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
© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only. 3
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
© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only. 4
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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Coyognü Ozzpcü Schmptz, Mxrcüy CMpS00000277737 x&O xutovürspchürung Oydünqurg xG Hürr Güqhxrd Sxnnü Dprüktor
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Wprtschxztsmxthümxtpkür
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Example: Result of regression analysis by customer
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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.
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Obvious action points for two customer profiles
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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
Exposed to surrender?
Upselling potential
Value of up-sale 20 50
high
low
VN1
VN2
Life P&C Total
Customer Value -150 200 50
Exposed to surrender?
Upselling potential
Value of up-sale 20 50
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
Exposed to surrender?
Upselling potential
Value of up-sale 20 20
high
high
VN3
VN4
Less obvious action points for two further customer profiles
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“Customer Centricity is not About Being nice to Customers. It’s About Making Money”
© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only. 9
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?