How a Fortune 500 insurance company used cluster
How a Fortune
500 insurance company
used cluster analyses
analyses to identify their top customer personas
to identify their top
customer personas
When you have multiple audience types, you can’t expect a single message to resonate with everyone. But if you take the time to uncover the specific needs of each segment, you can create relevant communications that drive engagement and customer conversion.
Assurant, Inc., is a Fortune 500 company that provides risk management, insurance, technology, and customer experience solutions to consumers and businesses. To grow their multifamily housing (MFH) division, they sought to identify the most common types of renters to help property managers assess risk and opportunity before committing to a lease.
Agency services:
A cluster analysis is an exploratory tool designed to reveal natural groupings (or clusters) within a data set. But first, we needed to define that data set by interviewing renters representing a balanced mix of gender, age, education, work status, and community. This not only yielded reliable and actionable clusters, but also recommended the proper number of clusters. In Assurant’s case, five personas took shape.
The right renter carries very little risk, allowing property managers to focus on other critical areas of their business like attracting new tenants, maintaining their property, and retaining existing tenants. The opposite is true with the wrong renter, consuming time and resources with delinquent payments, insurance coverage gaps, and property damage.
Renters insurance helps to mitigate risks, while enabling property managers to increase occupancy, retention, and net operating income. Armed with the newly discovered persona data, Assurant’s customers could sell renters insurance by framing their conversations around the specific behaviors, needs, and concerns of each tenant.
While specific results are confidential, Assurant continues to partner with FARM on using research to inform marketing channel selection, target-audience segmentation, product bundling, and content messaging.
Imagine the playground torments and
Imagine the playground
torments and missed
missed high-school dances our data
high-school dances our
data scientists had to
scientists had to endure–all
endure–all so you can
be the big cheese at
so you can be the big cheese at the
the reunion. Now can
we have our
reunion. Now can we have our
lunch money back?
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