Optimized Pricing :
A Key Lever for
Profitable Growth
CAS Spring Meeting
18 May, 2004
P&C personal line insurers around the world are seeking to set “optimized” prices to
drive profitable growth …
How do we set optimized prices – ie those
which best meet our financial objectives eg…
• Gain market share while ensuring
adequate returns?
• Increase returns while maintaining
adequate market share?
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…. But are facing significant obstacles
1. Don’t have a rigorous way to estimate price elasticity of granular
customer segments , and to use this in price setting
• Have a reasonable handle on claims and expenses, and therefore
unit margins for different segments
• But don’t have a robust way of estimating the way volume and
therefore profit responds to price changes by segment
• And don’t have a way to integrate unit margins and elasticity to
predict the financial impact over time of price changes for the many
thousands of segments
2. Don’t feel able to set optimized and therefore differential margins
across segments in highly regulated environments like the US
• Regulators won’t approve prices that are “unfairly discriminatory” – ie
that are significantly out of alignment with costs
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While customers differ both in terms of their unit costs and their price sensitivity, most
insurers model the former but not the latter
Examples of Unit
Cost Differences
• Some drivers are more risky than
others (ie have higher expected claims
costs) ….
– Are less careful and/or less skilled
Examples of Price
Sensitivity Differences
• Some customers are more price
sensitive than others (ie have a larger
change in conversion /renewal rates for
a given price change)
– Drive more miles
– Shop around at each renewal or at
low levels of renewal price increase
– Drive cars that are more expensive to
repair
– Shop a wider basket of competitors
– On routes/locations that are more
accident or theft prone
• Factors typically correlated with these
differences include gender, age, vehicle
type/age, use, location, driving/claims
history, credit history etc
 Claims models
– Are more inclined to switch brands for
small price differences
• Factors likely to be correlated with
these differences include income/wealth,
sum insured, age, tenure, channel,
extent of product bundling, payment
method, credit history etc
? Observed Closure or
Retention Rates = Elasticity
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Quantifying these segment elasticity differences enables the setting of more
optimal prices
Price to Max Profit at Current Volume
$115
At Current Prices
Elastic
Segment
A
Inelastic
Segment
B
Price
$100
$100
Margin
$20
$20
$95
= $4000 profit
Or
$15
$35
130
70
= $4400 profit
= 200 units
Price to Max Volume at Current Profit
Volume
100
100
Elasticity
6
2
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= 200 units
= % ∆ in
Volume for
a -1% ∆ in
Price
$110
$90
$10
$30
= $4000 profit
160
80
= 240 units
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So how do you go about setting and maintaining optimal prices?
Setting Initial Optimized Prices
1. Build unit profit
models – contribution
per customer if they
accept at a given price,
for each segment
2. Build price elasticity
models – volume of
customers accepting at
a given price , for each
segment
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Keeping Prices Optimal
3.
Integrat
e the
models
into an
multiyear
profit
simulati
on - to
determin
e the
optimize
d prices
4.
