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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64025
Volume:
10 |
5 |
May 2026
Pages:
1-12
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Business Administration
Rathinam Technical Campus
Master of Business Administration
Rathinam Technical Campus
The rapid growth of digital commerce has fundamentally transformed the Indian textile industry, creating an increasingly heterogeneous consumer base that demands more targeted and personalised business strategies. This study investigates customer segmentation in the Indian textile e-commerce sector through a data-driven analytical framework encompassing RFM (Recency, Frequency, Monetary) analysis, descriptive statistics, frequency distribution, cross-tabulation, and chi-square testing. Primary data were collected from 120 respondents through a structured questionnaire distributed across various regions of India. Five distinct customer segments were identified — Champions (14.2 Percent), Loyal Customers (44.2 Percent), Potential Loyalists (27.5 Percent), At-Risk Customers (10.8 Percent), and Lost/Dormant Customers (3.3 Percent). The study reveals that Myntra is the most preferred platform (34.2 Percent), ethnic wear is the most purchased category (20.8 Percent), and slow delivery (20.8 Percent) constitutes the primary barrier. Overall customer satisfaction stands at 65.0 percent positive. The study concludes that data-driven segmentation provides actionable strategic insights for textile e-commerce organisations to optimise marketing investment, improve customer retention, and build competitive advantage.
KEYWORDS
The present study — A Data-Driven Study of Customer Segmentation in Indian Textile E-Commerce — has successfully applied RFM analysis, descriptive statistics, cross-tabulation, chi-square testing, and ranking analysis to primary data from 120 respondents to generate a rich and actionable understanding of consumer segments in India's online textile market. The study was conducted over four months from January to April 2025 and drew upon both primary survey data and secondary industry sources. The RFM-based segmentation identified five distinct customer groups: Champions (14.2 Percent), Loyal Customers (44.2 Percent), Potential Loyalists (27.5 Percent), At-Risk Customers (10.8 Percent), and Lost/ Dormant Customers (3.3 Percent). The dominance of the Loyal Customer segment signals a substantial base of consistently engaged buyers, while the Champions cohort represents the highest-priority group for retention and advocacy investment. Potential Loyalists present the most significant strategic growth opportunity, and At-Risk Customers require immediate reactivation strategies to prevent further disengagement. Key findings confirm that the Indian online textile consumer base is predominantly young, mobile-native, and increasingly geographically diverse. Myntra leads platform preference, ethnic wear dominates product category demand, and product quality and price competitiveness are the top purchase decision factors. Slow delivery, fabric quality assessment limitations, and high prices remain the most critical barriers requiring operational resolution. Overall, 65.0 percent of respondents report positive satisfaction with their online textile shopping experience, reflecting the significant progress made by the sector while also underscoring room for further service quality improvement. In conclusion, this study establishes that data-driven customer segmentation is not merely an analytical exercise but a fundamental strategic capability for textile e-commerce organisations operating in one of the world's most dynamic and culturally diverse consumer markets. The insights generated by this study provide a practical roadmap for organisations seeking to optimise marketing investment, improve customer retention, and build enduring competitive advantage in the Indian textile e-commerce ecosystem.
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Publication Date
5/26/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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