Customer segmentation using K - Means Algorithm

Arjun Bijili Gopakumar; Gautam Krishnan; Nitha L Rozario; Remijius Brian Nazreth; Pooja Anilkumar

Customer segmentation using K - Means Algorithm

Authors: Arjun Bijili Gopakumar, Gautam Krishnan, Nitha L Rozario, Remijius Brian Nazreth , Pooja Anilkumar

Unique Paper ID: 154245
Volume: 8
Issue: 7
PageNo: 127-133

Keywords: Customer segmentation K means Algorithm RFM model

Abstract:
Customer Segmentation is often said as the subdivision of a market into customer groups that can share similar characteristics or features. It can be a powerful means to identify unsatisfied customer needs. In todayâ€™s world businesses run based on innovation and having the ability to capture the fascinated attention of customers with a large variety of products. But such a large raft of products can leave the customers confused, what to buy and what not to. Even the companies are nonplussed about what section of customers to target to sell their products. This is where machine learning comes into play. Various algorithms are applied to unravel hidden patterns to make data for better decision-making for the future. Most customer segmentation approaches based on customer value fail to look for the factor of time and the trend of value changes in their analysis. Here, we classify customers using the RFM model and K-means clustering method. An assessment of changes over several intervals of time is administered and customer value for each time interval is calculated. A python program has been developed and the program is being trained using the K means Algorithm by applying MinMax scaler for normalization onto a dataset having shopping details and trends of customers in a shopping mall. This research is based on the time and trend of customer value changes for improving the accuracy of predictions and will be based on the past behavior of customers.

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Cite This Article

ISSN: 2349-6002
Volume: 8
Issue: 7
PageNo: 127-133

Customer segmentation using K - Means Algorithm

Available:https://ijirt.org/Article?manuscript=154245

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