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Customer Relationship Management Using Data Mining Model

S. Balamurugan, M Selvalakshmi

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Abstract

The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right marketing promotion and services to keep them loyal. The paper discusses the number of data mining techniques with reference to customer retention for mobile phones (CART, Rule inductions, Ann etc) with a common user interface that the tool can support, an ability to support a number of different types of analysis including classification, prediction, and association detection.

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What this paper is about

The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right marketing promotion and services to keep them loyal. The paper discusses the number of data mining techniques with reference to customer retention for mobile phones (CART, Rule inductions, Ann etc) with a common user interface that the tool can support, an ability to support a number of different types of analysis including classification, prediction, and association detection.

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Available abstract

The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right marketing promotion and services to keep them loyal. The paper discusses the number of data mining techniques with reference to customer retention for mobile phones (CART, Rule inductions, Ann etc) with a common user interface that the tool can support, an ability to support a number of different types of analysis including classification, prediction, and association detection.

Key concepts: Profitability index, Blueprint, Promotion (chess), Computer science, Customer relationship management, Customer intelligence, Association rule learning, Customer lifetime value

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