Client Named Contact

UK0001

21st Nov 2016

Example Client

Reference :

Data Audit ReportConsumer

Prepared for

All processing has been performed in line with our standard terms & conditions

Contact :

All processing has been performed in line with our standard terms & conditions

Contents

Overview

Sample of Data

Address Validation

Address Quality

Name Quality

Duplicates

Email Analysis

Telephone Analysis

Obscene Keyword

Part Two: Profile

Part One: Cleanse

Enhancements

About Me

Where I live

Finances

Lifestyle

Shopping Habits

Top Variables

Orchard™

Personar™

Contact Details

Quantity InvalidAll Records 48,563 1,756Supplied name and address 46807 1,756

Supplied email addresses 22,385 29Supplied telephone numbers 45,153 2,134

Poor Address Info 3,516

Duplicates 180

Goneaways 5,531

Deceased 161

Other Suppressions 11

Title Mismatches 0

Valid Records 37,419

Suppressed Records 9,388

Invalid Records 1,756

11,144

77%

11,144

£11,144

Overall Information

Records suppressed or invalid in total

of the file is mailable

37,419

9,388

1,756

Overview

Invalid Records

Based on a DM campaign we have identified the following number of records that may not be suitable for mailing:

Valid Records

Suppressed Records

The following figures are a brief summary of the more detailed pages found within this document.

In addition to the potential cost savings, there is also your adherence to data protection principles in order to "keep clean"your database(through effective use of suppressions), and remove those that do not wish to receive DM.

If we make an assumption that each pack is worth £1 on average for each mailing, this equates to a waste of:

Coupled with cleansed and deduplicated data this will enhance your customers experience with your communications.

x x

x x

x x

x x

x x

x x

x x

x x

No Field Name MinValue MaxValue PopulatedCount NullCount DistinctCount Occupancy

1 COMP_RECORD_REF2016090601/00001028 01/00001028 48563 0 48563 100.00%

2 COMPANY_RECORD_NUMBER00001028 00001028 48563 0 1313 100.00%

3 SALUTATION Mr John Milburn (Jigsaw Financial Managment) 48530 33 20665 99.93%

4 TITLE Mr & Mrs 46619 1944 21 96.00%

5 INITIALS FSAUO 47043 1520 48 96.87%

6 FIRST_NAME For Sales Admin Use Only 46286 2277 3290 95.31%

7 SURNAME Essex Police Transport Service 48553 10 14181 99.98%

8 ADDRESS_1 Ministry Of Defence Police Headquar 46588 1975 40701 95.93%

9 ADDRESS_2 Jubilee Avenue",Highams Park Industrial Estate" 26985 21578 7098 55.57%

10 ADDRESS_3 Bellringer Road",Trentham Lakes South" 12290 36273 2593 25.31%

11 ADDRESS_4 Bishops Stortford Herts 43869 4694 1092 90.33%

12 ADDRESS_5 Kirkcudbrightshire 40716 7847 109 83.84%

13 POSTCODE 020 8531 9225 46793 1770 21866 96.36%

14 PERSONAL_PHONE 01376 322074TPS 28569 19994 27109 58.83%

15 WORK_PHONE 07765 672066mrs 5307 43256 4922 10.93%

16 FAX_NUMBER 07740704396(MRS 807 47756 565 1.66%

17 MOBILE_NUMBER 07836 288507TPS 36507 12056 35786 75.17%

18 JOB_TITLE Self Employed Plumber/Heat Engineer 247 48316 186 0.51%

19 BUSINESS_TYPE XXX 1407 47156 48 2.90%

20 SOURCE_OF_BUSINESS RTC 32079 16484 92 66.06%

21 BRANCH 001 48558 5 15 99.99%

22 SUFFIX spjuk ltd 44965 3598 18 92.59%

23 EMAIL transportservicescontrolcentre@ukpowernetworks.co. 22385 26178 21958 46.09%x

Input Data ValuesThe following table details the key fields supplied together with the range of values in them, as well as the level of field population

Before processing the number of mailable records were 46,319 which is 98.96% of your file. After processing it becomes 46,513 which is 99.37%. That is a change of 194

Quality Volume %

Mailable 46,319 98.96% 194

#REF!

Not Mailable 488 1%

Total 46,807#REF!

