A non-transitory computer-readable storage medium encoding computer executable instructions that, when executed by at least one processor, performs a method for granular level segmentation of users based on online activities on a webpage in real-time, the method comprising:
- receiving, at a computing device, a first set of data associated with a plurality of users in the real time, wherein the plurality of users is associated with one or more communication devices, wherein the first set of data corresponds to personal information of the plurality of users, wherein the first set of data comprises name data, age data, e-mail identity data, contact number data, gender data, geographic location data, angiographic data, demographic data, payment cards data, banking partners data, and relationship status data, and wherein the first set of data is received from one or more online platform database, one or more communication device database, and a third party database;
- fetching, at the computing device, a second set of data associated with a plurality of past events of the plurality of users on at least one webpage of a plurality of webpages of one or more online platforms through the one or more communication devices;
- obtaining, at the computing device, a third set of data associated with a plurality of live events of the plurality of users on the at least one webpage of the plurality of webpages of the one or more online platforms through the one or more communication devices, wherein the third set of data is obtained in the real-time;
- analyzing, at the computing device, the first set of data, the second set of data and the third set of data in real-time using one or more machine learning models, wherein the analysis is performed based on training of the one or more machine learning models, and wherein the one or more machine learning models is trained using one or more of a supervised machine learning model, and an unsupervised machine learning model;
- identifying, at the computing device, using the one or more machine learning models, a match of one or more patterns of each of the plurality of users on a granular level based on the analysis performed on the first set of data, the second set of data and the third set of data, wherein the match of the one or more patterns is a match between a user pattern of each of the plurality of users among the first set of data, the second set of data and the third set of data;
- predicting, at the computing device, a behavior of each of the plurality of users based on the identified match of the one or more patterns, the second set of data, and the third set of data, wherein the behavior of each of the plurality of users is predicted in the real-time;
- selecting, in the real time at the computing device, a plurality of filters from a webpage category based on the predicted behavior of each of the plurality of users and one or more parameters, wherein the plurality of filters comprises time based filters, days based filters, age based filters, location based filters, events based filters, inactivity based filters, user properties filters, demographic filters, geographic filters, technographic filters, and application field filters, and wherein the one or more parameters comprise day, time, language, location, events, inactivity, and online platform;
- creating, at the computing device, one or more segments of the plurality of users based on the selected plurality of filters, wherein the one or more segments are created in the real-time;
- triggering, at the computing device, initialization of one or more marketing campaigns for the created one or more segments, wherein the one or more marketing campaigns are initiated based on the match of the one or more patterns with the created one or more segments using the selected plurality of filters, wherein the one or more marketing campaigns are initiated in the real-time;
- controlling, upon the initialization of the one or more marketing campaigns for the one or more segments, the one or more communication devices to display one or more advertisements associated with the one or more marketing campaigns for the one or more segments, wherein the one or more advertisements are displayed to each of the plurality of users on a corresponding communication device of the one or more communication devices based on the match of the one or more patterns, wherein the one or more advertisements are displayed in the real-time on the one or more communication devices;
- and
- further comprising creating, at the computing device, a segment plot for each of the one or more segments, the segment plot is in one or more forms, wherein the one or more forms comprise bar graph, histogram, pictogram, pie graph, line graph, and, cartesian graph, wherein the segment plot is downloaded in one or more formats, wherein the one or more formats comprise chart, joint photographic experts group, portable network graphics, portable document format, scalable vector graphics, And comma-separated values.