Wearable Item Selection Grid for Subscription Bias
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Solution Overview
Problem
Rental clothing subscription services face challenges in optimizing wearable item selection to maximize user satisfaction and business objectives, often favoring high-quality garments for satisfied users while neglecting dissatisfied ones, leading to biased selection decisions.
Innovation Solution
A computer-implemented method that generates a grid based on historical data to determine average usage and predictive wearability metrics, converts these metrics into 'squashed' metrics to account for user sensitivity, and assigns items based on both usage and satisfaction levels, ensuring a balanced selection across user segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wearable items are selected based on predictive wearability metrics to maximize overall wearability, then high-quality garments are shipped to satisfied users, but dissatisfied users remain dissatisfied and are neglected
Solution Approach 1:
The patent segments users into different categories based on their wearability history (e.g., satisfied users vs. dissatisfied users). This segmentation allows the system to apply different selection strategies to different user groups, ensuring that dissatisfied users receive attention and improvement opportunities while satisfied users continue to receive high-quality items.
Solution Approach 2:
The patent applies local quality by tailoring the wearable item selection strategy to specific user segments. Instead of using a uniform approach for all users, the system adjusts its behavior based on individual user characteristics and historical performance, providing customized selection criteria for different user categories.
2Productivity
If the system optimizes wearable item selection to maximize overall wearability, then high-quality garments are allocated to satisfied users, but the selection becomes biased toward satisfied users
Solution Approach 1:
The patent incorporates feedback mechanisms that continuously monitor user wearability history and use this information to adjust future selections. The system learns from past performance data and adapts its selection algorithms to account for user-specific patterns, preventing bias while maximizing overall wearability through iterative improvement.
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for assigning wearable items in a subscription electronics transactions platform. For example, a method may include: generating a grid based on information regarding historically shipped wearable items, wherein the grid comprises at least a first cell and a second cell; determining an average percentage indicating how many wearable items have been used and an average predictive wearability metric for wearable items indicative of a propensity of a user to use the wearable items per number of wearable items shipped for each cell; generating a mapping configured to convert a predictive wearability metric to a squashed predictive wearability metric; and converting a first predictive wearability metric to a first squashed wearability metric based on the generated mapping.