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Paper ID:IJSREM64025

A DATA-DRIVEN STUDY OF CUSTOMER SEGMENTATION IN INDIAN TEXTILE E-COMMERCE

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64025

Volume:

10 |

5 |

May 2026

Pages:

1-12

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64025

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.

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Authors

SS

SETHURATHINAM S

Master of Business Administration

Rathinam Technical Campus

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Mentors

DSM

Dr. S. MUTHULEKSHIMI

Master of Business Administration

Rathinam Technical Campus

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Abstract

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

Customer Segmentation RFM Analysis Indian Textile E-Commerce Purchasing Behaviour Loyalty Digital Marketing K-Means Clustering

Cite This Article

SETHURATHINAM S, Dr. S. MUTHULEKSHIMI, "A DATA-DRIVEN STUDY OF CUSTOMER SEGMENTATION IN INDIAN TEXTILE E-COMMERCE," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-12, May 2026. https://doi.org/10.55041/IJSREM64025

Full Article/Conclusion

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.

References

1.  Ramkumar, R., Sharma, P., & Karthik, S. (2026). Advanced RFM Analysis for Customer Segmentation in Indian Fashion E-Commerce. International Journal of Retail Analytics, Vol. 12, No. 3, pp. 45–59. 2.  Nair, S., Thomas, J., & Reddy, P. (2025). Hybrid RFM and Deep Learning Framework for Customer Churn Prediction. Journal of E-Commerce Analytics, Vol. 14, No. 2, pp. 91–108. 3.   Wakhare, P. (2025). AI-Based Customer Segmentation and Retail Marketing Strategies in Indian E-Commerce. Asian Journal of Digital Commerce, Vol. 11, No. 1, pp. 62–79. 4.  Giri, A., & Sharma, V. (2025). Digital Transformation Platforms in India's Textile Industry. Indian Journal of Business Transformation, Vol. 10, No. 3, pp. 74–92. 5.   Wong, L., Tong, K., & Haw, M. (2025). Customer Segmentation Using RFM Analysis and Hierarchical Clustering. International Journal of Data Science and Retail Management, Vol. 13, No. 2, pp. 57–75. 6.  Serwah, K., Mensah, D., & Arthur, P. (2024). Intelligent Customer Segmentation Using Bisecting K-Means Algorithm. Journal of Data Mining and Business Intelligence, Vol. 6, No. 1, pp. 81–99. 7.  Thakur, N., & Sankala, P. (2024). Online Purchase Intention and E-Cart Abandonment among Small-Town Consumers in India. International Journal of Consumer Studies, Vol. 15, No. 2, pp. 28–46. 8.  Dixit, R., Choudhary, S., & Govil, M. (2024). Factors Influencing Consumer Buying Behaviour in Online Shopping. Indian Journal of Retail and Marketing Management, Vol. 12, No. 4, pp. 39–57. 9.  Rungruang, P., Charoen, T., & Lim, S. (2023). RFM-Based Customer Segmentation Using Formal Concept Analysis. Expert Systems with Applications, Vol. 213, pp. 1–17. 10.  Li, H., Hu, Y., & Ye, Z. (2023). Customer Segmentation and Personalized Marketing Strategy Based on Machine Learning. Journal of Digital Economy and Commerce, Vol. 9, No. 1, pp. 70–88.

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Publication Date

5/26/2026

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