A major global economic issue in the telecom industry, churn, if not properly managed, leads to significant loss of revenue and blocks the growth of all operators. Economic loss caused by churn is twofold. Firstly, operators lose any future revenue that a churned customer would provide and secondly, all marketing funds used to acquire the customer in the first place are lost.
According to Sicap, a typical mobile operator with an ARPU of €30 in a mature market and subscriber acquisition cost of €270, loses a total of 18% of their annual revenue due to churn, when one assumes an industry average annual churn rate of 24%. The lost subscriber acquisition cost is €65-million per year for one million subscribers, before calculating the lost future revenue.
Sicap AI Engine predicts and identifies churn-prone subscribers, by combining customer-related big data, statistical and analytical techniques and self-learning neuronal networks. The AI Engine makes use of customer data provided by Sicap’s device and SIM management platforms, as well as operators’ other internal and external data sources.
Before the AI Engine is deployed, its neuronal network system is trained by using an operator’s historic data. To increase the prediction accuracy over time, the training is continued using the operator’s actual data.
The system provides a churn prediction list including potential causes for churn and subscriber segments, based on their likelihood to churn within certain confidence intervals. The results are then used to automatically engage customers with targeted and personalized incentive programmes and offers, depending on the segment the subscriber belongs to. Accurate targeting results in more relevant offers, and prevents customers from churning.