Market Analysis - Willis Towers Watson · PDF fileMarket Analysis Consumer Insight and ... including Siebel, ... Integrating pricing, segmentation, media optimisation and contact management

Market AnalysisConsumer Insight and Segmentation

A presentation to Irelandby Ian Liddicoat February 2013

© 2013 Towers Watson. All rights reserved.

© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com

Topics to be covered!

l The consumer view – its actually what matters

l Segmentation approaches

l Segmentation and pricing in insurance

l Reporting and tracking

l Customer insight applications – Media optimisation

l Wrap up

l Please ask questions as we go – Its more fun


Ian is the director of Marketing Sciences delivering market leading analytical and technological solutions through Towers Watson to the marketplace.

He has spent the last 18 years in Marketing Services developing consultancy solutions in a range of markets in the US and Europe.

BackgroundIan started his career as a Senior Analyst with the Ministry of Defence before he moved to Infolink Equifax as Director of Marketing Services and Director of Credit Scoring. He then joined Claritas as Director of Information Systems and Managing Director of Consultancy. After that, he was with IPG/Draft Worldwide as Director of Micromarketing Consultancy, before starting up his own consultancy business, Monday International Ltd.

He has held senior management positions in Data Analysis, Direct Marketing, Information Technology, Risk Management and Marketing Strategy. Ian has also spent time working in the United States on Category Management assignments for Mars/Pedigree and Unilever, and Risk Management projects for prominent financial institutions. Ian has managed major projects for a number of banks, building societies, retailers, packaged goods, automotive companies and technology vendors. Ian has managed a number of enterprise IT implementations including Siebel, Oracle and large mainframe solutions in packaged goods, broadcasting and financial services.

Following the sale of this business to Grey Worldwide (WPP) Ian went on to build this into MDS global consulting providing marketing consultancy solutions across 14 markets and building revenues to just over £10M. Ian now leads the TW Marketing Sciences practice.

QualificationsIan has obtained an Honours Degree in English and History. A Diploma in Direct Marketing from the Institute of Direct Marketing, and an MBA from Kingston Business School.

Ian Liddicoat, MBA, IDMManaging Consultant, EMEA


Introduction to my presentation

l My background is Marketing analytics for 25 years.

l I worked across sectors with Equifax, Claritas and WPP and for the past 5 years with EMB and now Towers Watson with a particular focus on Life and Non Life Insurance.

l I am a SAS and Emblem user, have an MBA and lecture on the Institute of Direct Marketing Diploma course. I took this in 1994 winning the British Airways sponsored Top Student prize.

l We unite strategy with analytics, data and technology

l We have particular strengths in Direct to Consumer because of data availability.

l Lets make this interactive and hopefully thought provoking


Towers Watson Marketing Sciences — Our Services

Prospect Pool

Web Analytics

Customer Analytics

Segmentation

Pricing

Bespoke Research

Media Optimisation

Neolane Campaign Management

Decision Support Dashboards

Technology Consultancy


Media proliferation - both a challenge and an opportunity!

CAPABILITIES


Why do we need customer insight and segmentation?

Roaming Customers, Fragmented Channels, Consumer demand


New Challenges for Marketing & Communications?

l Traditional Marketing Losing Relevance

l Impersonall Unidirectional

l Perceived as intrusive

l Reestablish Customer Intimacy via Conversational Marketing

l Personall Conversation

l Unify service & marketing to create value


Integrating pricing, segmentation, media optimisation and contact management – It has to be this way!

Quotes Research Claims Payments Contact History Complaints Suppressions Media

Performance

Web Broker Response Data Channel Data Third Party Prospects

Third Party Geodems

Third Party Perils

AuditData Modelling

ExtractionDe-duplicationTransformation

Meta Data Generation

External Data Media Optimisation

Segment Migration

Inbound

Outbound Execution

Dynamic DashboardReporting/MI

CLV Measures CLV Rules

Value-based CustomerSegmentation

Single Customer View

Optimised Prices


Insight requires data

Solution Hosting Data Auditing Call Credit Multi-Channel Campaigns KPI Definition

