Churn Risk Management automation



The Claim Prediction Tool (ClaP) is a system designed to support the analysis and supervision of customers at risk of churning in the Telco industry. The tool is primarily used in the context of CRM and Customer Retention actions - and in particular Proactive Caring activities - to actively monitor and prevent reports of inefficiency or customer dissatisfaction in relation to voice and data services and Trouble Ticket Management, and effectively solve issues based on customer profiling.


While it is easy for a customer to verify the occurrence of any service disruptions or inadequacies on the part of a network operator, for the operator it is much more complicated to identify such issues because:
  • information on service quality gaps may reside in different systems
  • technical data must be compared and analysed against commercial data to identify any service quality gaps
  • in order to transform data into knowledge complex integration, pre- and post-processing activities must be carried out on data flows obtained from different sources (DBMSs and IT systems owned by the Telco Service Providers).


ClaP is a tool based on a predictive Machine Learning model that automatically assigns a “Churn Score” to each user in the customer base, enriching their profile with information on the customer segment to which he/she belongs. The tool therefore enables: a structured view of customers at greater risk of churning, based on the Churn Score; access to detailed information relating to the customer profile (e.g. KPIs associated with the commercial offer, devices currently used by the customer, the history of customer-service provider relationships) and the availability of tracking reports of the main errors that have a negative impact on the customer experience.


ClaP has been designed to be integrated into the work tools made available to customer operations agents, making it possible to instantly identify the risk of churn, to promptly report possible complaints and to suggest, in real time, the best actions to be taken to resolve the issues thus identified. Analysis of the use of ClaP shows that 84.4% of customer contacts with the Customer Service function were correctly predicted and 76.6% of customer complaints were anticipated by 3 days, thus reducing the customer complaint rate by 2/3.


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