Customer Analytics for Increased Cross Selling

The Customer

The company is a leading provider of group, individual, life, car, home, travel, health insurance, and retirement pension plans. The company distributes its products through channels like direct mail, direct phone, agents, intermediaries, and partnerships with local banks.

Business Situation

• Unable to have a SINGLE view of the customer, and lack of data visibility across departments led to loss of cross selling opportunities
• Inability to characterize a customers’ net policy holding and transaction value due to silos of information coming from NINE different sources.
• For producing a SINGLE REPORT ONCE A MONTH it took about 20 days using excel spreadsheet from 8 different systems.
• Issues & errors in the reports produced with repeated request for data from IT all over again.
• The extraction logic kept changing – complexity and transformation was built into this logic.
• The integration logic – identify customers scattered across different underwriting systems and manually create customer master

Our Solution

• Captured the business need – from all aspects for decision making
• Understood every transformation that the business was applying manually using excel spreadsheet
• Understood in detail all the transformations that IT was applying during the extraction process.
• Introduced CloverETL as the integration engine- this started the simplification process
• Steps in the process of simplification:

• Understanding of data maculation logic used in the underwriting  systems and move it over to Clover ETL
• Data could now be visually represented and controlled at the will of business.
• understanding the data integration logic used to create the customer master
• Move the customer master to clover ETL for data integration logic
• Understanding the reporting requirement and creating a dimension data model
• Formulating a well-defined drill path that allowed client in “Our Solution”  to drill down using  a reporting tool
• Collating the data and loading it into a MySQL database
• Using CloverETL and MySQL to compare data between months
• Producing month by month comparative analysis reports
• Automating the complete solution – Allows business users to generate these reports at the click of a button

• Creating a SELF SERVICE ENVIRONMENT by porting these reports over to Tableau. Tableau stores all the historical data currently
• Implemented a foundational data integration solution – created a data model integrating the NINE different data sources
• Used a combination of open source and low cost but highly effective technology to integrate and visualize the end data representation

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