Garment Fit Evaluation via Customer Feedback on Daily Wear

Abstract

The apparel manufacturing sector is progressively employing virtual prototype demos to optimize the pre-production phase and augment correspondence with relevant parties. Connecting consumer demands with accessible sizes is still difficult, though. This paper suggests a methodology that uses natural language programming to understand customers' practical requirements through customer interactions. Customer perceptions can be incorporated into the research process to obtain important insights into the preferences of the target audience and guarantee that the clothing fulfils their expectations. This methodology facilitates precise evaluation of clothing fit, enhances client contentment, and pinpoints opportunities for enhancement. Customers' participation in research also fosters inclusivity and diversity in terms of body shapes and preferences. Through the integration of client feedback, brands can develop customized products that cultivate brand loyalty and enduring partnerships. The study used sentiment analysis and self-observation to gather extensive data for a virtual model of predicted clothing fit. The results emphasize the value of body positivity, self-acceptance, and a broad definition of beauty. Brands may challenge conventional beauty norms and increase customer happiness and confidence by providing a variety of sizes and styles. The study gathered information on factors pertaining to self-perception, personal preferences, and the assessment of garment fit. Correlations between the perception of body form and size and the choice of clothing were found using a logistic regression model. The findings offer insightful information that helps companies customize their offerings and marketing plans, outperform rivals, and retain a devoted clientele.

Keywords: Garment Fit, Customer Satisfaction, Natural Language Processing, Psychological Preferences

DOI: 10.54941/ahfe1004922