Sap Se

Shane Bracher

Daniel Ricketts

Mark Daniel Holmes

Hoyoung Jeung

Asadul Islam

Liam Mischewski

Glenn Neuber

Hua Wang

Padmanabhan Krishnan

Paola Sala

Karsten Ploesser

Pascal Hochwarth

Andrew Clark

George Mohay

Roheena Khan

Malcolm Corney

Michael McClenaghan

Priya Vijayarajendran

Ulrich Flegel

Xiaohua Cao

Hermann Resch

Stephan Wollny

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

Systems, methods and articles for determining anomalous user activity are disclosed. Data representing a transaction activity corresponding to a plurality of user transactions can be received and user transactions can be grouped according to types of user transactions. The transaction activity can be determined to be anomalous in relation to the grouped user transactions based on a predetermined parameter.

Detecting anomalous user activity

Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving, from a computing device used by an authenticated user, a validation request, the validation request including a first hash value and a first validation token, the first hash value being generated based on restricted content of a workflow object and the first validation token being associated with a first state of the workflow object, and determining that the authenticated user is authorized to request validation of the workflow object and, in response: decrypting the validation token to provide a second hash value, and determining that the second hash value is equal to both the first hash value and a third hash value and, in response, transmitting a validation response to the computing device, the validation response indicating that the workflow object is valid.

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Morningside, Australia

Shane Bracher