Research Paper on Customer Segmentation Using K-Means Clustering And RFM Analysis

Abstract— Customer segmentation, a crucial step in data driven marketing and personalization, entails breaking up a customer base into discrete groups that share characteristics or behaviors. Simple demographic divisions, like age or income, have given way to more intricate behavioral and psychographic groupings, influenced by preferences, lifestyle choices, and purchasing patterns, as businesses work to better understand their clientele. Businesses can now create highly refined customer segments using advanced analytical techniques and vast amounts of data thanks to technological advancements. These segments inform personalized customer journeys, product recommendations, and targeted marketing campaigns. This section talks about the increasing importance of customer segmentation and how machine learning has increased the range of possible uses for it, allowing businesses to make well-informed decisions supported by data.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-06-13
DOI
https://doi.org/10.55041/ijsrem50159
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
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article

Research Paper on Customer Segmentation Using K-Means Clustering And RFM Analysis

Mrs. S. N. Firame
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Customer churn and segmentation
article

Research Paper on Customer Segmentation Using K-Means Clustering And RFM Analysis

Mrs. S. N. Firame
article en

Abstract

Abstract— Customer segmentation, a crucial step in data driven marketing and personalization, entails breaking up a customer base into discrete groups that share characteristics or behaviors. Simple demographic divisions, like age or income, have given way to more intricate behavioral and psychographic groupings, influenced by preferences, lifestyle choices, and purchasing patterns, as businesses work to better understand their clientele. Businesses can now create highly refined customer segments using advanced analytical techniques and vast amounts of data thanks to technological advancements. These segments inform personalized customer journeys, product recommendations, and targeted marketing campaigns. This section talks about the increasing importance of customer segmentation and how machine learning has increased the range of possible uses for it, allowing businesses to make well-informed decisions supported by data.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(06)
Openalex Percentile: Top 83%
Customer churn and segmentation
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