System and a method for identifying prospects with a buying intent and connecting them with relevant businesses

Abstract

A system and method for identifying prospects with a buying intent and connecting them with relevant businesses. An example method may comprise obtaining a training dataset; applying a first scoring algorithm to obtain a first score for each entry in the training dataset; receiving one or more scores from a user for one or more entries in the training dataset; rescoring the training dataset based on the one or more scores received from the user; creating a deep learning model based on the rescored dataset; obtaining digital media posts comprising data from one or more digital media platforms; scoring each received digital media post by using the deep learning model; providing certain scored digital media posts to the user; receiving a second score from the user; and updating the deep learning model based on the second score.

What is claimed is: 
     
         1 . A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:
 obtain a training dataset;   apply a first scoring algorithm to obtain a first score for each entry in the training dataset, the first score indicating the likelihood that a prospective customer will buy an offering;   receive one or more scores from a user for one or more entries in the training dataset;   rescore the training dataset based on the one or more scores received from the user;   create a deep learning model based on the rescored dataset, the deep learning model comprising a library of words, phrases, and contextual relationship between one or more items in the library indicating a likelihood that a prospective customer will purchase an offering;   obtain digital media posts comprising data from one or more digital media platforms;   score each received digital media post by using the deep learning model, the scores indicating the likelihood that a customer will purchase an offering;   provide certain scored digital media posts to the user;   receive a second score from the user, the second score indicating the likelihood that a customer will buy an offering based on the content of the digital media post; and   update the deep learning model based on the second score.   
     
     
         2 . A method comprising:
 obtaining a training dataset;   applying a first scoring algorithm to obtain a first score for each entry in the training dataset, the first score indicating the likelihood that a prospective customer will buy an offering;   receiving one or more scores from a user for one or more entries in the training dataset;   rescoring the training dataset based on the one or more scores received from the user;   creating a deep learning model based on the rescored dataset, the deep learning model comprising a library of words, phrases, and contextual relationship between one or more items in the library indicating a likelihood that a prospective customer will purchase an offering;   obtaining digital media posts comprising data from one or more digital media platforms;   scoring each received digital media post by using the deep learning model, the scores indicating the likelihood that a customer will purchase an offering;   providing certain scored digital media posts to the user;   receiving a second score from the user, the second score indicating the likelihood that a customer will buy an offering based on the content of the digital media post; and   updating the deep learning model based on the second score.   
     
     
         3 . The method of  claim 2 , wherein the first scoring algorithm uses pre-identified keywords and deep learning to identify context suggesting a purchase intent associated with each entry, including whether the entry indicates a need for an offering, whether the need is immediate, or whether the entry requests a recommendation. 
     
     
         4 . The method of  claim 2 , wherein the one or more digital media platforms comprise one or more of digital social media platforms, digital message boards, and digital review collection systems. 
     
     
         5 . The method of  claim 2 , wherein obtaining digital media posts comprises using one or more of application programming interfaces (APIs) associated with one or more digital media platforms, and screen scraping and text recognition technologies. 
     
     
         6 . The method of  claim 2 , wherein the user is provided scored digital media posts only if the posts are scored as having a high likelihood that a potential customer will purchase an offering. 
     
     
         7 . The method of  claim 2 , wherein the deep learning model is updated with the second score if the second score indicates a low likelihood that a potential customer will purchase an offering. 
     
     
         8 . The method of  claim 2 , wherein the relationship used by the deep learning model comprises the presence or absence of certain words or phrases in the library. 
     
     
         9 . The method of  claim 2 , wherein the relationship used by the deep learning model comprises the order in which the words or phrases appear.