SCV Generation Address Cleansing Experian Content Management Report Designand Development

Data Modelling Data De-duplification Acxiom Contact Optimisation Hosted Web Solutions

Database Designand Development Data Matching Equifax Email Deployment

GIS Mapping Suppression Screening Ordnance Survey Social Media

Analysis

Data Strategy Consultancy

Meta Data Management Transactis Real Time Web

Optimisation

Technology Consultancy Royal Mail Lead Management

Data Services

Database and Technology Data Quality Third Party

Data IntegrationCustomer

CommunicationDashboard Reporting


Third Party Data Integration used in segmentation and customer analytics

l We have relationships with most of the major external data suppliers l Our extensive experience and knowledge of integrating third party data

sources with our clients customer data enables our clients to realise the maximum value from these external data sources


DashboardSegmentation - Overview

Segmentation allows you to understand your portfolio and how it is changing over time and apply strategic decisions consistently across the company

SCV

Cluster Data

Strategy Segment Product Sales channel Strategy Impact

1 Segment A: Older high-value customers with families Home Services Phone €50 High street voucher Increase conversion

rate by 2%

1 Segment C: Young cash-conscious customers Gas/Electricity Online €20 Cash back Increase conversion

rate by 5%

2 Segment C: Young cash-conscious customers Gas/Electricity Online €20 Retail voucher Increase conversion

rate by 5%

1 Segment G: Middle aged mid-value customer who purchased basic product early on in life Online Services Online Cross sell additional home services 25% conversion rate

Segmentation OutputSegment definition — Set of rules that allows customers to be categorised into segments. Examples: l Ages 50+ with children -> “segment A:

Older high-value customers with families”l Ages 35-50 and with a product loyalty

score > 20 -> “segment G: Middle aged mid-value customer who purchased basic product early on in life”


Recent example – UK Motor Market Segmentation

Aggregator Internet Phone Total Aggregator Internet Phone Total Aggregator Internet Phone

1 Less affluent inner city 30s males 322 21,727 20,018 42,066 5.47% 7.99% 7.75% 7.84% 70 102 99

2 Affluent married 40s females with protection 256 21,807 30,966 53,029 4.35% 8.02% 11.98% 9.89% 44 81 121

3 Affluent single 40s females without protection 587 25,387 24,420 50,394 9.97% 9.33% 9.45% 9.40% 106 99 101

4 Married 30s without protection 1,228 50,680 35,657 87,565 20.86% 18.63% 13.80% 16.33% 128 114 85

5 Married 30s females with protection 413 21,956 26,300 48,668 7.01% 8.07% 10.18% 9.07% 77 89 112

6 Affluent 40s males without protection 979 41,762 34,550 77,291 16.63% 15.35% 13.37% 14.41% 115 107 93

7 Unmarried females with protection 320 14,797 19,184 34,301 5.44% 5.44% 7.42% 6.40% 85 85 116

8 Less affluent 30s males without protection 366 17,118 11,310 28,793 6.22% 6.29% 4.38% 5.37% 116 117 82

9 Affluent married males with protection 633 23,427 25,158 49,218 10.76% 8.61% 9.73% 9.18% 117 94 106

10 Less affluent 40s males without protection 252 14,605 16,798 31,655 4.29% 5.37% 6.50% 5.90% 73 91 110

11 Inner city unmarried females 529 18,758 14,083 33,370 9.00% 6.90% 5.45% 6.22% 145 111 88

5,885 272,024 258,442 536,351

Index compared to total% of ChannelCount

5060708090

100110120130140150

1 2 3 4 5 6 7 8 9 10 11

Aggregator Internet Phone

SEGMENTATION


Implementing Segmented Marketing

The applications of a Segmentation are broad reaching:

Contact Strategy

Retention Programme

Product Acquisition

Cross Sell/Up Sell

Life stage and Event Triggers

Performance Measurement

Campaign Performance

Key Performance Indicators

Analysis and Insight

Strategic Planning

Marketing Communications

Product Proposition

Direct versus Intermediary

Channel Development


Trade-offs between types of segmentation

Brand Marketing Investment Channel

MassMedia

TargetingDirect

Targeting Messaging

Attitudinal

Needs Based

Behavioural

Demographic

Value

High

Medium

Low

Low

Low

Low

Medium

High

Medium

High

Medium

High

High

Medium

High

Medium

Medium

Medium

High

Low

High

Medium

Medium

Medium

High

High

Medium

Low

Low

Low


Types of segmentation

Segment customers by:

Demographic Age, affluence, gender, life stage, household composition, location, etc.

