Under the bonnet: Mosaic data, methodology and build · Innovation in Methodology Optimising Mosaic at a household level Household clustering using smoothed data ... Loss of variance

New Mosaic Launch event 1st April 2014 #OneMosaic

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Under the bonnet: Mosaic data, methodology and build

Paul Cresswell

Matt Holgate

Kevin Smith

Experian Marketing Services

2 2 © 2014 Experian Limited. All rights reserved.

Experian Public.

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1. The Mosaic Segmentation

Process

2. New Data to Identify New Trends

3. Innovation in Methodology

4. Optimising Mosaic and

Measuring Uplift

5. Bringing Mosaic to Life:

Interpretation & Description

6. The Mosaic DNA and Mosaic Data

Exhaust: other Mosaic data sets

Under the bonnet: what we’ll cover


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UK Economy now in recovery phase

New rental and owning patterns

New immigration trends

Trends in urban/rural migration, household composition, family structures

Our Mosaic build team were charged with creating a segmentation for today and tomorrow, identifying the significant new trends we’ve seen in society…

Societal changes mean we need new clusters relevant to

understanding the UK consumer of today and tomorrow


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• Our segmentation product knowledge and expertise ensures we continue to

develop innovative segmentation solutions. Mosaic UK development team have

over 100 years combined experience delivering global and regional solutions,

segmenting over 2.3 billion consumers and 700 million households

• Experienced architects developing new techniques to fully exploit depth of

consumer data available

• Mosaic optimises balance of geo-demographics and individual data, delivering

a stable, robust segmentation

• Experian invested significantly more than we’ve ever done to completely

redesign our interpretation and user tools to ensure Mosaic is easy to

understand and use

And we’re certainly well equipped….!


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1. The Mosaic Segmentation Process Building Mosaic: Step by Step

1. Design Requirements

2. Data Sourcing/Gathering

3. Data Processing

4. Clustering

5. Additional Reporting with Interpretative Data

6. Interpretation

7. Visualisation

8. Additional Outputs

9. Implementation

Building a

segmentation

isn’t just

about

clustering!


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2. New Data to Identify New Trends Depth and quality of data is at the heart of Mosaic

Through combined Experian proprietary, public and

trusted 3rd party sourced data, Mosaic condenses

billions of pieces of information into a concise, easy

to understand consumer classification

• Greatest ever volume and type of data

• Full assessment of new and existing data

sources

• Granular household data

• Key Census trends

• New data identifying new segments

• Investment in new research sources for

description and visualisation


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2. New Data to Identify New Trends Our person, household & neighbourhood data drives our most robust household level solution ever

850m+ source records with 450+ variables to fully understand consumer characteristics, lifestyles & locations

Consumers. Our ConsumerView picture of all UK adults created using hundreds of millions

of input records with actual data at individual and household level:

• Edited Electoral register

• Large scale contributor files

• Lifestyle survey responses

• Experian movers information

• Decades of archived data on years at address

• Family/personal names linked to ethnicity

• Directors from Companies House

Neighbourhood information

• ConsumerView variables accumulated to postcode level

• Rurality/Urbanisation

• Shopping accessibility measures

• Commercial/Residential mix

Census data Accurate, universal source of information

still very relevant in classifying consumers.

Covers household composition,

employment, ethnicity, dwelling type,

tenure, health, qualifications, industry,

occupations.

• Experian Credit non-CAIS risk indicators

• Land Registry data from 1995 onwards

• Registers of Scotland Transaction data

• Council Tax band

• pH data/National Business Database

• PAF including multiple residences

Neighbourhoods

& Places


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ConsumerView Variable Improvements using new data:

► Tenure & Property - Rightmove and National Register Social

Housing

► Children using Emma’s diary

► Northern Ireland detailed property data available for first time

► Improvements from refreshed census data used in calibrations.

Higher Education Statistics Authority (HESA) college and

university students database linked to home & term time postcodes

Transience & attractiveness - Experian movers data indicates

how populations are changing

OS Streetview - Mapping data about property area, ratio of

gardens to buildings, cul-de-sacs, sea/part/lake view

Census 2011 - a new snapshot of entire UK population

Accessibility to shopping centres, high streets; distance to

motorways, schools, railway stations, coast, GPs, bus stops….

Level of urbanisation/rurality based on population densities

Open Data - Police.UK crime data, GCSE results, Gas & Electricity

consumption, DWP child benefits, tax credits, income support by

Census LSOA areas

Broadband speeds (OFCOM) - Postcode level

2. New Data to Identify New Trends Just some of our new data sources directly allowing us to identify new segments……


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Data from Rightmove and Government Open Data enables more accurate household level tenure data. We can confidently differentiate owners, private renters and social renters at household level.

