Applying Data Mining Procedures On A Customer Relationship Management System

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The purpose of this document is to present a proposal for applying data mining procedures on a Customer Relationship Management System of a company to reduce Churn Rate and identify valuable customer termed in this document as optimal customers. The constantly updated database of the company will be used as the source of the data for the analysis purpose.

Exploiting the customer information hidden in large database can help identify valuable customers and predict future behavior, enable the company to become proactive with their campaign, implement knowledge-driven decisions and make it possible for the organization to limit the defect rate.

The aims and objectives will be discussed in this document in regards to the various data mining procedures that will be applied. Next the background section will shed light into the existing technologies being used in the CRM domain. Continuing on this document will present the CRISP-DM methodology based process that will involve Business understanding, Data understanding, Data preparation, Modeling, Evaluation
Towards the end of the document, how the results will be evaluated and deployed will be discussed followed by a brief project deployment plan and conclusion.

2. Aims, Objectives and Possible Outcomes
2.1 Aims
The key aim of implementing data analytics techniques on a Customer Relationship Management system is to increase profitability of an organization by reducing the churn rate and identify key customers.


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