Behavioural Transactional data such as recency and frequency of purchase, channel, payment methods, attrition rate

Needs Based Underlying or stated product/service needs derived from primary research and/or behavioural data

Attitudinal “Psychographics” — general attitudes and aspirations, often through third-party research

Value Current or potential value, or LTV


Recommended multi-dimensional segmentation approach for insurers

LTV

Policy InformationNCD, risk factors…

Socio-demographicAge, affluence, car ownership, home ownership, location


Framework for Building Customer P&L

How It Might Look LikeOverview of the Approach

Customer P&L will be built based on the two stage approach:

– Stage 1: Create a P&L which includes GWP and claims cost / loss ratio only (Q2 2013)

– Stage 2: Add CET, Expenses, Other Income and Commission to build a fuller P&L. Initially, figures for these new additions are likely to be mainly assumption driven (Q4 2013)

– Note: the P&L will be built based on assumptions and is shown on a written rather than earned basis. If required, we could look to address these issues in the later stage

17

Segment 1 Segment 2 Segment 3 Segment 4 Segment 5 Segment 6P&L SummaryGWP

Claims cost

Claims expense transfer

Expenses

Other Income

Commission

Profitability

KPIs

# PIF

# New Policy

# MPH

Retention Rate

Average GWP

Acquisition Costs / Policy

Expense / Policy

Note: The Customer P&L should be reconcilable to product P&L


Differentiated price sensitivity by segment

Relative Likelihood of Acquiring a TD

50

75

100

125

150

175

200

. . . . . . . . . . . . . . . . . . . . . . . . . .

Less Competitive More Competitive

Measure of Price Competitiveness

Rel

ativ

ity

Segment A Segment B Segment R Segment S

Stables

Switchers

Reluctant Loyals

Advice seekers


Major activities of a segment-driven business

Assess Current state

New approach

Develop segments

Develop value

proposition

Define & deliver

capabilities

Historic customer value

Old approach

Continuous tracking

Forward-looking

expected customer value

Single segmentation criteria -value/ demographic

Multi-criteria based on

customers needs, value received &

given

Targeting based on most

profitable segments to

date

Targeting based on

expected future value

Differentiation from

competition

Insights are synonymous

with IT analytics tools?

Insights are context-based?

Segment effectiveness not tracked

Month-on-month tracking

by segment, across all channels


Customer Profile example


Example - dashboard tracks segment migration for motor brand


Strategy into Action

Value Segments

Movement and Trends

Behavioural Data

1 2 3 4 5 6

Customer Lifecycle & Trigger Events

Where’s the value and opportunitiesin the customer base?

Share of Wallet Contact historyIn/Outbound Life style

Financial Sophistication Product Repertoire Customer Research

Risk of lapsing Stage in Relationship

Increasing Relationship

Stable Relationship

Decreasing Relationship

Renewal dateWhen do we target them? What message?

What’s the customer proposition? And why?

Who do I target & Why? And what’s the possible return for doing so?

We use insights to create ‘test and learn’ campaigns


Best Practice — Insight Driven Direct Marketing

Evaluation and

Potential Rollout

Test Program Deployment

Target Segment And Test

Framework

Segmentation and Value

PropositionDescriptive

Analysis Data Exploration and

Initial Audit

Customer Data

PolicyData

ClaimsData

Sales Data

1. What data is available to analyse / understand current renewal performance?

Policy IDNational IDAgeSexLocationOriginal offer

Assume there is more customer level data available

Policy IDCar modelCar ageStart / renewal date Original premiumRenewal quote

Derived variablesActual premium increasePercentage premium increaseLength of relationship