Example – identifying consumers with different tenures living alongside one another in an estate that was once all social housing

2. New Data to Identify New Trends New Tenure data identifies significant changes in property ownership at household level

J40 Renting a Room

Private renting

I39 Families in Need

Social renting

M54 Down-to-earth Owners

Budget owners


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Census 2011 is a strong contextual data asset that accurately shows key demographic

characteristics of whole population. Gives valuable neighbourhood information such as

ethnic diversity, employment and industry, levels of education and health

28% of information used to build Mosaic UK is from Census. Experian produce ongoing

Current Year Estimates of key census variables which feed into the build

2. New Data to Identify New Trends Census data – still a strong contextual data asset so why not use it

G28 Modern Parents L49 Dependent Greys

C10 Wealthy Landowners

Best health Worst health

Most cars

K46 High Rise Residents

L49 Dependent Greys

Fewest cars

Highest education Lowest education

Most professionals Most manufacturing

A03 Penthouse Chic

A05 Uptown Elite J40 Make Do & Move On


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2. New Data to Identify New Trends Higher Education Statistics Authority (HESA) identifying students

• More accurate assignment of students

• Most “studenty” type (O66 Student Scene) consists

of 77% students vs just 34% in previous Mosaic

Previous Mosaic New Mosaic


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Experience-driven design

of effective and unique

methodologies for variable

selection, smoothing,

transformation & weighting

Optimisation of household

level solution whilst

retaining benefits of

postcode data

Maximising discrimination

in larger urban areas

3. Innovation in Methodology Learning from over a hundred collective years of segmentation building…..


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Singular Value Decomposition (SVD) is an advanced analytics technique to reduce “noise” in big data, making it easier to spot patterns.

Method used for analysing other huge data sets in our business - Hitwise and Cheetahmail.

Allows us to use all household and person information about a household, converting binary response data into “probability” scores

Scores have distributions, so better suited to clustering

3. Innovation in Methodology Example - Smoothing Household Data using advanced analytics techniques

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New methodology clusters at household level using household & neighbourhood data to

exploit best clusters from both levels to drive optimum discrimination in all areas

3. Innovation in Methodology Optimising Mosaic at a household level

Household

clustering

using

smoothed

data

Postcode

allocation

Nearly 60% of households have a different

Mosaic Type to their postcode

Postcode DE75 7JA

Subgroups found even where they don’t group

together distinctly at postcode level

Household characteristics well defined across all age groups

Consumers in mixed neighbourhoods well defined by household characteristics

Neighbourhood characteristics still highly significant

Postcode allocation still retains optimum discrimination

Types dominated by locational factors such as rurality well defined


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Improved identification of consumer types who don’t always live together in the same postcodes

Take this street of Suburban homeowners where mixed lifestages often live in same road…..

3. Innovation in Methodology Optimising household characteristics

F22 Boomerang

Boarders

E21 Solo

Retirees

J42 Midlife

Stopgap

H30 Primary

Ambition

© Google © Google


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Improved identification of consumer types in rural locations

Consumers in rural locations can get grouped together because of location but in reality they

have very different incomes and lifestyles. Optimised use of household level data allows these

differences to be uncovered…..


D15 Local Folk

D16 Outlying Seniors

© Google

© Google

© Google


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Improved identification of emerging family types


B08 Bank of Mum & Dad I37 Budget Generations

Families supporting adult children - Given financial pressures on young people from

high house prices/rents, student loans, finding employment..., more couples continuing to

provide a home for their children into adulthood

New types where adult children are living at home - Bank of Mum & Dad who can afford

to support their kids to Budget Generations where older children living at home more

likely to cause financial strain to parents. Cultural Comfort & Asian Heritage also have

high incidences of adult children living at home.


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• Large urban areas very different to rest of UK and have seen big changes since last census

• Household level characteristics key in differentiating consumers in these locations - these differences can be lost at postcode level

• Use tried & tested methods

recently deployed in Tokyo - one of

world’s largest urban conurbations -

for Mosaic Japan

• Allows creation of more diverse

clusters, potentially new clusters and better discrimination

3. Innovation in Methodology Maximising discrimination in larger urban areas

Mosaic Japan - Metropolitan Elites


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In inner London number of

types with >2,000 households

increased from 10 to 13

• Renters / Homeowners /

Council tenants clearly

differentiated

• Tiers of wealth, age,

household composition,

property type well

differentiated within renting

majority in these areas

• Household clustering

distinguishes much more

variation in old O63 Urban

Cool and O64 Bright Young

Things segments

3. Innovation in Methodology Example - we’re seeing significant improvements in discrimination in London

Main types old O63 & O64 are

allocated to in new Mosaic


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Bloomsbury in London shows how new Mosaic picks out tiny pockets of council housing,

upper-class residences, students much better.

