A processor implemented method, comprising: receiving, via one or more hardware processors, an input data comprising information corresponding to a plurality of users, one or more associated transactions, one or more clickstreams associated with one or more items, and one or more offers; computing, via the one or more hardware processors, a look-ahead score for at least one of a potential sale, a likelihood of churning of the plurality of users, and a subsequent user arrival window based on the received input data; identifying, via the one or more hardware processors, a set of potential segments based on the computed look-ahead score and one or more historical scores, wherein the one or more historical scores correspond to one or more historical transactional data; and mapping, via the one or more hardware processors, each of the plurality of users to at least one potential segment from the set of potential segments; receiving, in real-time, a trigger notification associated with one or more users; generating a set of one or more recommended items for the one or more users by eliminating popularity bias based on the received trigger notification, wherein the set of one or more recommended items are generated by eliminating the popularity bias associated with one or more items comprised in an item catalogue; mapping one or more personalized offers to the generated set of one or more recommended items for the one or more users, using a deciphering technique; and determining a set of optimal offers based on the one or more personalized offers being mapped to the generated set of one or more recommended items. receiving, via one or more hardware processors, an input data comprising information corresponding to a plurality of users, one or more associated transactions, one or more clickstreams associated with one or more items, and one or more offers; computing, via the one or more hardware processors, a look-ahead score for at least one of a potential sale, a likelihood of churning of the plurality of users, and a subsequent user arrival window based on the received input data; identifying, via the one or more hardware processors, a set of potential segments based on the computed look-ahead score and one or more historical scores, wherein the one or more historical scores correspond to one or more historical transactional data; and mapping, via the one or more hardware processors, each of the plurality of users to at least one potential segment from the set of potential segments; receiving, in real-time, a trigger notification associated with one or more users; generating a set of one or more recommended items for the one or more users by eliminating popularity bias based on the received trigger notification, wherein the set of one or more recommended items are generated by eliminating the popularity bias associated with one or more items comprised in an item catalogue; mapping one or more personalized offers to the generated set of one or more recommended items for the one or more users, using a deciphering technique; and determining a set of optimal offers based on the one or more personalized offers being mapped to the generated set of one or more recommended items.