Policy IDClaim ever Y/NClaim in last 12 monthsClaim amount

Policy IDDirect or Internal SalesSales Person IDChannel , e.g., phone, web,

2. Descriptive Analysis to understand drivers of retentiona. Univariate analysis This does not deal with causes or relationships; it looks to describe central tendency — mean, mode, median dispersion — range, variance, max, min, quartiles, standard deviation. frequency distributions e.g., renewal rate by age, sex, car model, car ageb. Bivariate or MultivariateThis looks to explain the cause or relationship of variables of an outcome:l Analysis of two or more variables simultaneously l Correlations l Comparisons, relationships, causes, explanations l Tables where one variable is contingent on the values of the otherl Independent and dependent variables c. Renewal Model We would also look to build an initial renewal propensity model from the analysis above to score the customer base given the data available

Phase 1

Outputs from the data exploration stage include variable population, completeness of fields, valid ranges, outliers / erroneous data


How to rise to the challenges…………………

The challenges facing marketing functions and the demands of the customer require data, analytics and technology to be integrated and decisions made increasingly in real time


Customer segmentation – Dynamic and practical to use

Customer Segmentation is a process that involves the division of a customer base into actionable homogenous groups to enable customized communication and differential investment

Analytical Consultancy/Delivery MarketingData

Customer Behavior

Transactional Analysis

Research

Demographics/Lifestyles

The Right Channel

The Right Time

The Right Customer

The Right Message

PredictiveStatistical Modelling

Segmentation

Customer Profiling

Customer Knowledge


Best Practice — Insight Driven Direct Marketing

Evaluation and

Potential Rollout


Target Segment And Test

Framework

Segmentationand Value



Initial Audit

Phase 2

Age

Renewal % Increase

Car model

Based on information we have on individual customers, we would look to segment them and define propositions per segment

Segment Strategy Investment Channel Offer Impact

Segment 1: Older family car drivers

Retain Mid – High Personal contact

5% discount on renewal

Improve retention rate by 5%

Segment 2:Younger female hatchback drivers

Grow Medium Phone and Email

12 months cover for 11 months premium


Segment 3:Younger high-risk drivers, rural

Minimise Low Email only Standard terms Improve profitability by lower costs

Segment 4:Younger city families, low to medium risk

Grow Medium Phone and Email

12 months cover for 11 months premium


Segment 1 Location Value Likelihood to renew AudienceCustomer 1 Beijing 700 45% testCustomer 2 Shanghai 500 50% testCustomer 3 Jiangsu 800 47% controlCustomer 4 Guangdong 750 42% controlCustomer 5 Beijing 800 44% controlCustomer 6 Shanghai 800 45% controlCustomer -- Zhejiang 750 47% controlCustomer 9999 Shenzhen 600 50% control

With access to all the customer-level data, we can see the impact of the offer on overall renewal, as well as being able to analyse and identify any specific areas of outperformance or underperformance in the activity, e.g., certain car makes, cities, previously claimed, renewal month, which will allow us to further refine the renewal activity moving forward.

1. Example Segments 2. Example Segment Strategies and Propositions

3. Defining target audience for test activity


Best Practice — Insight-Driven Direct Marketing

Evaluation and

Potential Rollout


Target Segment and Test

Framework

Segmentationand Value



Initial Audit

Phase 3

l Define and agree the target audience that we want to run the initial test activity against l Set up control group for assessment of uplift l Ring-fence test group from any other activity l Ensure accurate KPIs are set for both test and control groups — pre-test levels for benchmarking

1. Target audience

2. Agreement on timeframe for the test program

3. Assessment of results against the control groups

4. Depending on the results of the test

l Define valid testing period for activity — will depend on volumes of policies for statistical significance

l Based on pre test KPIs — how did the test group compare to: l Previous performance — did it uplift against historic levels?l Performance of the control group — did the test group outperform the control group?l Did the control group also improve during the test for some reason?

l Were there any significant areas that outperformed or underperformed in the test?l Different offices performing differentlyl Different offers / incentives worked better than others if we tested more than onel Did certain customer types / retention profiles outperform / underperform?

l What was the financial impact of the activity — how much incremental revenue and ROI from the test?l If we extended this, what is the impact?

l Do we extend the activity this to the rest of this segment?l Do we set up a further series of test activities for this segment — a champion v challenger program

— to continually try to improve performance?l Do we now set up a test strategy for other segments to improve their performance?


