Achieving Effective Customer Relationship using Frequent Pattern-Growth Algorithm Association Rule Learning Technique

Authors

  • NNAEMEKA VIRGINUS UGWU Department of Computer Science, University of Nigeria Nsukka, Enugu State, NIGERIA
  • C. N. Udanor Department of Computer Science, University of Nigeria Nsukka, Enugu State, NIGERIA

Keywords:

customer relationship management, association rule, data mining, frequency pattern algorithm

Abstract

Customer relationship management (CRM) is a methodology and tool that possesses the plan and techniques that companies should follow in relating with their customers. In today’s business world, the relationship between organizations and their customers is very important in engaging the customers’ interest, which has the direct effect in increasing the business profit. Due to ineffective collaboration and interaction between organizations and their customers, identifying who the real customers are, along with what they need has failed. A breach of trust from the company, and lack of feedback from the customer could make an organization not to be able to compete with her rivals in a business environment and win her customers’ loyalty. Therefore, the guarantee of the customer continuing transactions with the company may no longer be assured. This work deploys an association rule learning technique of data mining using frequent pattern growth algorithm to identify which items are regularly purchased together by customers and based on this result, analyzes and understands the customers’ buying habits. Object-Oriented Analysis and Design methodology (OOAD) is used to analyze and design the system, whereas the implementation is carried out using Python programming language and My-SQL database management system. The contribution of this work is that it enables firms to gather and analyze customers’ interests in a product so that the needs of their valued customers will be met in order to make them return for more business transactions, thereby achieving customer retention.

http://dx.doi.org/10.4314/njt.v40i2.19

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Published

2021-04-28

Issue

Section

Computer, Telecommunications, Software, Electrical & Electronics Engineering