Customer Segmentation Using K-Means Algorithm

Anal Acharya

7 citations0/0 files storedAdvances in business strategy and competitive advantage book series

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Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

In this research paper, authors explore the application of the K-means algorithm and the elbow method in customer segmentation using machine learning techniques. The algorithm is applied to a customer dataset and effectiveness of the resulting customer segments is evaluated. The performance of K-means with other clustering techniques is compare and the impact of different input parameters on the segmentation results is studied. The findings of this study can help businesses to improve their marketing strategies by targeting specific customer segments with customized marketing campaigns.

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References

2007Introduction to Data Mining 2011Customer churn prediction in telecommunications 2011The basis of market segmentation: a critical review of literature 2013An effective hybrid learning system for telecommunication churn prediction 2013Customer Segmentation Using Clustering and Data Mining Techniques 2010Applying Data Mining to Customer Churn Prediction in an Internet Service Provider 2017Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers

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2023Mall Customer Segmentation Engine Through Clustering Analysis

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