Systems and methods for optimizing wearable item selection in electronic clothing transactions platform

Abstract

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.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method for assigning wearable items in an electronic clothing transactions platform, the method comprising:
 determining, by one or more processors, information regarding historically shipped wearable items;   utilizing, by the one or more processors, a neural network trained based on information regarding the historically shipped wearable items to determine an average percentage indicating how many wearable items have been used per number of wearable items shipped;   utilizing, by the one or more processors, the trained neural network to determine an average predictive wearability metric for wearable items indicative of a propensity of a user to use the wearable items;   obtaining, by the one or more processors, information regarding a first pairing including a first unique user identifier and a first wearable item, wherein the information comprises: (i) a first predictive wearability metric associated with the first unique user identifier and the first wearable item, and (ii) a first percentage indicating how many wearable items have been used by the first user per number of wearable items shipped to the first user;   converting, by the one or more processors, the first predictive wearability metric to a first squashed wearability metric by comparing the first predictive wearability metric to the average predictive wearability metric, and comparing the first percentage indicating how many wearable items have been used by the first user per number of wearable items shipped to the first user to the average percentage indicating how many wearable items have been used per number of wearable items shipped; and   determining, by the one or more processors, whether to assign the first wearable item to the first unique user identifier based on the first squashed wearability metric and/or the first predictive wearability metric.   
     
     
         22 . The method of  claim 21 , wherein the first predictive wearability metric indicates a propensity of a first user associated with the first unique user identifier to use the first wearable item. 
     
     
         23 . The method of  claim 21 , wherein the information regarding historically shipped wearable items includes (i) one or more categories of percentages indicating how many wearable items have been used per number of wearable items shipped, and (ii) one or more categories of predictive wearability metrics for wearable items indicative of a propensity of a user to use the wearable items. 
     
     
         24 . The method of  claim 23 , wherein each of the categories regarding the percentages comprises a range of percentages, and wherein each of the categories regarding the predictive wearability metrics comprises a range of predictive wearability metrics. 
     
     
         25 . The method of  claim 21 , further comprising:
 determining an extrapolated percentage indicating how many wearable items have been used per number of wearable items shipped; and   determining an extrapolated predictive wearability metric.   
     
     
         26 . The method of  claim 23 , further comprising:
 determining that the first predictive wearability metric falls under a category of predictive wearability metrics.   
     
     
         27 . The method of  claim 23 , further comprising:
 determining that the first percentage falls under a category of percentages indicating how many wearable items have been used per number of wearable items shipped.   
     
     
         28 . A computer system for assigning wearable items in an electronic clothing transactions platform, the computer system comprising:
 a data storage device storing processor-readable instructions; and   a processor configured to execute the instructions to perform a method including:   determining information regarding historically shipped wearable items;   utilizing a neural network trained based on information regarding the historically shipped wearable items to determine an average percentage indicating how many wearable items have been used per number of wearable items shipped;   utilizing the trained neural network to determine an average predictive wearability metric for wearable items indicative of a propensity of a user to use the wearable items;   obtaining information regarding a first pairing including a first unique user identifier and a first wearable item, wherein the information comprises: (i) a first predictive wearability metric associated with the first unique user identifier and the first wearable item, and (ii) a first percentage indicating how many wearable items have been used by the first user per number of wearable items shipped to the first user;   converting the first predictive wearability metric to a first squashed wearability metric by comparing the first predictive wearability metric to the average predictive wearability metric, and comparing the first percentage indicating how many wearable items have been used by the first user per number of wearable items shipped to the first user to the average percentage indicating how many wearable items have been used per number of wearable items shipped; and   determining whether to assign the first wearable item to the first unique user identifier based on the first squashed wearability metric and/or the first predictive wearability metric.   
     
     
         29 . The computer system of  claim 28 , wherein the first predictive wearability metric indicates a propensity of a first user associated with the first unique user identifier to use the first wearable item. 
     
     
         30 . The computer system of  claim 28 , wherein the information regarding historically shipped wearable items includes (i) one or more categories of percentages indicating how many wearable items have been used per number of wearable items shipped, and (ii) one or more categories of predictive wearability metrics for wearable items indicative of a propensity of a user to use the wearable items. 
     
     
         31 . The computer system of  claim 30 , wherein each of the categories regarding the percentages comprises a range of percentages, and wherein each of the categories regarding the predictive wearability metrics comprises a range of predictive wearability metrics. 
     
     
         32 . The computer system of  claim 28 , the method further comprising:
 determining an extrapolated percentage indicating how many wearable items have been used per number of wearable items shipped; and   determining an extrapolated predictive wearability metric.   
     
     
         33 . The computer system of  claim 30 , the method further comprising:
 determining that the first predictive wearability metric falls under a category of predictive wearability metrics.   
     
     
         34 . The computer system of  claim 30 , the method further comprising:
 determining that the first percentage falls under a category of percentages indicating how many wearable items have been used per number of wearable items shipped.   
     
     
         35 . A non-transitory computer-readable medium containing instructions that, when executed by a processor, cause the processor to perform a method comprising:
 determining information regarding historically shipped wearable items;   utilizing a neural network trained based on information regarding the historically shipped wearable items to determine an average percentage indicating how many wearable items have been used per number of wearable items shipped;   utilizing the trained neural network to determine an average predictive wearability metric for wearable items indicative of a propensity of a user to use the wearable items;   obtaining information regarding a first pairing including a first unique user identifier and a first wearable item, wherein the information comprises: (i) a first predictive wearability metric associated with the first unique user identifier and the first wearable item, and (ii) a first percentage indicating how many wearable items have been used by the first user per number of wearable items shipped to the first user;   converting the first predictive wearability metric to a first squashed wearability metric by comparing the first predictive wearability metric to the average predictive wearability metric, and comparing the first percentage indicating how many wearable items have been used by the first user per number of wearable items shipped to the first user to the average percentage indicating how many wearable items have been used per number of wearable items shipped; and   determining whether to assign the first wearable item to the first unique user identifier based on the first squashed wearability metric and/or the first predictive wearability metric.   
     
     
         36 . The non-transitory computer-readable medium of  claim 35 , wherein the first predictive wearability metric indicates a propensity of a first user associated with the first unique user identifier to use the first wearable item. 
     
     
         37 . The non-transitory computer-readable medium of  claim 35 , wherein the information regarding historically shipped wearable items includes (i) one or more categories of percentages indicating how many wearable items have been used per number of wearable items shipped, and (ii) one or more categories of predictive wearability metrics for wearable items indicative of a propensity of a user to use the wearable items. 
     
     
         38 . The non-transitory computer-readable medium of  claim 37 , wherein each of the categories regarding the percentages comprises a range of percentages, and wherein each of the categories regarding the predictive wearability metrics comprises a range of predictive wearability metrics. 
     
     
         39 . The non-transitory computer-readable medium of  claim 35 , the method further comprising:
 determining an extrapolated percentage indicating how many wearable items have been used per number of wearable items shipped; and   determining an extrapolated predictive wearability metric.   
     
     
         40 . The non-transitory computer-readable medium of  claim 37 , the method further comprising:
 determining that the first predictive wearability metric falls under a category of predictive wearability metrics; and   determining that the first percentage falls under a category of percentages indicating how many wearable items have been used per number of wearable items shipped.