customer Profiling, Segmentation, and Sales Prediction Using AI in Direct Marketing
10.1007/s00521-023-09339-6 · ExternalCitation · doi-reference
A Review on Customer Segmentation Methods for Personalized Customer Targeting in E-Commerce Use Cases
10.1007/s10257-023-00640-4 · ExternalCitation · doi-reference
Dynamic customer segmentation via hierarchical fragmentation-coagulation processes
10.1007/s10994-022-06276-8 · ExternalCitation · doi-reference
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
10.1016/0377-0427(87)90125-7 · ExternalCitation · doi-reference
Customer Segmentation of Multiple Category Data in E-Commerce Using a Soft-Clustering Approach
10.1016/j.elerap.2010.11.002 · ExternalCitation · doi-reference
An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation
10.1016/j.eswa.2021.116373 · ExternalCitation · doi-reference
A Dynamic Customer Segmentation Approach by Combining LRFMS and Multivariate Time Series Clustering
10.1038/s41598-024-68621-2 · ExternalCitation · doi-reference
How Can Algorithms Help in Segmenting Users and Customers? A Systematic Review and Research Agenda for Algorithmic Customer Segmentation
10.1057/s41270-023-00235-5 · ExternalCitation · doi-reference
Customer Mobile Behavioral Segmentation and Analysis in Telecom Using Machine Learning
10.1080/08839514.2021.2009223 · ExternalCitation · doi-reference
A new family of power transformations to improve normality or symmetry
10.1093/biomet/87.4.954 · ExternalCitation · doi-reference
A new method for determining the type of distribution of plant individuals
10.1093/oxfordjournals.aob.a083391 · ExternalCitation · doi-reference
Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior
10.1108/jsit-10-2016-0061 · ExternalCitation · doi-reference
Psychographic and Demographic Segmentation and Customer Profiling in Mobile Fintech Services
10.1108/k-07-2023-1251 · ExternalCitation · doi-reference
A New Methodology for Customer Behavior Analysis Using Time Series Clustering: A Case Study on a Bank’s Customers
10.1108/k-09-2018-0506 · ExternalCitation · doi-reference
Enhancing Segmentation: A Comparative Study of Clustering Methods
10.1109/access.2025.3550339 · ExternalCitation · doi-reference
10.1109/ctems.2018.8769171
10.1109/ctems.2018.8769171 · ExternalCitation · doi-reference
10.1109/isms.2010.48
10.1109/isms.2010.48 · ExternalCitation · doi-reference
Predicting customer behavior in telecommunications
10.1109/mis.2004.1274911 · ExternalCitation · doi-reference
A cluster separation measure
10.1109/tpami.1979.4766909 · ExternalCitation · doi-reference
Clustering Short Temporal Behaviour Sequences for Customer Segmentation Using LDA
10.1111/exsy.12250 · ExternalCitation · doi-reference
Profile Segmentation: Clustering Approach Based on Behavioral Patterns Extracted from Mobile Phone Data
10.1177/18761364251343210 · ExternalCitation · doi-reference
A Comparative Dimensionality Reduction Study in Telecom Customer Segmentation Using Deep Learning and PCA
10.1186/s40537-020-0286-0 · ExternalCitation · doi-reference
An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market
10.3390/analytics2040042 · ExternalCitation · doi-reference
TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior
10.3390/jtaer18030070 · ExternalCitation · doi-reference
Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis
10.3390/jtaer19030081 · ExternalCitation · doi-reference
10.3390/jtaer21050139
10.3390/jtaer21050139 · ExternalCitation · doi-reference
10.3390/jtaer21050146
10.3390/jtaer21050146 · ExternalCitation · doi-reference
10.3390/jtaer21050155
10.3390/jtaer21050155 · ExternalCitation · doi-reference
10.3390/su14127243
10.3390/su14127243 · ExternalCitation · doi-reference
