optimized pricing : a key lever for profitable growth cas spring meeting 18 may, 2004

Optimized Pricing :A Key Lever for Profitable Growth

CAS Spring Meeting 18 May, 2004

© Optimal Decisions Group 2003. All rights reserved ODG-CAS2

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?


…. 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


While customers differ both in terms of their unit costs and their price sensitivity, most insurers model the former but not the latter

• Some drivers are more risky than others (ie have higher expected claims costs) ….

– Are less careful and/or less skilled

– Drive more miles

– Drive cars that are more expensive to repair

– 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

Examples of Unit Cost Differences

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)

– Shop around at each renewal or at low levels of renewal price increase

– Shop a wider basket of competitors

– 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

Claims models ? Observed Closure or Retention Rates = Elasticity


Quantifying these segment elasticity differences enables the setting of more optimal prices

$20 $20

100 100

= $4000 profit

= 200 units

$100 $100Price

Margin

Volume

ElasticSegment

A

InelasticSegment

B

6 2Elasticity

At Current Prices

$15 $35

130 70

= $4400 profit

= 200 units

$95

$115

Price to Max Profit at Current Volume

$10 $30

160 80

= $4000 profit

= 240 units

$90

$110

Price to Max Volume at Current Profit

= % ∆ in Volume for a -1% ∆ in Price

Or


So how do you go about setting and maintaining optimal 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

3. Integrate the models into an multi-year profit simulation - to determine the optimized prices

4. Continue to update models and reset optimized prices

Setting Initial Optimized Prices Keeping Prices Optimal


1. Build unit profit models to estimate the unit contribution for each applicant segment – if they accept at a given price

For each set ofapplicant characteristics

$ Profit Contributionper applicant accepting

Net Cost

Expected Claims

All Other Net Costs

Profit

Price

• 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


2. Build price elasticity models to estimate the price/volume trade-offs for each applicant segment – probability of acceptance at a given price

Number of Applicants Accepting Quoted Price

Total Available Market

Renewal

NewBusiness

PriceCompetitor’sPrice

• Simple approach : use past history of price changes and “strike” rate impacts, with linear models (eg GLM)

• 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



3. Integrate the models into an multi-year profit simulation to determine the optimised prices

RenewalNewBusiness Price

Total Profit

Year 1Year 2

Year 3


X

Number of ApplicantsAccepting

Renewal

NewBusiness

Price

Price

Unit Profit PerApplicant Accepting

• Simple approach : use segment-average 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


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


The results have been outstanding

Wanted to adjust pricing to maximize growth while maintaining a 15%pa ROC

Australian Home Insurer UK Auto Insurer

Wanted to maximize profits without shedding too much share

Objectives :

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

Results :

In general, extra pre-tax profit from Optimized Pricing around ~2% to 6% of Premium p.a., from higher margins and/or higher volume


But how can this approach work in the US market where regulations prohibit prices that are “unfairly discriminatory” ?

• 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

Some “discrimination” possible


There are ways to make elasticity-based pricing decisions that are not “unfairly discriminatory”

• 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

• 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.

Straightforward Price Changes Where optimal price increase is > cost increase


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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optimized pricing : a key lever for profitable growth cas spring meeting 18 may, 2004

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