Continu
e to
update
models
and
reset
optimiz
ed
prices
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1. Build unit profit models to estimate the unit contribution for each applicant
segment – if they accept at a given price
Profit
$ Profit
Contribution
per applicant
accepting
For each set
of
applicant
characteristics
Price
Expected
Claims
All Other Net
Costs
• Model claims cost based
on past claims
experience, enriched with
external data eg census,
perils, credit
• Allocate the variable
component of expenses,
reinsurance, investment
income and cost of
capital
• Model the cross-sale/
cross- “unsale” value
Net Cost
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2. Build price elasticity models to estimate the price/volume trade-offs for
each applicant segment – probability of acceptance at a given price
• Simple approach : use past
history of price changes and
“strike” rate impacts, with linear
models (eg GLM)
Number of
Applicants
Accepting
Quoted
Price
Total Available Market
Renewal
For each set
of
applicant
characteristics
New
Business
Competitor’s
Price
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Price
• But this doesn’t work well !!! :
- Past history too sparse,
uncontrolled for known
competitor rate changes,
and massively co-linear
- Well-fitted GLM strike rate
models produce inaccurate
elasticity predictions
• More advanced approach :
- Enrich past history with price
variation, competitor prices
and external data
- Use non-linear models
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3. Integrate the models into an multi-year profit simulation to determine the
optimised prices
Unit Profit Per
Applicant Accepting
Total Profit
Price
X
Number of Applicants
Accepting
Renewal
New
Business
Price
For each set of
applicant characteristics
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New Renewal
Price
Business
Year 1
Year 2
Year 3
• Simple approach : use segmentaverage elasticities x margins for a
series of one-way cuts to estimate
profit and volume impact of a price
change
• But this doesn’t work well !!! :
doesn’t allow for “adverse selection”
effects where elasticity and margin
are correlated within a segment;
doesn’t allow for “stacking” the
impacts of multiple price changes
• More advanced approach :
- Use a granular simulator running
the models over every quote
record in a large sample to
estimate multi-year impact
- Use a visualiser to examine the
impact by aggregating into
segments
- Use an optimiser to identify the
set of price changes that best
meets the financial objectives
and constraints
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4. Continue to update models and reset optimised prices
Timing
Actions
Routinely
Continuously sample competitor prices
and maintain price variation, rerun
competitor price and elasticity models,
and update optimized prices
When there are significant
changes in own/competitor
marketing activity (or annually)
Re-model elasticity and re-set optimised
prices
Periodically (at least annually)
Update unit profit models (ie claims,
expenses), re-set objectives, re-set
optimized prices
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The results have been outstanding
Australian Home Insurer
Objectives :
Results :
UK Auto Insurer
Wanted to adjust pricing to
maximize growth while
maintaining a 15%pa ROC
Wanted to maximize profits
without shedding too much
share
Has grown over 40% in last
2 years at or above 15%pa
ROC versus a control group
Raised annual profit before
tax by 3% of NWP while
holding share versus a
control group
In general, extra pre-tax profit from Optimized Pricing around ~2%
to 6% of Premium p.a., from higher margins and/or higher volume
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But how can this approach work in the US market where regulations prohibit
prices that are “unfairly discriminatory” ?
Some “discrimination” possible
• Despite the regulations against “unfairly
discriminatory” prices, we see high variation in margin
across segments for a given insurer
• This arises from different competitive conditions, and
the insurer’s different position and ambitions in
different segments
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There are ways to make elasticity-based pricing decisions that are not “unfairly
discriminatory”
Straightforward Price Changes
• Most price reductions (ie segments
with high elasticity and sufficient
margin) even in segments where
costs haven’t reduced as much or at
all. Regulators usually like to see at
least some consumers getting lower
prices, even if this somewhat expands
margin differentials versus other
consumers
• Some price increases (ie segments
with low elasticity) to those segments
where costs have increased by as
much or more. Regulators will
generally approve preservation or
reduction of margins.
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Where optimal price increase is >
cost increase
• If your conversion/ retention rates
are high and/or your prices are
low vs competitors, may be able
to argue that current margins are
inadequate ….and so price rises
in excess of the cost increases
can be justified
• Failing this, the price increase
may need to be limited to the cost
increase - ie in the right direction,
but short of “optimal”… and use
marketing/service levels to shift
mix to more optimally priced
segments
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Even if you don’t use elasticity differences to set differential margins, there are
other ways you can use elasticity insights to make better decisions
Use the overall value models (including elasticity) and the
simulation tool ……
– To explore the profit and volume impact of alternative
“regulated” pricing strategies – eg will your next planned
rate filing really meet your financial objectives for profit and
growth? How will the mix of risks change as a result of the price
change? which components of the price change are actually
value destroying?
– To identify the best segment allocation of your marketing
spend, given your marketing response models and a
“regulated” price set – ie how should you allocate marketing
spend not just to get the highest response rates from low risk
segments, but to get the highest overall profit contribution?
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porlay@optimal-decisions.com
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