Sap Se

Michael McClenaghan

Shane Bracher

Daniel Ricketts

Mark Daniel Holmes

Liam Mischewski

Glenn Neuber

Asadul Islam

Hoyoung Jeung

Priya Vijayarajendran

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

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Brisbane, Australia

Michael McClenaghan