Churn / early warning signs before cancellation

Business question
Which customers are about to cancel, and why?
What's at stake
For an insurer with 60,000 auto policies: 900 to 1,620 cancellations avoided a year, or $1.9M to $3.5M in premium retained.
Data required
Contact history, digital journeys, invoices/reviews, complaint verbatims over 6 to 12 months.
Sample result
List of at-risk customers ranked by stakes, with the dominant reason and the action window before likely cancellation.
Demo · 1 min
Case study

At a multi-line insurer with around 200,000 personal-lines customers, the CRM team had seen auto policy cancellations creep up, without being able to pin down the cause despite several internal analyses.

Datakili's agents cross-referenced contact history, billing data and complaint verbatims over 9 months. This cross-cutting read surfaced three distinct cancellation profiles.

The full analysis was delivered in 48 hours, versus the several weeks of manual cross-referencing it usually takes.

Cancellations on the targeted segment fell 18% the following quarter.

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