Systems and methods for digital shelf display

Abstract

The present disclosure provides methods and systems for quantifying item performance in a digital shelf. A method for quantifying item performance in a digital shelf may comprise: calculating a value associated with a shelf share of the given item; determining a set of factors for calculating a score indicative of the item performance on the digital shelf, wherein the set of factors includes the shelf share ;generating, using a trained machine learning algorithm, the score based on the set of factors; and displaying the score within a graphical user interface (GUI) on an electronic device.

1 . (canceled) 
     
     
         2 . A computer-implemented method for quantifying and searching performance of an item, the method comprising:
 (a) receiving, via a graphical user interface (GUI), a user input for searching performance of a given item on a digital shelf provided via an online platform;   (b) upon receiving the user input, generating a composite score indicative of the performance of the given item on the digital shelf, wherein the composite score is generated based at least in part on a shelf share value of the given item and wherein the shelf share value is calculated based at least in part on searches conducted for the given item and searches conducted for a similar item within the online platform or across one or more other online platforms; and   (c) rendering, on the GUI, graphical elements corresponding to a plurality of compositions of the composite score indicative of performance of the given item in the plurality of compositions and an overall score indicative of an overall performance of the given item.   
     
     
         3 . The computer-implemented method of  claim 2 , wherein the plurality of compositions of the composite score comprise one or more of the members selected from the group consisting of product positioning, customer response, and brand presence. 
     
     
         4 . The computer-implemented method of  claim 3 , further comprising generating a recommendation to improve a score of respective composition of the plurality of compositions. 
     
     
         5 . The computer-implemented method of  claim 4 , wherein the recommendation comprises at least one of recommended keyword or search terms of the given item, recommended description of the given item, recommended presentation of the given item in the digital shelf, and recommended price. 
     
     
         6 . The computer-implemented method of  claim 4 , wherein the recommendation is generated by a model trained using machine learning algorithm. 
     
     
         7 . The computer-implemented method of  claim 6 , wherein the model is continuously trained using newly acquired data. 
     
     
         8 . The computer-implemented method of  claim 2 , wherein the composite score is generated based on a set of factors including at least one of price, ratings, position within a catalog, and packaging quality. 
     
     
         9 . The computer-implemented method of  claim 2 , wherein the shelf share value is calculated based on one or more factors with dynamically assigned weights. 
     
     
         10 . The computer-implemented method of  claim 9 , wherein the weights are assigned based at least in part on a category of the given item, marketplace, seasonality, geography, user device, or consumer experience. 
     
     
         11 . The computer-implemented method of  claim 2 , wherein the shelf share value is calculated further based on a time of the searches conducted for the given item. 
     
     
         12 . A non-transitory computer-readable medium comprising machine-executable instructions, that, upon execution by one or more computer processors, implements a method for quantifying and searching performance of an item, the method comprising:
 (a) receiving, via a graphical user interface (GUI), a user input for searching performance of a given item on a digital shelf provided via an online platform;   (b) upon receiving the user input, generating a composite score indicative of the performance of the given item on the digital shelf, wherein the composite score is generated based at least in part on a shelf share value of the given item and wherein the shelf share value is calculated based at least in part on searches conducted for the given item and searches conducted for a similar item within the online platform or across one or more other online platforms; and   (c) rendering, on the GUI, graphical elements corresponding to a plurality of compositions of the composite score indicative of performance of the given item in the plurality of compositions and an overall score indicative of an overall performance of the given item.   
     
     
         13 . The non-transitory computer-readable medium of  claim 12 , wherein the plurality of compositions of the composite score comprise one or more of the members selected from the group consisting of product positioning, customer response, and brand presence. 
     
     
         14 . The non-transitory computer-readable medium of  claim 13 , wherein the method further comprises generating a recommendation to improve a score of respective composition of the plurality of compositions. 
     
     
         15 . The non-transitory computer-readable medium of  claim 14 , wherein the recommendation comprises at least one of recommended keyword or search terms of the given item, recommended description of the given item, recommended presentation of the given item in the digital shelf, and recommended price. 
     
     
         16 . The non-transitory computer-readable medium of  claim 14 , wherein the recommendation is generated by a model trained using machine learning algorithm. 
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein the model is continuously trained using newly acquired data. 
     
     
         18 . The non-transitory computer-readable medium of  claim 12 , wherein the composite score is generated based on a set of factors including at least one of price, ratings, position within a catalog, and packaging quality. 
     
     
         19 . The non-transitory computer-readable medium of  claim 12 , wherein the shelf share value is calculated based on one or more factors with dynamically assigned weights. 
     
     
         20 . The non-transitory computer-readable medium of  claim 19 , wherein the weights are dynamically assigned based at least in part on a category of the given item, marketplace, seasonality, geography, user device, or consumer experience. 
     
     
         21 . The non-transitory computer-readable medium of  claim 12 , wherein the shelf share value is calculated further based on a time of the searches conducted for the given item.