| CPC G06Q 30/0204 (2013.01) [G06N 20/00 (2019.01); G06Q 30/0254 (2013.01)] | 13 Claims |
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1. A computer-implemented method for granular level segmentation of users based on activities on webpages in real-time, the computer-implemented method comprising:
receiving, at a user segmentation system with a processor, 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 databases, and a third party database;
fetching, at the user segmentation system with the processor, 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 user segmentation system with the processor, 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 user segmentation system with the processor, 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 user segmentation system with the processor, 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 user segmentation system with the processor, 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 user segmentation system with the processor, 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 user segmentation system with the processor 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 user segmentation system with the processor, 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; and
displaying, at the user segmentation system with the processor upon initialization of the one or more marketing campaigns for the one or more segments, 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.
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