Out of all my PM experiences, this role was the most data-driven and involved solid understanding of actuarial rate making and various data analytical methods.  Each product manager had a portfolio, and I interacted with all the functional teams: actuarial, marketing, e-business, legal, compliance, claims, and underwriting to drive P&L impact.

 

Insurance product by nature is heavily price-driven.  I was fortunate to work closely with our actuarial team, the data scientists within insurance companies on pricing algorithm.  As PM, I made pricing recommendations at pricing committee meetings based on the below analysis:

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Cohort analysis - we aggregate user data by the year they sign up, the year accidents occur, the year losses occur etc.

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Data segmentation - rating state & territory, pricing tier, driver age & driving history, etc.

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External data aggregation - population density, theft rate, credit score, vehicle characteristics, DMV records, competitor rate filings, etc.

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Trend analysis - loss trend development (frequency vs. severity), rising medical costs, type of accidents (physical vs. bodily injury), large loss exclusion etc.

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