Best Practice — Insight-Driven Direct Marketing

Segment 3

January

March

May

Potential Champion/Challenger Set Up

Mobile/Social Email Other

Offer 1

Offer 2Control

Offer 2Message 1

Offer 2Message 2

ControlOffer 2

Customer Database

January

Performance of test assessed against control to determine best route for subsequent activity

Offer 1

Offer 2Control

Best performing offer taken forward as champion. Message added as new dimension for testing

Learning from email applied to Mobile / Social channel

O2/M2Creative 1

O2/M2Creative 2

ControlO2M2

Offer 2Message 1

Offer 2Message 2

Control

Customer Database

March

Customer Database

May

Best performing message taken forward as champion. Creative added as new dimension for testing

Results shape subsequent activity

Segment 2

Segment 1

Results shape subsequent activity

Results Analysis and learning

Learning from email applied to Mobile / Social channel


Online customer analytics

ModelInformation

Customer

DataCollected

Landing PagePrice-Sensitive Customer

Landing PageLoyal Customer

Landing PageMultiple Product Customer

Application of Models

Price ModelAlters price to maximise

conversion rate of customer

Cross Sell ModelAims to increase product

holdings of existing customers

Retention ModelAimed to deliver incentives

to retain customers

Modelled

Dynamic Interactive Online System


Media optimisation

Media optimisation provides a quantitative way to properly attribute customers to the correct sales drivers and to alter the new business mix through advertising

Media

SCVSEGMENTS

Media Models Media Optimiser

l Scenario testl Optimisel Identify driver of sales

Dashboard

Campaign Management

2012 Media Budget

Optimal Schedule


Our footprinting methodology allocates media to customers to build more granular and sophisticated models (UK example)

Customer Data

l Product holdings

l Value

Demographics

l Age

l Gender

l Social grade

l Ethnicity score

l Vehicle ownership/non-declaration

l Crime

l Unemployment

l DVLA stats

Response Data

l At varying levels of geography

l Differing KPIs

Geography

l Postal geography

l TV regions

l Catchment areas

Media


Market/External Factors Offer/Price/Customer

Competitor Activity Media

We consider several hundred factors that may predict enquiries or sales

PredictiveFactors

l GDP growthl Unemploymentl Interest ratesl Seasonalityl Sociol Consumer confidencel Demographics

l Vehicle ownershipl Public holidaysl Inflationl Weather

l Headline pricel Campaign offerl Fees and chargesl Product holdings

l Demographicsl Contract periodl Discount ratel Installation costl Minimum costl SEGMENT

l TVl Radiol Pressl Product launches

l Magazinesl Onlinel Price/offers

l TVl Radiol National pressl Cinemal Magazinesl Local press

l Online searchl Sponsorshipl Online displayl Direct maill Door dropsl Cross-selll Outbound telemarketingl Directoriesl Outdoor


Our approach can be tailored to business priorities, data availability and local market conditions

Breadth

Monthly

Weekly

Daily

Depth

National Regional Postcode Sector

l Accuracyl Upliftl Insight

Household level

Considerations include:

l Required speed of turnaround

l Maturity of the local market

l Availability of data

l Budget spend


Modelling at the household level allows us to properly isolate the contribution of each factor to response…

l Media factors can all be included more accuratelyl Above the line, below the line, online

l Socio-demographic and other factors can be included more accuratelyl Different types of customers are differently attracted to your brand

l Branch locations by postcode, etc.l Other product penetration by postcode segment/postcode district

l Serviceability by postcode

l Price-related factors can be included more accuratelyl Level of competition may vary by region (e.g., insurance)

l Price will have a different impact for different types of customersl Conversion and value models can also be built at a household level

l These can be combined with the response models to evaluate the true value of media deployment


…but what if we had more historical data? R

espo

nse

Upl

ift

Exposure

120

115

110

105

100

95

90

0 40050 100 150 200 250 300 350


We now have sufficient data to isolate the ‘pure effect’ of each variable on sales…

