METHOD AND SYSTEM FOR PREDICTIVE MARKETING CAMPIGNS BASED ON USERS ONLINE BEHAVIOR AND PROFILE
The present invention discloses a method for providing prediction of content usage in a website. The method comprising the steps of: real time monitoring traffic of visitors in a website, tracking visitors that are visiting the monitored website to identify one or more parameters relating to user profile, navigation path or content usage, applying at least one statistical algorithm on identified parameters, said algorithm is at least one of: clustering algorithm, nearest neighbor algorithm or probability algorithm and defining content replacement and recommendation for visitors when visiting the monitored website according analysis results of the at least one statistical algorithm.
1 . A method for providing prediction of content usage in a website, said method comprising the steps of:
real time monitoring traffic of visitors in a website;
tracking visitors that are visiting the monitored website to identify one or more parameters relating to user profile, navigation path or content usage;
applying at least one statistical algorithm on identified parameters, said algorithm is at least one of: clustering algorithm, nearest neighbor algorithm or probability algorithm and
defining content replacement and recommendation for visitors when visiting the monitored website according the statistical algorithm analysis results of the at least one statistical algorithm.
2 . The method of claim 1 clustering algorithm enable classifying user into groups by analyzing of profile and navigation activities parameters of the user.
3 . The method of claim 1 wherein the probability algorithm enables analyzing the monitored user behavior and identifying correlation or association between visitors profile parameters, navigation path and/or content usage for creating probability tree.
4 . The method of claim 1 wherein the nearest neighbor algorithm enable classifying visitors into groups by analyzing content usage parameters of the user.
5 . The method of claim 1 further comprising the step of scoring the content items according to a probability tree created by the probability algorithm.
6 . The method of claim 1 further comprising the step of scoring the content items according to Neighborhoods created by the nearest neighbor algorithm.
7 . The method of claim 1 further comprising the step of analyzing organization behavior by Identifying function of each user in the organization and classifying his behavior for generating marketing scenarios based on analyzed organization behavioral.
8 . The method of claim 1 further comprising the step of checking user profiles and classified pattern behavior for identifying correlation with existing clients in CRM database.
9 . The method of claim 1 further comprising the step of characterizing user marketing state in a sale scenario based on comparing analysis results to predefined marketing templates.
10 . The method of claim 1 further comprising the step of scoring incoming leads based on CRM accounts similarity.
11 . The method of claim 1 further comprising the step of providing recommendation for further communication or actions to be taken based on one of the following: neighborhood and/or clustering groups or probability algorithm.
12 . The method of claim 1 wherein the statistical algorithm analysis results are cached using in-memory optimized matrix model, which allow real-time interactions on big data.
13 . The method of claim 1 wherein the content recommendation is based on content items clustering algorithm which enable classifying content into groups by analyzing plurality of attributes of the visitors that consumed the content.
14 . A system for providing prediction of content usage in a website, said system comprised of:
A tracking module for monitoring traffic of visitors in a website and identifying one or more parameters relating to user profile, navigation path or content usage;
statistical algorithm for analyzing identified parameters, said algorithm is at least one of: clustering algorithm, nearest neighbor algorithm or probability algorithm and
prediction module for defining content replacement and recommendation for visitors when visiting the monitored website according to analysis results of the at least one statistical algorithm.
15 . The system of claim 14 wherein the clustering algorithm enables classifying user into groups by analyzing of profile and navigation activities parameters of the user.
16 . The system of claim 14 wherein the probability algorithm enables analyzing the monitored user behavior and identifying correlation/association between visitors profile parameters, navigation path and/or content usage for creating probability tree.
17 . The system of claim 14 wherein the nearby neighbor algorithm enables classifying user into groups by analyzing content usage parameters of the user.
18 . The system of claim 14 wherein the prediction is further based on scoring the content items according to probability tree created by the probability algorithm.
19 . The system of claim 14 wherein the statistical algorithm further comprises the step of analyzing organization behavior by Identifying function of each user in the organization and classifying his behavior for generating marketing scenarios based on analyzed organization behavioral.
20 . The system of claim 14 wherein the prediction is further based on checking user profiles and classified pattern behavior for identifying existing clients in CRM database.
21 . The system of claim 14 the statistical algorithm further comprise characterizing user marketing state in a sale scenario based on comparing analysis results to predefined marketing templates.
22 . The system of claim 14 wherein the prediction module is further based on scoring incoming lead based on CRM accounts similarity.
23 . The system of claim 14 wherein the prediction module further providing recommendation for further communication or actions to be taken, based on neighborhood and/or clustering groups or probability algorithm.