A01: World Class

Wealth

O66: Student Scene

K44: Inner City Stalwarts &

K45: Crowded Kaleidoscope

3. Innovation in Methodology Mosaic identifying micro-variations in urban areas


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For those who understand these things….!

Average distance to cluster/total error in

cluster

Relative sizes of clusters

Loss of variance of first join in dendogram

Variance explained in individual variables

Regional analysis

Characteristics of clusters make sense

4. Optimising Mosaic & Measuring Uplift Settling on a Solution: Selecting the best from the rest

In our segmentation process we

create hundreds of potential solutions.

We use a wide range of diagnostics to

test, select, validate and interpret

clusters created.

But a key “acid test” is that

characteristics of clusters

make sense and they’re

identifying key trends


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4. Optimising Mosaic & Measuring Uplift Overall Performance improvement: 20% Uplift

Measure performance of Mosaic to

discriminate client customer bases

using

► Total Weighted Deviation -

how much client customer file

differs from UK in terms of it’s

Mosaic Distribution

► Lift Score – % of maximum

possible lift gained above

random assignment of types

Calculated these metrics across

>50 behaviour files covering many

different vertical markets.

On average seeing 20%

uplift in these metrics

compared to Mosaic 2009 By targeting 5% of our ConsumerView universe

you’d reach 21% of client’s customer base with

new Mosaic, but 16% in previous Mosaic


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Rich depth of interpretative data paints detailed picture of who target audiences and customers are

More than 4,300 profiles considered while writing descriptions

Completely new visualisation portal

5. Bringing Mosaic to Life: Interpretation & Description New Interpretive data & visualisation tools


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5. Bringing Mosaic to Life: Interpretation & Description New Interpretive data & visualisation tools


Experian Public.

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5. Bringing Mosaic to Life: Interpretation & Description Experian commissioned Research Now survey around technology and digital behaviour

O66 Student Scene

Watching content on smartphone,

tablet, laptop, PC: Every Day

Smartphone Usage

J41 Disconnected Youth

N59 Asian Heritage

A03 Penthouse Chic

Multiscreen Content


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6. Other Useful Mosaic Data Sets Making the most of segmentation assets

Mosaic Segments - the DNA or basic building blocks of Mosaic. Can be combined on basis of specific data to create a bespoke segmentation to meet your needs

Mosaic Factors distil underlying data used to build each Mosaic, summarised into continuous non-correlated variables

“Distance” to Type measure extent to which household or postcode fits its allocated Type

2nd Best Type indicates which Mosaic Type is next best fit

Affinity Measures indicate how close a household or postcode is to ‘tipping point’ between Mosaic Types

Geographic aggregations of Mosaic mix to many geographical areas for use in location insight

Mosaic DNA!

Mosaic Data Exhaust!


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6. Other Useful Mosaic Data Sets Example: Segments – the DNA of Mosaic

Mosaic Types

Mosaic Segments

Custom

Segment A

Custom

Segment B

Custom

Segment C

Bespoke Customer

Segmentation

Mosaic UK Segments are 238 subtypes split

out of 66 Types, providing further level of

discrimination for profiling & analysis

Build customised clusters with even more discriminatory power for clients – from

Mosaic DNA

Detailed household consumer

sub-types created using input

data from Mosaic.

Target organisations with niche

audiences not adequately

identified by Mosaic classification

Allows clients to capitalise on

input data used to build Mosaic &

build own bespoke

classification that can be linked

back to Mosaic

Our Mosaic Custom tool

developed to do exactly this!


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New Mosaic Understanding the UK consumer today and tomorrow, through:

New data identifying new trends

Innovative in methodology

Bringing Mosaic to life through new visualisation tools and customised solutions


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Ask our Mosaic build experts

Questions


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[email protected]

@ExperianMkt_UK

0845 234 0391

www.experian.co.uk/mosaic

Documents

Under the bonnet: Mosaic data, methodology and build · Innovation in Methodology Optimising Mosaic at a household level Household clustering using smoothed data ... Loss of variance