Price Optimization Getting Started - the Conference Exchange · 2009-05-01 · Copyright © 2009 LexisNexis Risk & Information Analytics Group Inc. All rights reserved. Price optimization

Copyright © 2009 LexisNexis Risk & Information Analytics Group Inc. All rights reserved.

A LexisNexis® Company

Price Optimization – Getting Started

Bob Weishaar

CAS Spring Meeting New Orleans

May 5, 2009


Agenda

• Building Blocks

– Elasticity

– Transitions

• Opportunities and Pitfalls

– Implementation

– Regulation


Price optimization combines estimates of risk/costs and of customers’ price elasticity to set prices which best meet a given financial objective

Incorporates an understanding of both the risk/costs…..

Claims & other costs

Price

Profit Per Customer

X

…..and the price sensitivity of different customers and applicants…..

….to predict the impact of a price change, and then…

…..to set prices which best meet a set of profit and volume objectives, and constraints

Elasticity Modelling

Simulation and OptimizationClaims and Expense Modelling

Total Profit

Price

Multiple Years

# Customers

Price

Current Price vsCompetitors

Profit Maximizing

Price


Elasticity is not the same as conversion or retention rate; Elasticity measures the response to price changes

Co

nve

rsio

n r

ate

Total Quote Pool

PriceCurrent Price Price

Total Quote Pool

Currentconversion

Rate

Segment A Segment B Illustrative

Marginal Elasticity of 1

Marginal Elasticity of 3

Current Price


Understanding elasticity is paramount to setting optimized prices

$20 $20

100 100

= $4000 profit

= 200 units

$100 $100Price

Margin

Volume

ElasticSegmentA

InelasticSegmentB

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


Single Get married/ graduate

Add home Add 17-yr old Have an accident

21-yr oldmoves out

Incorporating how customers are likely to change over time is critical to setting the right prices today …

Elasticity

Margin

Policy evolving over time


... there are both simple and complex models to incorporate significant customer changes ...

AgeCar AgeTenure…

Model changes to policy

characteristics

Accidents/ ViolationsClaimsCross sell/unsell…

Vehicle changesDriver changesCoverage changes….

DeterministicChanges

UncertainEvents

ExposureChanges

Co

mp

lexi

ty

15

Calculate impact on premium / pure premium / lifetime

value / etc

Incorporating accurate estimates of these changes in

to a price optimization framework has a significant

impact on optimized prices


… which leads to improved pricing decisions

• Sixteen year olds age, and expected loss costs decrease

• Many single product customers become multi-product over time, and this propensity varies by segment

• Customer expectations vary concerning accident/violation pricing

• Some customers go through major transitions, and don’t react to the price change like typical renewal business

Example decisions dependent on lifetime value models


• Building Blocks

– Elasticity

– Transitions

• Opportunities and Pitfalls

– Implementation

– Regulation

Agenda


There are unique implementation challenges associated with building a price optimization framework

• Data

– Transactional data grouped by event, not exposure period

– Quote data cleansing and de-duplication

– Competitor premium estimation

• Modeling

– Losses revised to reflect “continuous” nature of risks

– Elasticity bias and nuanced modeling

• Process

– Interface with other pricing tools, e.g. analytical reraters

– Interface with other processes, e.g. regulatory filings


Loss analysis

• Research: Estimates indicated rate changes with frequency / severity or loss ratio analyses and recommends rates …

• Product management: “Will these prices ‘work’ in the market?”

• A subjective debate ensues …

When communicated properly, a price optimization framework promotes collaboration between research and product management

Price optimization framework

• Research and product management compare notes in a quantitative environment … reducing dependence on instinct and anecdotal evidence

• Ensures that deviations from single term loss costs are in line with customer preferences and profitable for the company


A price optimization framework incorporates rather than replaces human judgment

Pricing managers• Same strategy and objectives• Similar data• Similar competitive environment• Making decisions on the same rating factors

Software tools• Quickly identify opportunities• “Test” the system• Automate steps in an iterative process• Enable the managers to focus on decisions rather than

mechanics

Optimized Pricing Framework

Be careful when discussing internally … optimization is not a black box process which changes the pricing strategy


Price optimization is compatible with regulatory constraints and the framework provides additional benefits

• Pricing decisions are already made – just adding science

• Forecasting is more accurate even if pricing decisions are not altered

• Impact of possible competitor actions can be understood

• Many “optimal” pricing decisions are in line with regulatory

constraints

• Complaints are reduced as pricing aligns with customer preferences

• Non-pricing initiatives can be monitored within this framework

Documents

Price Optimization Getting Started - the Conference Exchange · 2009-05-01 · Copyright © 2009 LexisNexis Risk & Information Analytics Group Inc. All rights reserved. Price optimization