Quality Volume %Paf Validated 46,336 98.99%Paf Fail (Mailable) 177 0.38%Paf Fail (Unmailable) 294 0.63%Foreign 0 0.00%Total 46,807

Do Not Mail records are generally not recommended to be mailed. Significant key

elements of the address are missing such as the town or postcode.

Best Quality records are PAF matches and ideal for mailing. After cleaning, this

equates to 46,336 records which is 99% of your file.

Mailable records have all the important address elements but could not be found on

the PAF file. These records should still be considered for mailing."

After Clean

Before Clean

Address Validation

This page provides a summary of the quality of your data in its current state and then after our address validation process has been applied.Before & After

Mailable

Non-Mailable

Best Quality

Mailable

Do Not Mail

46,336

177

294

Results

Male gender identified: 30,218

Female gender identified: 15,301

Unknown gender: 1,288

Records A B C D E F G H Total GOOD GOOD % OK OK % POOR POOR %

19,999 35,840 5 8,955 5 1,528 8 343 123 46,807 44,800 95.71% 1,533 3.28% 474 1.01%

A

B

C

D

E

F

GH

CODE

SURNAME and FORENAME with KNOWN TITLE

SURNAME with TITLE and FORENAME

Other

SURNAME and TITLE present

SURNAME and FORENAME present

SURNAME with KNOWN TITLE

FORENAME present

SURNAME present

Name Quality

Records Records in data: 48,563

Address Address Level Duplicates 1,980

Household Househouse Level Duplicates 1,847

Individual Individual Level Duplicates 209

Address where the individual residesMs Pinn 19 Heywood Way Heybridge CM9 4BH

Mrs Y Yvonne Pinn 19 Heywood Way Heybridge CM9 4BH

Mr D David Cope Copes Cobox Limited RM17 5DL

Mr D David Cope Copes Cope Estates Ltd RM17 5DL

Mr J Jim Cave 22 Crummock Close Great Notley CM77 7UP

Mr Cave 22 Crummock Close Great Notley CM77 7UP

Mr P Pete Dickens 67 East Street Coggeshall CO6 1SL

Mr P Peter Dickins 67 East Street Coggeshall CO6 1SL

Mr K Keith Hunts Woodlands Park Hall Road CO9 1SQ

Mr K Keith Hunt Woodlands Park Hall Road CO9 1SQ

Address level sample duplicate records HouseHold Level sample duplicate recordsAddress where the individual resides Address where the individual residesMr S Steve Dolan Ductclean Uk Ltd 1 Woodfield Road AL7 1JQ Mr B Brian Durrant 21 High Street Puckeridge SG11 1RNMr A Andy Wallace Ductclean Uk Ltd 1 Woodfield Road AL7 1JQ Miss T Tracey Durrant 21 High Street Puckeridge SG11 1RN

Mr N Norman Shaddick First City Care London Plc 2-4 Little Ridge AL7 2BH Mrs H Helga Edwards 47 Firs Chase West Mersea CO5 8NNMr J James Manning First City Care London Plc 2-4 Little Ridge AL7 2BH Mr K Kevin Edwards 47 Firs Chase West Mersea CO5 8NN

Mr T Tony Humphreys Autolease Ltd Blake House B26 3RZ Mr A Alastair Nye 16 Stambourne Road Toppesfield CO9 4DGMr Pattenden Autolease Ltd Blake House B26 3RZ Miss S Samantha Nye 16 Stambourne Road Toppesfield CO9 4DGMr T Trevor Downey Hatchford Way B26 3RZ Mr R Richard Crowe 39 Brook Meadow Sible Hedingham CO9 3PJMr Simpson Hatchford Way B26 3RZ Mr P Patrick Crowe 39 Brook Meadow Sible Hedingham CO9 3PJ

