Market Analysis - Willis Towers Watson · PDF fileMarket Analysis Consumer Insight and ......
Transcript of Market Analysis - Willis Towers Watson · PDF fileMarket Analysis Consumer Insight and ......
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
© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com
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
© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com
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
© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com
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
© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com
Media proliferation - both a challenge and an opportunity!
CAPABILITIES
© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com
Why do we need customer insight and segmentation?
Roaming Customers, Fragmented Channels, Consumer demand
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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
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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
© 2013 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.towerswatson.com
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
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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
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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”
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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
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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
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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
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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
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Recommended multi-dimensional segmentation approach for insurers
LTV
Policy InformationNCD, risk factors…
Socio-demographicAge, affluence, car ownership, home ownership, location
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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
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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
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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
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Customer Profile example
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Example - dashboard tracks segment migration for motor brand
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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
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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
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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
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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
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Best Practice — Insight Driven Direct Marketing
Evaluation and
Potential Rollout
Test Program Deployment
Target Segment And Test
Framework
Segmentationand Value
PropositionDescriptive
Analysis Data Exploration and
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
Improve retention rate by 10%
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
Improve retention rate by 10%
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
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Best Practice — Insight-Driven Direct Marketing
Evaluation and
Potential Rollout
Test Program Deployment
Target Segment and Test
Framework
Segmentationand Value
PropositionDescriptive
Analysis Data Exploration and
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?
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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
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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
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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
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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
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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
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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
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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
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…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
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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
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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
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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
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National vs. postcode — Model prediction comparison
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Observed Average Postcode Model Regional Model National Model
Modelling Period Prediction
Model divergence
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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.
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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
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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
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MediaOptimiser: Analyse results geographically
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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
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Web-based dashboards to track effectiveness
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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 !