Data mining application: Customer retention at the Port of Singapore Authority (PSA)

KianSing Ng; Huan Liu; HweeBong Kwah

Data mining application: Customer retention at the Port of Singapore Authority (PSA)

KianSing Ng, Huan Liu, HweeBong Kwah

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

`Customer retention' is an important real-world problem in many sales and services related industries today. This work illustrates how we can integrate the various techniques of data-mining, such as decision-tree induction, deviation analysis and multiple concept-level association rules to form an intuitive and novel approach to gauging customer's loyalty and predicting their likelihood of defection. Immediate action taken against these `early-warnings' is often the key to the eventual retention or loss of the customers involved.

Original language	English (US)
Title of host publication	SIGMOD Record (ACM Special Interest Group on Management of Data)
Editors	Anon
Publisher	Croatian Soc Chem Eng
Pages	522-525
Number of pages	4
Volume	27
Edition	2
State	Published - Jun 1998
Externally published	Yes
Event	Proceedings of the ACM SIGMOD International Conference on Management of Data - Seattle, WA, USA Duration: Jun 1 1998 → Jun 4 1998

Other

Other	Proceedings of the ACM SIGMOD International Conference on Management of Data
City	Seattle, WA, USA
Period	6/1/98 → 6/4/98

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Information Systems
Software

Cite this

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title = "Data mining application: Customer retention at the Port of Singapore Authority (PSA)",

abstract = "`Customer retention' is an important real-world problem in many sales and services related industries today. This work illustrates how we can integrate the various techniques of data-mining, such as decision-tree induction, deviation analysis and multiple concept-level association rules to form an intuitive and novel approach to gauging customer's loyalty and predicting their likelihood of defection. Immediate action taken against these `early-warnings' is often the key to the eventual retention or loss of the customers involved.",

author = "KianSing Ng and Huan Liu and HweeBong Kwah",

year = "1998",

month = jun,

language = "English (US)",

volume = "27",

pages = "522--525",

editor = "Anon",

booktitle = "SIGMOD Record (ACM Special Interest Group on Management of Data)",

publisher = "Croatian Soc Chem Eng",

edition = "2",

note = "Proceedings of the ACM SIGMOD International Conference on Management of Data ; Conference date: 01-06-1998 Through 04-06-1998",

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T2 - Proceedings of the ACM SIGMOD International Conference on Management of Data

AU - Ng, KianSing

AU - Liu, Huan

AU - Kwah, HweeBong

PY - 1998/6

Y1 - 1998/6

N2 - `Customer retention' is an important real-world problem in many sales and services related industries today. This work illustrates how we can integrate the various techniques of data-mining, such as decision-tree induction, deviation analysis and multiple concept-level association rules to form an intuitive and novel approach to gauging customer's loyalty and predicting their likelihood of defection. Immediate action taken against these `early-warnings' is often the key to the eventual retention or loss of the customers involved.

AB - `Customer retention' is an important real-world problem in many sales and services related industries today. This work illustrates how we can integrate the various techniques of data-mining, such as decision-tree induction, deviation analysis and multiple concept-level association rules to form an intuitive and novel approach to gauging customer's loyalty and predicting their likelihood of defection. Immediate action taken against these `early-warnings' is often the key to the eventual retention or loss of the customers involved.

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M3 - Conference contribution

VL - 27

SP - 522

EP - 525

BT - SIGMOD Record (ACM Special Interest Group on Management of Data)

A2 - Anon, null

PB - Croatian Soc Chem Eng

Y2 - 1 June 1998 through 4 June 1998

ER -

Data mining application: Customer retention at the Port of Singapore Authority (PSA)

Abstract

Other

ASJC Scopus subject areas

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