Abstract: Embodiments described herein relate to methods and systems for defining and executing a cross-project orchestration process. The methods and systems can cause display of an orchestration system user interface having a rule-library panel defining one or more issue definition blocks and a rule-generation panel configured to receive issue definitions blocks and define a dependent relationship between one or more of the issue definition blocks. The orchestration system user interface can be used to generate an orchestration process flow. The orchestration process flow can include multiple issue definition blocks displayed in the rule-generation panel. Each issue definition block can define one or more issue parameters used to generate a respective issue at an issue tracking system. Placement of an issue definition block with respect to other issue definition blocks can define a dependent relationship of the respective issues at the issue tracking system.

Abstract: 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).

Abstract: 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).