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Mark Daniel Holmes

Daniel Ricketts

Shane Bracher

Liam Mischewski

Glenn Neuber

Asadul Islam

Hoyoung Jeung

Alexander Dreiling

Marek Kowalkiewicz

Karsten Ploesser

Christian Janiesch

Michael McClenaghan

Priya Vijayarajendran

Xiaohua Cao

Melissa Adkins

Dawid Grzegorz Weckowski

Implementations are directed to identifying potential churn of a user of one or more computer-implemented systems provided by an enterprise. In some examples, actions include identifying potential churn of providing a plurality of event profiles based on historical data, each event profile being representative of interactions of users with the enterprise and corresponding to churn of the users, at least one event profile being representative of an interaction of users with the one or more computer-implemented services, providing a pulse of the user at least partially based on historical data associated with the user, and one or more event profiles of the plurality of event profiles, and determining that the user is at-risk of churn based on the pulse of the user and a risk index value, and in response, displaying an indication that the user is at-risk of churn in a graphical user interface.

Real-time system to identify and analyze behavioral patterns to predict churn risk and increase retention

Customer churn risk scores are based on a multi-variable churn risk model relating customer and customer account characteristics to a risk of customer churn. A computer-implemented method of generating and presenting churn risk scores of customers of a telecommunication provider involves analyzing, on an in-memory database platform, customer call data records and customer records to calculate a churn likelihood value, an influence factor value, and an average spend value for each customer. The method assigns a churn risk score to each customer according to the model using the calculated churn likelihood value, the calculated influence factor value, and the calculated average spend value as input to the model. The churn risk scores for one or more customers are displayed visually on an interactive computer-user interface (UI).

Churn risk scoring using call network analysis

The present disclosure is directed to systems and methods including providing a data structure stored in computer-readable memory at a first location, monitoring a first work surface provided at the first location, determining that a first physical medium has been manipulated on the first work surface, and, in response, generating a first operation based on a manipulation of the first physical medium, applying the first operation to the data structure. Systems and methods can further include receiving operation and object data from a server system, the operation and object data corresponding to a second operation generated in response to manipulation of a second physical medium on a second work surface provided at a second location, applying the second operation to the data structure, processing the object data, and projecting a first virtual medium on the first work surface, the first virtual medium corresponding to the second physical medium.

Systems and methods for augmenting physical media from multiple locations

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Paddington, Australia

Mark Daniel Holmes