The Generali Group wished to increase the number of policies to strengthen its life insurance sector.

The life insurance policy portfolio was analyzed with Viscovery software to determine the probability that a customer would purchase a life insurance policy. Customers with a high affinity for life insurance were addressed with specific campaigns. Based on the success of the pilot project, the same measures were extended throughout Austria. The accuracy of the prediction model enabled highly effective target-group-specific measures and the successful implementation by the field staff. The response was evaluated as “very satisfactory” by the Generali Group, and the project considered a success in active portfolio management.

Cross Selling


OBI employed Viscovery to analyze the buying behavior of customers using the “Biber-Bonus-Card”, which allows customers to receive an annual bonus based on their purchases.

Customer segments were identified and used to optimize the use of advertising media. The suggested measures improved the effectiveness of advertising by approximately 20%. OBI headquarters as well as franchise partners consider the project to be highly successful, and OBI has implemented the customer behavior prediction for franchise partners.


Web Marketing employed Viscovery to analyze use of their web portal.

Target group profiles, provided to advertisers to address particular groups of users, must be significant to effectively focus advertising measures. Previous methods failed to exploit opportunities efficiently. Based on Viscovery's analysis, the structure of the site was optimized according to customer needs. By delivering specific target group profiles to its advertising partners, has significantly increased advertising revenues.

Internet Use


Xella/Ytong engaged Viscovery to analyze the production processes for Ytong blocks to optimize product quality and reduce scrap.

When critical process states are identified, it is possible to monitor sensitive events and to avoid quality losses. Previous analyses with conventional methods had failed due to the complex relationships between production parameters and product properties. Based on the cluster analysis of production data using Viscovery, several sources of defects were identified, including a miscalculated formula. Problematic processes were corrected, permanently reducing production costs.