New cross-sell paths for
a large insurer.
A large insurer wanted to understand its B2B customer base well enough to align product offerings and service to real needs.
We extracted demographic, sales, and behavior data and used statistical techniques (k-means cluster analysis) to segment the customer base into four distinct groups, then analyzed each segment's purchasing habits, preferences, and satisfaction. From there we shaped focused campaigns, tailored offerings, and service recommendations matched to each segment.
The insurer identified new opportunities to cross-sell and upsell by segment and improved satisfaction through more personalized offerings, enhancing customer experience and supporting growth.
Even before agentic AI, disciplined data work turns a vague “understand our customers” goal into specific, actionable segments, the analytical foundation good AI builds on.
Anonymized; results as reported by WCG. The work predates the productized Sprint, shown as proof of operational AI delivery.
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