Method and system for selection, filtering or presentation of available sales outlets

Abstract

Embodiments disclosed herein provide systems and methods for the filtering, selection and presentation of vendors accounting for both user characteristics and vendor characteristics, such that the systems and methods may be used by both customer and vendor alike to better match customer needs with the resource-constrained vendors with whom a successful sale has a higher probability of occurring. Embodiments may include filtering, selecting and/or presenting vendors to a user sorted by the probability that the particular vendor will possess the characteristics that appeal to a particular customer and therefore result in a large probability of sale and suppress presentation of those vendors that are unlikely to be selected by the customer since their characteristics are less consistent with those needed by the customer and, therefore, are unlikely to result in a sale.

What is claimed is: 
     
         1 . A method, comprising:
 determining, by a computer for each of a plurality of sales outlets, a probability of closing a sale (P c ), wherein P c  is a function of P s  and P b , wherein P s  represents a probability of a specific sales outlet selling a product to a user interested in purchasing the product given that the sales outlet is presented to the user and wherein P b  represents a probability of the user buying the product from the sales outlet given a historical preference of the user;   selecting, by the computer based at least in part on the probability of closing (P c ), a list of sales outlets from the plurality of sales outlets; and   presenting, by the computer to an interested consumer via a Web application, the list of sales outlets on a user device associated with the interested consumer.   
     
     
         2 . The method according to  claim 1 , further comprising:
 receiving, via the Web application, a request from the interested consumer interested in purchasing the product, wherein the presenting is performed in response to the request from the interested consumer.   
     
     
         3 . The method according to  claim 1 , wherein P c  is determined based at least in part on features of a plurality of feature types, wherein the features of the plurality of feature types include features of a first feature type describing individual sales outlet (X i,t ), features of a second feature type describing X i,t  compared to other sales outlets (X i,t,S ), features of a third feature type describing individual customer (Y c,t ), and features of a fourth feature type describing the interactions of a particular customer and a particular sales outlet (Y c,i ). 
     
     
         4 . The method according to  claim 3 , wherein the features of the plurality of feature types include at least a historical close rate of an individual sales outlet, a product price by the individual sales outlet, an inventory by the individual sales outlet, or a drive time between an individual customer and the individual sales outlet. 
     
     
         5 . The method according to  claim 4 , wherein the historical close rate is determined based on a measure of historical performance of the individual sales outlet in a period of time, and wherein the measure of historical performance is defined as a number of sales by the individual sales outlet in a locality over the period of time, the locality associated with a customer search parameter of the Web application. 
     
     
         6 . The method according to  claim 5 , wherein the period of time is 45 days. 
     
     
         7 . The method according to  claim 4 , wherein the historical close rate is determined based on counts of sales and leads within 60 miles drive distance of the individual sales outlet, a designated market area average, or a geographic boundary average. 
     
     
         8 . A system, comprising:
 a processor;   a non-transitory computer-readable medium; and   stored instructions translatable by the processor for:
 determining, for each of a plurality of sales outlets, a probability of closing a sale (P c ), wherein P c  is a function of P s  and P b , wherein P s  represents a probability of a specific sales outlet selling a product to a user interested in purchasing the product given that the sales outlet is presented to the user and wherein P b  represents a probability of the user buying the product from the sales outlet given a historical preference of the user; 
 selecting, based at least in part on the probability of closing (P c ), a list of sales outlets from the plurality of sales outlets; and 
 presenting, to an interested consumer via a Web application, the list of sales outlets on a user device associated with the interested consumer. 
   
     
     
         9 . The system of  claim 8 , wherein the stored instructions are further translatable by the processor for:
 receiving, via the Web application, a request from the interested consumer interested in purchasing the product, wherein the presenting is performed in response to the request from the interested consumer.   
     
     
         10 . The system of  claim 8 , wherein P c  is determined based at least in part on features of a plurality of feature types, wherein the features of the plurality of feature types include features of a first feature type describing individual sales outlet (X i,t ), features of a second feature type describing X i,t  compared to other sales outlets (X i,t,s ), features of a third feature type describing individual customer (Y c,t ), and features of a fourth feature type describing the interactions of a particular customer and a particular sales outlet (Y c,i ). 
     
     
         11 . The system of  claim 10 , wherein the features of the plurality of feature types include at least a historical close rate of an individual sales outlet, a product price by the individual sales outlet, an inventory by the individual sales outlet, or a drive time between an individual customer and the individual sales outlet. 
     
     
         12 . The system of  claim 11 , wherein the historical close rate is determined based on a measure of historical performance of the individual sales outlet in a period of time, and wherein the measure of historical performance is defined as a number of sales by the individual sales outlet in a locality over the period of time, the locality associated with a customer search parameter of the Web application. 
     
     
         13 . The system of  claim 12 , wherein the period of time is 45 days. 
     
     
         14 . The system of  claim 11 , wherein the historical close rate is determined based on counts of sales and leads within 60 miles drive distance of the individual sales outlet, a designated market area average, or a geographic boundary average. 
     
     
         15 . A computer program product comprising a non-transitory computer-readable medium storing instructions translatable by a processor to perform:
 determining, for each of a plurality of sales outlets, a probability of closing a sale (P c ), wherein P c  is a function of P s  and P b , wherein P s  represents a probability of a specific sales outlet selling a product to a user interested in purchasing the product given that the sales outlet is presented to the user and wherein P b  represents a probability of the user buying the product from the sales outlet given a historical preference of the user;   selecting, based at least in part on the probability of closing (P c ), a list of sales outlets from the plurality of sales outlets;   receiving, via a Web application, a request from an interested consumer interested in purchasing the product; and   in response to the request from the interested consumer, presenting, to the interested consumer through the Web application, the list of sales outlets on a user device associated with the interested consumer.   
     
     
         16 . The computer program product of  claim 15 , wherein P c  is determined based at least in part on features of a plurality of feature types, wherein the features of the plurality of feature types include features of a first feature type describing individual sales outlet (X i,t ), features of a second feature type describing X i,t  compared to other sales outlets (X i,t,S ), features of a third feature type describing individual customer (Y c,t ), and features of a fourth feature type describing the interactions of a particular customer and a particular sales outlet (Y c,i ). 
     
     
         17 . The computer program product of  claim 16 , wherein the features of the plurality of feature types include at least a historical close rate of an individual sales outlet, a product price by the individual sales outlet, an inventory by the individual sales outlet, or a drive time between an individual customer and the individual sales outlet. 
     
     
         18 . The computer program product of  claim 17 , wherein the historical close rate is determined based on a measure of historical performance of the individual sales outlet in a period of time, and wherein the measure of historical performance is defined as a number of sales by the individual sales outlet in a locality over the period of time, the locality associated with a customer search parameter of the Web application. 
     
     
         19 . The computer program product of  claim 18 , wherein the period of time is 45 days. 
     
     
         20 . The computer program product of  claim 17 , wherein the historical close rate is determined based on counts of sales and leads within 60 miles drive distance of the individual sales outlet, a designated market area average, or a geographic boundary average.