Understanding the value of customer segments and leveraging these insights

30 September

Customer Centricity Conference

© 2016 Willis Towers Watson. All rights reserved.

Understanding the value of customer segments and leveraging

these insights

Francesco Daboni and Michael Kluettgens

Agenda

© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only. 2

Value of in-force per

policyAnalyse and Assess

Customer Behaviour

Customer & Sales

Management

Up-Selling to

Existing

Customers

Smarter

Handling of

Low Value

Customers

Cross Selling

to Existing

Customers

Use

Analytics to

give better

targeting of

offers

Maturity and

Retention

Mgmt. of

High Value

Customers

Review level

and structure

of renewal

commissions

Calculating a VIF per Policy with your Solvency II model requires additional considerations

but a lot can be leveraged


Yield Curve

No extrapolation or transitional provision to measure economic value

Use of illiquidity premiums in valuation appropriate provided liabilities allow for this and

investment assets are expected to earn the premium

Going Concern approach appropriate but decline of in-force to be reflected properly

Be careful not to overestimate profitability of small premium policies

Cost may vary significantly by administration system

To be assessed whether GLM techniques can result in materially improved biometric

assumption at customer level

Customer behavior could be included individually (see analysis later in presentation)

Biometric

Assumptions

Yield Curve

Mismatch cost to be allocated only to new business if asset risk exposure considered

important to remain attractive for new customers

Solvency risk margin not a good proxy for cost of capital

Expenses

Cost of mismatch

and cost of capital

It is not the objective to get to a realistic impact of lapses on the whole portfolio

relevant for embedded value, Solvency II planning purposes

P&C actuaries are using GLM type approaches for many

years to assess the risk exposure of individual customers

Customer behavior: the tools and techniques are available


Objective is to Assess the Lapse Probability of each Customer Individually

Identify

customers who

have surrendered

in the past

Analyse which

customer

attributes

(“variables”)

strongly correlate

with lapse

experience

Perform

regression

analysis to

determine for

each variable a

lapse factor

relative to a base

lapse rate

Rank

policyholder by

individual lapse

probability and

focus activities

Goodness of fit

test

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Example: Result of regression analysis by customer


Age Policy Year Income relative to

age

Life Event Driven Total factor

Cu_x 30 3 high House bought

Factor 1.6 1.5 1.4 1.2 4.2

Lapses.. ..reduces with increasing

age

.. reduces with increasing

policy term

… is particularly high

with very high and very

low wages relative to age

..increase with various

life events

Lapse prob.* Customer Value

Cu x 21% (5%*4.2) 1000

Cu y 26% (5%*5.1) -500

In a retention campaign the focus should be on customers of type x.

Customers of type y should be eased to surrender

It is important to measure success of activities by defining control groups

External data

sources

Age Policy Year Income relative to

age

Life Event Driven Total factor

Cu_y 50 12 low Lost job

Factor 0.9 1.1 1.3 3.9 5.1

*5% base lapse rate assumed

Insurers may obtain data from various sources

6

By using granular user data, insurers gain valuable customer insights and can make

better use of the individual cross- und up-selling potential.

External data

Professional data traders

(e.g. Experian, Acxiom,

RapLeaf) collect data, enrich

data and summarise the results

in extensive user profiles. The

main disadvantage of this

approach is again the missing

real-time context.

...

Internal data

The number of insurers that are

launching apps is increasing.

These apps often have nothing

to do with the core business

itself but are designed to

collect useful data. These

methods go far beyond the

opportunities of classical web

analysis.

Social media data

The current social media

platforms gather numerous

user data with an extraordinary

high real-time context.

For example: Facebook offers a

free API interface to seek the

information that is publicly

available.

Public data

Insurers have access to public

birth, marriage and death

records and can use these

records for marketing purposes.

This approach is already

applied in practice. The

disadvantage of this approach

is the missing real-time context.

© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.

Obvious action points for two customer profiles


Life P&C Total

Customer Value 50 200 250

Exposed to surrender?

Upselling potential

Value of up-sale 20 50

Customer we want to retain

Exploit upside potential

Exclusion from retention

program

Customer we want to retain

Customer retention program

Exclusion from upselling

activities

low

high

Life P&C Total

Customer Value 50 200 250


Upselling potential


high

low

VN1

VN2

Life P&C Total

Customer Value -150 200 50


Upselling potential


Ideally, customer surrenders

life policy only and buys new

contracts

To be assessed whether lapse

incentives may not yield the

opposite of the objectives

Customer we may not want to

retain

Upselling potential to small to

offset negative life customer

value

To be assessed whether lapse

incentives may not yield the

opposite of the objectives

high

high

Life P&C Total

Customer Value -500 20 -480


Upselling potential


high

high

VN3

VN4

Less obvious action points for two further customer profiles


“Customer Centricity is not About Being nice to Customers. It’s About Making Money”


Positive customer

value for LifePositive customer

value of for P&C

Positive customer value

for Health

Which clients may buy a new product (and what type of product)?

Which customers do we risk to (or may we want to) loose?

Understanding the value of customer segments and leveraging these insights

Business

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