life insurance market segmentation - acsw · analytics. diagnostic analytics. predictive analytics....

6 JUNE 2019

Talex Diede

Life Insurance Market Segmentation

Motivation

Predictive Analytics Journey

3DIFFICULTY

VALU

E Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

What happened?

Why did it happen?

What will happen?

How can we make it happen?

Source: Gartner (March 2012)

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Market segmentation and targeted marketing in your life

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Financial usage of segmentation

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Segmentation is already happening in insurance

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Current paradigm in life insurance

Customer segmentation in life insurance

• Understand the needs of the customer

• Build tailored products

• Efficiently market existing products

• Improve customer retention

Segmentation for Life Insurance

Current view of customers

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Susan is a 65 year-old female and has a qualified tax status

Susan

Kathy is a 65 year-old female and has a qualified tax status

Kathy

Mary is a 65 year-old female and has a qualified tax status

MaryTheoretically… Kathy, Susan, and Mary look identical, are assumed to behave in the same way, and have the same value and risk profile.

In Reality…• Do they really behave the

same?• Do they have the same

needs?• How much does the value

and risk profile of each customer vary?

• How can you acquire and retain the best customers?

Understanding your customers

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Major types of segmentation data

GeographicDemographic Psychographic Behavioral

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Data enrichment provides a comprehensive view

Ensure segments are useful

Measurable Substantial AccessibleDifferentiable Actionable

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Example of data-driven segments that identify policyholders likely to behave in similar ways

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Retired:Likely to be older, and live in areas with high proportions of individuals over the age of 65

Middle Income:Slightly higher than average education levels, home values, and income levels

Lower Income:Lower than average education levels, home values, and income levels, newer homeowners

Settled:Married, owned their homes for a long time, low loan-to-value ratios, lower home values

In Debt:Low credit scores, high counts of credit delinquencies in the last five years

Renters: Unlikely to own their own homes

High Income:Highest education levels, home values, and income levels

What to do with segmentation

Create segmented behavior assumptionse.g. Lapse

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Unsegmented

Unsegmented

Debt

Debt

Retired

Actuarial projection to determine profitability

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Customer Level Profitability (CLP)

Calculate profitability measure at seriatim level

Cash Flow Projection Model

Project seriatim cash flows

Customer Profile

Implement customer segment level behavior assumptions

Enhanced view of your customers

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Susan is 65 years old and has a qualified tax status

Susan is also retired, but she lives in Georgia where she has owned her home for 30+ years now

Susan is identified as belonging to the “Settled” segment

Susan tends to have high persistency in her product

Susan

Kathy is 65 years old and has a qualified tax status

Kathy is retired, and she currently lives in Florida in a community of other retired folks

Kathy is identified as belonging to the “Retired” segment

Kathy tends to be the most sensitive to the value of the guarantees in her product

Kathy

Mary is 65 years old and has a qualified tax status

Mary is not yet retired, she lives in the San Francisco bay area and has high income and net worth

Mary is identified as belonging to the “High Income” segment

Mary is less likely to use the liquidity features in her product

Mary Knowing… How could this new view of your customers be used?

Actuarial• Product design• ALM/Hedging• Inforce management• Solvency & risk

Sales & Distribution• Firm selection• Wholesaler lead scoring

Marketing• Driving demand• Cross-selling

Identify regions to focus marketing based on segmentation

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Target policyholders for retention

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Noaction

Noaction

Target for retention

Consider buyback

Policy 1

Policy 2

Policy 3

Policy 4

Policy 5

Probability of lapse

Prof

itabi

lity

Target messaging and products for different phases of life

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Important Considerations

Things to keep in mind or investigate for this kind of work

Use case Data availability Regulations

FCRAHIPAAGDPRNY Circular letter No. 1

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Looking forward

Move beyond segments to individual people

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Questions?

Talex Diede

Thank you

talex.diede@milliman.com