Earticle

초록

영어

The rapid expansion of online apparel retail has increased the demand for accurate size recommendations that minimize returns and enhance customer satisfaction. This study presents a data-driven analysis of a bioelectrical impedance analysis (BIA)–based size recommendation system implemented on a live e-commerce platform. Using anonymized transaction and feedback data from Boastfit.com, the research compares behavioral and perceptual outcomes between BIA-based recommendations and conventional size guides. The BIA group recorded a return rate of 9.6 percent compared with 17.2 percent in the control group, an average satisfaction score above 8 on a ten-point scale, and a repurchase ratio of 79 percent. These results confirm that physiological data–driven personalization improves predictive accuracy, post-purchase satisfaction, and repurchase intention. The findings contribute to next-generation computing and fashion retail analytics by demonstrating how body-composition data can be integrated into intelligent recommendation systems to enhance user trust and sustainable engagement.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
III. RESULTS AND DISCUSSION
A. Return Rate Comparison
B. Customer Satisfaction and Repurchase Intention
C. Summary of Key Outcomes
D. Discussion and Implications
IV. CONCLUSION
REFERENCES

키워드

저자

• Wookwhan Jung [ Department of Data Science Dankook University Yongin, South Korea/Department of Fashion Industry Hansung University Seoul, South Korea ]
• Tae-Hyung Kim [ Department of Data Science Dankook University Yongin, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004