Adjusting content of a webpage in real time based on users online behavior and profile

Abstract

A method and system for providing adjusted content in a webpage are described. The system monitors traffic to a website and tracks users that are visiting the website to identify one or more parameters relating to relating to the user, including parameters associated with an identity of the user, navigation behavior for the user within the website, and usage of content by the user within the website. The system analyzes the parameters and selects at least one statistical algorithm for a type of the parameter, and based on the analysis, identifies an organization to which the user belongs. The system selects and presents content for the website to be presented to the user based on the analysis.

What is claimed is: 
     
         1 . A method comprising:
 tracking a sequence of user actions of a user visiting a website;   analyzing the user actions to determine: a first subset of the user actions performed during a first visit to the website, and a second subset of the user actions performed during at least a second visit to the website;   based on the first subset and the second subset of the user actions, determining, using an algorithm, a marketing state of the user as one of awareness, interest, or evaluation;   selecting content for the website to be presented to the user based on the determined marketing state of the user, wherein the content is selected from a plurality of content items stored in a content database;   modifying the website, in real-time, to include the content selected based on the determined marketing state of the user; and   presenting the modified website, including the content selected based on the determined marketing state of the user, to the user.   
     
     
         2 . The method of  claim 1 , further comprising identifying a first parameter associated with an identity of the user and a second parameter associated with navigation behavior of the user within the web site. 
     
     
         3 . The method of  claim 2 , further comprising applying a first statistical algorithm to analyze the first parameter associated with the identity of the user and a second statistical algorithm to analyze the second parameter associated with the navigation behavior of the user within the web site. 
     
     
         4 . The method of  claim 3 , further comprising selecting the content for the website to be presented to the user based on an analysis of the first parameter associated with the identity of the user according to the first statistical algorithm and an analysis the second parameter associated with the navigation behavior for the user according to the second statistical algorithm. 
     
     
         5 . The method of  claim 1 , further comprising utilizing a probability algorithm for creating a probability tree based on identification of a statistical correlation between the marketing state of the user and content items. 
     
     
         6 . The method of  claim 5 , wherein selecting the content for the website to be presented to the user based on the determined marketing state of the user comprises utilizing the probability tree to identify one or more content items. 
     
     
         7 . The method of  claim 1 , wherein determining, using the algorithm, the marketing state of the user as one of awareness, interest, or evaluation comprises utilizing a nearest neighbor algorithm for applying collaborative filtering and classifying users into an awareness group, an interest group, or an evaluation group. 
     
     
         8 . The method of  claim 1 , further comprising storing the marketing state of the user in a caching repository that enables real time statistics and data retrieval. 
     
     
         9 . The method of  claim 1 , wherein selecting the content for the web site to be presented to the user based on the determined marketing state of the user selecting the content based on a content-items-clustering algorithm, including classifying the plurality of content items into groups and analyzing a plurality of attributes of each one of a plurality of users who consumed each one of the plurality content items. 
     
     
         10 . A system comprising:
 at least one processor;   at least one non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
 track a sequence of user actions of a user visiting a website; 
 analyze the user actions to determine: a first subset of the user actions performed during a first visit to the website, and a second subset of the user actions performed during at least a second visit to the website; 
 based on the first subset and the second subset of the user actions, determine, using a clustering algorithm, a marketing state of the user as one of awareness, interest, or evaluation; 
 select content for the website to be presented to the user based on the determined marketing state of the user, wherein the content is selected from a plurality of content items stored in a content database; 
 modify the website, in real-time, to include the content selected based on the determined marketing state of the user; and 
 present the modified website, including the content selected based on the determined marketing state of the user, to the user. 
   
     
     
         11 . The system of  claim 10 , further comprising instructions that, when executed by the at least one processor, cause the system to utilize a probability algorithm for creating a probability tree based on identification of a statistical correlation between the marketing state of the user and content items. 
     
     
         12 . The system of  claim 11 , wherein the instructions, when executed by the at least one processor, cause the system to select the content for the website to be presented to the user based on the determined marketing state of the user by utilizing the probability tree to identify one or more content items. 
     
     
         13 . The system of  claim 10 , wherein the instructions, when executed by the at least one processor, cause the system to determine, using the clustering algorithm, the marketing state of the user as one of awareness, interest, or evaluation by utilizing a nearest neighbor algorithm for applying collaborative filtering and classifying users into an awareness group, an interest group, or an evaluation group. 
     
     
         14 . The system of  claim 10 , further comprising instructions that, when executed by the at least one processor, cause the system to store the marketing state of the user in a caching repository that enables real time statistics and data retrieval. 
     
     
         15 . The system of  claim 10 , wherein the instructions, when executed by the at least one processor, cause the system to select the content for the website to be presented to the user based on the determined marketing state of the user by classifying the plurality of content items into groups and analyzing a plurality of attributes of a plurality of users who consumed the content items in each group. 
     
     
         16 . A non-transitory computer readable medium storing instructions thereon that, when executed by at least one processor, cause a computer device to:
 track a sequence of user actions of a user visiting a website;   analyze the user actions to determine: a first subset of the user actions performed during a first visit to the website, and a second subset of the user actions performed during at least a second visit to the web site;   based on the first subset and the second subset of the user actions, determine, using a clustering algorithm, a marketing state of the user as one of awareness, interest, or evaluation;   select content for the website to be presented to the user based on the determined marketing state of the user, wherein the content is selected from a plurality of content items stored in a content database;   modify the website, in real-time, to include the content selected based on the determined marketing state of the user; and   present the modified website, including the content selected based on the determined marketing state of the user, to the user.   
     
     
         17 . The non-transitory computer readable medium of  claim 16 , further comprising instructions that, when executed by the at least one processor, cause the computing device to utilize a probability algorithm for creating a probability tree based on identification of a statistical correlation between the marketing state of the user and content items. 
     
     
         18 . The non-transitory computer readable medium of  claim 17 , wherein the instructions, when executed by the at least one processor, cause the computing device to select the content for the web site to be presented to the user based on the determined marketing state of the user by utilizing the probability tree to identify one or more content items. 
     
     
         19 . The non-transitory computer readable medium of  claim 16 , wherein the instructions, when executed by the at least one processor, cause the computing device to determine, using the clustering algorithm, the marketing state of the user as one of awareness, interest, or evaluation by utilizing a nearest neighbor algorithm for applying collaborative filtering and classifying users into an awareness group, an interest group, or an evaluation group. 
     
     
         20 . The non-transitory computer readable medium of  claim 16 , further comprising instructions that, when executed by the at least one processor, cause the computing device to store the marketing state of the user in a caching repository that enables real time statistics and data retrieval.