Applying agglomerative fuzzy K-means to reduce the cost of telephone marketing

Ming Jia Hsu, Ping Yu Hsu, Bayarmaa Dashnyam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This research utilizes marketing research database the Taiwan telecom itself has together with Agglomerative Fuzzy K-Means to proceed fuzzy clustering analysis. The database content includes online behaviors and basic properties of clients, such as online motive, online frequency, salary, and gender. First, we use descriptive statistics to determine the difference in online behavior among different client clusters; these differences among clusters comprise indexes. Next, we compare the obtained indexes with experts' judgments to verify the precision of each index. These indexes can be used to estimate client's mobile online hours and the adaptive tariff plan. In addition, while approaching different cases, sales personnel can specifically query on significant questions within the index. Moreover, using these pre-identification indexes, prolonged question analysis, especially on illogical answers, can be avoided. This can result in time saving and increase the number of cases handled, causing an overall improvement in industry performance.

Original languageEnglish
Title of host publicationIntegrated Uncertainty in Knowledge Modelling and Decision Making - International Symposium, IUKM 2011, Proceedings
Pages197-208
Number of pages12
DOIs
StatePublished - 2011
Event2011 International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2011 - Hangzhou, China
Duration: 28 Oct 201130 Oct 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7027 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2011 International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2011
Country/TerritoryChina
CityHangzhou
Period28/10/1130/10/11

Keywords

  • Agglomerative Fuzzy K-Means
  • Clustering analysis
  • Data mining
  • Internet behavior

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