AI Size Fit Prediction for Apparel Retailers
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Solution Overview
Problem
Online clothing retailers face challenges in predicting the perfect fit for customers due to inconsistent sizing across brands and styles, leading to time-consuming exchange processes and difficulties in selecting appropriate sizes without physical measurements.
Innovation Solution
The implementation of a process using artificial intelligence and machine learning to determine a predicted size fit by training models with customer sizing information and item feedback, allowing for personalized size predictions and dynamic adjustment of custom sizes based on customer feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sizing methods are used where customers order their expected size, then the ordering process is simple, but the fit accuracy deteriorates leading to multiple exchanges
Solution Approach 1:
The system performs preliminary sizing analysis by collecting customer feedback on fit and size for multiple items, then trains machine learning models in advance to predict the optimal size for future purchases. This preliminary modeling action enables accurate size prediction before the customer makes a purchase decision, eliminating the need for time-consuming exchanges later.
Solution Approach 2:
The system implements a feedback loop where customer sizing information and fit feedback from previous purchases are continuously collected and used to retrain and improve the machine learning models. This feedback mechanism enhances prediction accuracy over time, reducing exchange rates as the system learns from actual customer experiences.
2Measurement precision
If professional human stylists assist in size selection, then size prediction accuracy may improve, but the operational complexity and cost increase
Solution Approach 1:
The system enables self-service sizing by allowing customers to input their own size information and receive automated size predictions based on machine learning models. The system autonomously processes sizing data, trains models, and generates predictions without requiring human stylist intervention, thereby maintaining high accuracy while reducing operational complexity.
Solution Approach 2:
The patent replaces the mechanical system of human stylists with an automated machine learning system. The ML models process sizing data, learn from feedback, and generate size predictions algorithmically, substituting human judgment with computational intelligence that scales without increasing operational complexity.
3Adaptability or versatility
If inventory is maintained in multiple sizes to ensure availability, then customer satisfaction improves, but inventory costs and complexity increase
Solution Approach 1:
The system changes the parameter of size selection from a static customer-chosen value to a dynamically optimized value based on machine learning predictions. By adjusting the size parameter according to predicted fit accuracy, the system ensures optimal size availability while reducing the need to maintain excessive inventory across all possible sizes.
Data Source
AI summary
A predicted size of a specific subject and a predicted size of a specific item are determined using one or more machine learning models. The machine learning models are trained using at least a specified size of the specific subject, feedback of the specific subject regarding sizing of a plurality of items, and feedback of other subjects regarding sizing of the plurality of items. The determined predicted size of the specific subject and the predicted size of the specific item are used to determine a predicted size fit between the specific item and the specific subject.