Leading Insurance Provider leveraging BI Implementation

Infogain leveraged its experience in the insurance domain and with BI technologies to deliver
a state-of-art solution for the client. Infogain worked with the client’s IT and functional teams
to produce multi-dimensional data warehouse systems using the Microsoft BI Suite. This
Business Intelligence solution enabled the conversion of raw data into the information needed
for accurate management decision-making.
Some of the business and technology benefits include:

• In-depth analysis of KPIs such as claims, broker earnings, outstanding claims and premiums down to the policy level
• of Key Performance Indicators (KPIs) vs Budget/Forecast
• Intuitive dashboards based on dials, gauges and traffic lights
• Reports downloadable to common Microsoft Office formats and PDF for ease of use and sharing
• Capability to share information through discussion threads and public folders
• Robust metadata to deliver a flexible and scalable BI architecture
• Single IDE for all reports and dashboards
• Consistent and extendible ETL technology for all data marts and reusability of dimensions.

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Building Customer Intelligence _Step_two

Intelligent analysis of aggregated customer data is Customer Insight. It enables identification of relationships between individual customers and product / service profitability. With the growing complexity and value added personalized service demands across all customer facing touch points there is a need to quickly attain the capability of ‘Intelligent Customer Insight’. Customer insight enables OLAP & Data Mining Analytics, measuring Customer Value and visibility to all historic customer activity enterprise-wide.
One of the most critical functions performed by Customer Insight is Customer Analytics. It includes data mining, segmentation and building a customer analytics architecture. With customer analytics, an enterprise can enhance cross selling and up selling market opportunities. It is the technique to analyze customer behavior and knowing them better for a more focused market campaigns suited to different customer segments. From the ‘single view’ customer data repository, different segments are created on the basis of customer value to the enterprise. With the various tools, relationship between individual product/service and customer can be directly mapped to profitability. Segmentation is mainly the end of data mining.