Mailing decisions in the catalog sales industry
Management Science
Data preparation for data mining
Data preparation for data mining
ACM Computing Surveys (CSUR)
Ideal patterns of strategic alignment and business performance
Information and Management
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
The language of quarterly reports as an indicator of change in the company's financial status
Information and Management
An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
A comparison of the behavior of different customer clusters towards Internet bookstores
Information and Management
Journal of Management Information Systems
Journal of Management Information Systems
Weighted order-dependent clustering and visualization of web navigation patterns
Decision Support Systems
Correlated pattern mining in quantitative databases
ACM Transactions on Database Systems (TODS)
Detection of the customer time-variant pattern for improving recommender systems
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Customer segmentation of multiple category data in e-commerce using a soft-clustering approach
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
A multi-criteria network-aware service composition algorithm in wireless environments
Computer Communications
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We use customer clustering to explore the behavioral patterns of customers who subscribe to mobile services. Two clustering techniques, K-means and KVQ, are used to cluster customers using knowledge about attributes that are broadly grouped under usage, revenue, services, and user categories. We used inter-cluster analysis on the clusters generated from the two techniques to compare the distribution of customers among the different categories of attributes. We observed that it was important to use multiple techniques for clustering. Our analysis discovered several interesting facts about customers, such as the imbalance between customers' usage of mobile services, subscriptions to services, and revenue contributions. These knowledge nuggets could enable mobile service providers to better align their marketing strategies with the needs of customers.