Flytxt B.v.

Jobin Wilson

Prateek Kapadia

Santanu Chaudhury

Brejesh Lall

Shabana K M

Vinod Vasudevan

Noopur Jain

A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The methods and systems reduce the n-dimensional feature space to a lower dimensional space for easy processing, improved quality of emerging clusters and superior prediction accuracies. Further, the method applies the correspondence analysis so that each user is assigned with a new coordinate in the lower dimension, which maintains a similarity, difference and the relationship between the variables. Once the correspondence analysis is completed, clustering or grouping of the coordinates based on the similar trends of the users is performed. Further, unlabeled cluster members are assigned class membership proportional to the labeled samples in the cluster. Finally, the method predicts the future actions of the users based on the past trends that are observed from the labeled clusters.

Method and system for automated detection, classification and prediction of multi-scale, multidimensional trends

Embodiments herein provide a method and system that determines an underlying social network from user interaction graphs based on mobile service usage and derive social influence scores for various contexts based on user interaction parameters. The present disclosure pertains to a method of determining social influence score for a user of a social network, said method comprising creating, using a social network analyzer, a social graph based on usage data generated by a plurality of users, said usage data being obtained from a mobile communication network, wherein vertices of the created social graph represent the plurality of users along with edge weights that are based on weighted linear or non-linear combinations of key performance indicators (KPIs) representing actions made by each user; and deriving, by the social network analyzer, influencer score for each user in the social graph based on user interactions from online social networks and mobile interaction patterns.

Systems and methods for social influence based loyalty and customer experience management in mobile communication networks

Systems and methods for management of multi-perspective customer segments. This invention relates to customer management, and more particularly to management of customer segments, wherein the customer segments can be multi-perspective. Embodiments herein disclose methods and systems for generating multiple sets of clusterings of customers based on at least one customer attribute, and combining and interactively visualizing the clusterings, to derive insights.

Systems and methods for management of multi-perspective customer segments

Disclosed herein are methods and systems for providing multivariate time series clustering for customer segmentation. The system comprises of a model management unit that devices a customer segmentation procedure based on temporal variations of user preferences, using MTS clustering, and utilize the discovered clusters to learn association rules specific to each clusters, and improves campaign targeting. The order of the VAR model is fixed based on the nature of the data and length of the time series.

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Ernakulam, India

Jobin Wilson