The Feasible Infrastructure of Customer Segregation using Data Mining Approach
Darshana A Naik1, Vivek K M2, Seema S3

1Darshana A Naik, Assistant Professor, Department of CSE, MSRIT, Bangalore (Karnataka), India.

2Vivek K M, Student, Department of CSE, MSRIT, Bangalore (Karnataka), India.

3Seema S, Professor, Department of CSE, MSRIT, Bangalore (Karnataka), India.

Manuscript received on 15 March 2020 | Revised Manuscript received on 23 March 2020 | Manuscript Accepted on 15 April 2020 | Manuscript published on 30 December 2020 | PP: 19-24 | Volume-1 Issue-1 December 2020 | Retrieval Number: 100.1/ijainn.A1006011121

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Abstract: The customers are the basic assets of the company and their satisfaction plays a greater role in the development of a company. Customers are potentially the future source of profits. And for classifying the customers more accurately we need to have some understanding on the history of their transactions. In our project we made use of the concept of outline data. The outline data contains the useful information related to the customers. By our approach we are using machine learning and data mining algorithms to effectively classify the customers based on their purchase activities. The algorithm classifies the customers by assigning the score based on the calculation. We sorted the classified customers and further determined the loyalty of the customers. The loyalty with respect to each customer is determined based on the factors like frequency of purchase and usage of the offers provided. The classified data can be used by the marketing teams to focus on promotions relating to the loyal customers.

Keywords: Customer Segregation; Data Mining; Outline Data; Loyalty; Offers