Knowledge Discovery For CRM Improvement
Author(s)
G. M. Caputo, V. M. Bastos, A. M. Cister & N. F. F. Ebecken
Abstract
The objective of this paper is to present a database marketing analysis through
data and text mining tools. A case study of a Brazilian Power Energy distribution
was developed indoors. The main idea is to transform the database information
into strategic marketing knowledge. Thus a data warehouse sample was treated,
reduced and clustered. Principal component analysis was used to reduce the
original number of variables. The entire database was classified after creation by
the decision trees and neural networks approach. In this work, text mining
techniques were used to process customers’ claims in order to improve cluster
results. The CRM group has developed a powerful tool to gather knowledge
regarding the skills and habits of customers, thereby gaining their confidence and
loyalty.
Keywords: data mining, text mining, clustering, CRM.
1 Introduction
This work shows a methodology to extract knowledge of customer habits from
an electric energy company database, in order to discover useful information that
will serve as base for CRM implementation in the company.
For this, segmentation variables have been used that identify partnereconomic
characteristics, behaviors and customer invoicing data, which
represent the customer profile for the company.
The set of initial variables extracted from the company Data Warehouse
involved a great number of dimensions, with many forms of customers’ segment
identification. Through the data extraction process, it was possible to get samples
with good universal representation, allowing the application of knowledge
extraction techniques, in order to identify customers’ behavior.
Keywords
data mining, text mining, clustering, CRM.
Related Book
Other papers in this volume
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