The strategic importance of data mining analysis for customer-centric marketing strategies
The strategic importance of data mining analysis for customer-centric marketing strategies
The main challenge for companies is to identify accurate models and methods to predict winning competitive strategies. Data mining is becoming an astonishing approach for data analysis because the meaningful knowledge is often hidden in enormous databases, and most traditional statistical methods could fail to uncover such knowledge. An efficient development of the customer relationship management and the data mining is the vital resource to collect and to manage this knowledge. The purpose of this chapter is to demonstrate the strong relationship between data mining and customer relationship management in order to forecast customer-centric marketing strategies. The last part of this chapter shows the results of an empirical study related to the identification of the main marketing and financial activities that could be leading customers in a credit-risk state. This study focuses the attention on the logistic regression model and on the criteria based on the loss function.
9781466625242
126-148
Veglio, V
a25dedee-3686-47be-a4cc-87a6d91cad7d
November 2012
Veglio, V
a25dedee-3686-47be-a4cc-87a6d91cad7d
Veglio, V
(2012)
The strategic importance of data mining analysis for customer-centric marketing strategies.
In,
Kaufmann, H.R and Ali Khan Panni, M.F
(eds.)
Customer Centric Marketing Strategies: Tools for Building Organizational Performance.
(Premier Reference Source)
Hershey, US.
IGI Global, .
(doi:10.4018/978-1-4666-2524-2).
Record type:
Book Section
Abstract
The main challenge for companies is to identify accurate models and methods to predict winning competitive strategies. Data mining is becoming an astonishing approach for data analysis because the meaningful knowledge is often hidden in enormous databases, and most traditional statistical methods could fail to uncover such knowledge. An efficient development of the customer relationship management and the data mining is the vital resource to collect and to manage this knowledge. The purpose of this chapter is to demonstrate the strong relationship between data mining and customer relationship management in order to forecast customer-centric marketing strategies. The last part of this chapter shows the results of an empirical study related to the identification of the main marketing and financial activities that could be leading customers in a credit-risk state. This study focuses the attention on the logistic regression model and on the criteria based on the loss function.
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Published date: November 2012
Organisations:
Centre for Relational Leadership & Change
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Local EPrints ID: 366379
URI: http://eprints.soton.ac.uk/id/eprint/366379
ISBN: 9781466625242
PURE UUID: 9f9f0fd4-8980-48b6-8827-ffcbb26b5ca7
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Date deposited: 01 Jul 2014 15:56
Last modified: 14 Mar 2024 17:07
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Contributors
Author:
V Veglio
Editor:
H.R Kaufmann
Editor:
M.F Ali Khan Panni
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