Background_
A prominent insurance provider was looking for new ways to acquire new b2b broker/agents using d2c data. Current personas of brokers are not providing much value in building engagement. 


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What we did_
Implemented a quantitatively defined, qualitatively described personas that properly segmented brokers based on their clients and portfolios, allowing for a prescriptive model to dictate which promotions and loyalty rewards best resonate with each firm.

These dramatically different personas were created using transactional data and informed UX and Content Strategy that created customized and personalized experiences creating an increase in sales and loyalty. Policy sales increased by 4% year-over-year as new loyalty programs were implemented and tailored to each persona's needs. 


Industry: Insurance

Tags: User Research, Personas, Big Data, Small Data, Machine Learning

Created At: Whirlpool


 

Results_

Policy sales increased by 4% year-over-year