Res

pons

e U

plift

Exposure

120

115

110

105

100

95

90

0 40050 100 150 200 250 300 350

No uplift in sales below 50 TVRs No additional sales

after 300 TVRs


A more granular approach also allows observation of decay rates directly from data

l Traditional approach tests different candidate decay rates in the models to determine which appears to fit the data well

l The decay rates contribute to ad stock or how a response to media in a given week also generates response in subsequent weeks

l With more data, we are able to observe the shape of the decay curve and apply these concepts to all media. Again this is essential to support strategic media allocation decisions

l We have also observed, in some cases, that the decay does not apply from the date of the impact (as shown below)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12

Pro

porti

on o

f res

pons

e fr

om m

edia

Weeks since exposure

25% 30% 35% 40% 45% 45% 50%

0%

20%

40%

60%

80%

100%

0 2 4 6 8 10 12 14 16

Pro

port

ion

of re

spon

se fr

om m

edia

Weeks since exposure

y norm Observed decay Curve 50% Asserted decay curve 35% asserted decay curve


Evaluation

l Our target is for +/-5% accuracy against the out-of-time data

l The example below shows +/-6% of accuracy from a client during a period of substantial market change from the growth of aggregators

Actual vs Expected - Internet

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Week Number

Num

ber o

f Quo

tes

Fitted Quotes Observed QuotesValidation Period


National vs. postcode — Model prediction comparison

0

1

2

3

4

5

6

7

2008

14

2008

17

2008

20

2008

23

2008

26

2008

29

2008

32

2008

35

2008

38

2008

41

2008

44

2008

47

2008

50

2009

01

2009

04

2009

07

2009

10

2009

13

2009

16

2009

19

2009

22

2009

25

2009

28

2009

31

2009

34

2009

37

Observed Average Postcode Model Regional Model National Model

Modelling Period Prediction

Model divergence


Optimisation process

l The optimisation process works by trading off spend and the target KPI, e.g., Sales between media containers defined by flighting patterns and between media. The KPI is then calculated using the models

l The spend is then reduced for the container that has the least impact on the KPI and the process repeated to create an efficient frontier

l The optimisation algorithm avoids the need to calculate every possible permutation of spend

Period 1 Period 2 Period 3 Period 4 Period 5 Etc.TV containers

Period 1 Period 2 Period 3 Period 4 Period 5 Etc.Radio containers

Etc.


MediaOptimiser: Input schedules

l Individual schedules or parameters for optimisation can be input or modified directly or imported to MediaOptimiser utilising the range of footprints available

l Optimisation parameters can also be defined to test alternative flighting patterns, e.g., continuous or pulsing, and set maximum/minimum levels of spend for each media type


MediaOptimiser: Defining efficient frontier and comparing current schedule

Current schedule

Efficient frontier. No combination of media for the given level of spend can yield a greater response

Greater response for the same spend

Same response for less spend


MediaOptimiser: Analyse results geographically


Understanding the true effects of media

l Lag effects, particularly from television exposure can be modelled effectively. The chart below shows the result from the model of continuous TV exposure and the residual effect when TV is stopped in week 135

When TV exposure is reduced to Zero, there is still a residual lift to sales

In week 136 TV exposure is reduced to zero. There is still a residual lift to sales due to the effect of the lag

Responses in week 126 benefit from the

lag from TV exposure in week 125


Web-based dashboards to track effectiveness


Summary

l Customer insight requires segments to be applied at customer level with agreed measures of LTV, CLTV

l Pricing and Marketing need to be aligned

l P&C insurers are now developing integrated customer segments

l Life insurers in Asia now have advanced segmentation driving loyalty schemes

l Predictive models have to be applied within segment and used collectively to manage all forms of customer inter raction

l Research must be within segment

l Segmentation is dynamic and must be reported as such

l The customer requires this level of personalisation and integration !

Documents

Market Analysis - Willis Towers Watson · PDF fileMarket Analysis Consumer Insight and ... including Siebel, ... Integrating pricing, segmentation, media optimisation and contact management