Optimizing enterprise data architecture and operationalizing analytics.
Kelly® Technology executes a highly complex new global modeling evaluation for a large insurance company.
Data modeling to optimize new revenue sources.
A leading insurance client wanted to drive growth by targeting new cross-selling opportunities that allowed customers to take advantage of its enhanced offerings. Global modeling for the initiative was highly complex due to the nature of the data sources, database components, and market segments. The goal was to maximize value to policyholders, analyzing purchasing habits and upsell opportunities by better defining and classifying data with market segments. The client wanted to optimize revenue sources by leveraging data ingestion techniques and combining dozens of data sources with databases, requiring POC models to be built. They needed to establish virtualization and data lakes between information sources.
Using BI tools for POC concepts to monetize data.
Optimization consisted of data modeling, working with a suite of BI tools for POC concepts to monetize data and drive revenue more accurately. Kelly developed POCs targeted to segment teams, devoted to data virtualization shortcuts. They also built models that would improve the underwriting process, leveraging Data Foundations BI, Big Sequel, Hadoop, and Hive. They operationalized the analytics process by making the data collection process more valuable for data owners in locating, aggregating, cleansing, and visualizing data. Kelly also met stakeholders and teams to align on success metrics for POC models.
Improved operations by 30% and customer service by 20%.
Kelly developed robust data and analytics models to provide integrated, trusted, and timely data. This improved reporting and data analysis, to drive increased action and decision-making, to drive business growth. They created a team scoring process with stakeholders for POC models. The project was shown to optimize reporting and operations with underwriting groups by nearly 30%. It also centralized data and improved quality in targeted regions across the U.S. and EMEA. Finally, it achieved goals of activating cross-selling to targeted policy holders and new customers—while improving customer service, customer experience, and conversation rates by 20%.
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