SimCog has developed new predictive analytics methods to improve pharma industry
Our health and the health of the future generations is one of the primordial worries of our society. The large costs related to health are often the cause of of discussion, especially for medical insurances and pharma industries. In many cases, these costs could be reduced through a proper prevention mechanism, i.e. by analysing possible patterns and relations between different illnesses, or by to identify which products can suit the needs of patients.
Reduction of the Contract Termination Rate
Health insurances profit from a high customer loyalty. This is not only archieved by a proper approach to new customers, but also by effective actions regarding existing customers. The identification of customers with a high risk of cancelling is advantageous when having to decide on countermeasures. SimCog offers the possibility to identify customers in this high-risk group in advance. The resulting churn-rate model helps companies to reach out to a large number of potentially terminating customers, limiting the required expenses
Early Diagnosis of Deseases
The anonymized historic data of patients, combined with preprocessed external data, are analyzed by SimCog’s complex pattern recognition methods. Through this, customers at risk of illnes can be identified, providing the possibility of initiating preventive treatments.