Mr Belcher Masterlease International House B37 7HQ Mr C Christopher Abbott 51 Brook Meadow Sible Hedingham CO9 3PJMr R Roger Little Masterlease International House B37 7HQ Mr N Nigel Abbott 51 Brook Meadow Sible Hedingham CO9 3PJMr R Russell Knight First In Service Ltd Unit 2 B7 4PR Mr S Steve Young 41 Great Smials South Woodham Ferrers CM3 5WNMr R Richard Wright First In Service Ltd Unit 2 B7 4PR Mrs C Christine Young 41 Great Smials South Woodham Ferrers CM3 5WNMr Line Chubb Fire And Security Ltd Shadsworth Business Park BB1 2PR Mr M Mike Hopper 46 Washington Road CM9 6BNMr Child Chubb Fire And Security Ltd Shadsworth Business Park BB1 2PR Mrs P Pamela Hopper 46 Washington Road CM9 6BN

Mr C C Brown Woolbro Distribution Ltd Woolbro Simba Toys BD4 7BG Mr S Stephen Willis 48 Washington Road CM9 6BN

Woolbro (Distributors) Ltd Woolbro Distribution Ltd Broomfield House BD4 7BG Mrs J Jennifer Willis 48 Washington Road CM9 6BN

Nationwide Churchill Insurance Co Ltd Churchill Court BR1 1DP Mr T Tim Hull 48 Washington Road CM9 6BN

Virgin Money Churchill Insurance Co Ltd Churchill Court BR1 1DP Mrs S Sharon Hull 48 Washington Road CM9 6BN

Individual level sample duplicate records

DuplicatesWe have run the data through our deduplication routines and can identify the following duplicates:

0

500

1000

1500

2000

2500

Address Level Duplicates Househouse Level Duplicates Individual Level Duplicates

Records in file PopulatedValid

(normal email

addresses)

Valid(free email addresses eg. hotmail

etc)

Invalid

48,563 22,385 4,776 17,580 29

Not Personal Personal Part Personal

No name /

not personal

Personal

(Email matches

supplied name/s)

Part-Personal

(Email partially matches

supplied name/s)

13,005 660 8,720

Email AnalysisOur email validation tools analysises the email data string to check for format and domain validity as well as breaking down the types of email addresses supplied

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Not Personal Personal Part Personal

Country RecordsTelephone numbers

supplied% Populated

Valid format/number

of digits% Valid

Unverified

format/number of digits% Unverified

United Kingdom 48,563 45,152 92.98% 44,673 98.94% 479 0.99%

Telephone Numbers

The counts combine all supplied telephone fields.

We have passed the supplied telephone numbers through a format check process and have identified the following:

All mobile, telephone and fax numbers have been standardised in order to be consistent throughout the file.

Please note that this process does not determine whether the number exists. Instead it checks the numbers according to common number formats for a given country, identifies those that are probably invalid and reformats the telephone number field.

2

Miss Nicola Bender 3A High Street

Ms Hilary Beaver 46 The Chase

Mr R Beaver Whitford Hatfield Broad Oak

Miss Charlene Loo 20 Gilchrist Way

Miss Esmee Gummer 28 Well Terrace Heybridge

Mr Paul Gash 17 Meeson Meadows

Mr Jack Offord William H Brown Coggeshall

Mr Chris Gummer 6 Lavender Walk

Mr Paul Slapper Thorney Bay Park Ltd Thorney Bay Road

Obscene KeywordsOur Obscene Keyword search flags records with possible expletives or issues.

We analyse the data fields separately to improve accuracy e.g. Jesus Christ may well be valid as part of a church name, however not expected as an individuals name.

Please note, not all of the below may turn out to be expletives - only a manual check can confirm. For example:

Total potential Obscene Keyword records found:

Mr George Balls - Likely to be a valid name

Examples:

Mr Hairy Balls - Likely to be an invalid name

Telephone Numbers Emails

Total Records: 48,563 Total Records: 48,563

Numbers Supplied: 45,153 Emails Supplied: 22,385

93% of records have a phone number 46% of records have an email address

3,094 No number/email supplied - and no

matches to greenstone's list of numbers

either

25,607

44,673

Supplied in correct format

22,356

796 Extra numbers/emails that can be

appended using Orchard600

Contact ChannelsAs part of the audit process, we have analysed how many telephone numbers and email addresses can be used for marketing, allowing you to target your customers through several channels to improve campaign performance

25,607

600

22,35

3,094

796

44,673

Breakdown of NumbersBreakdown of Numbers Breakdown of Emails

A multi-sourced UK consumer file offering unbeatable coverage of the UK

48 million consumers

26 million households

Over 350 pieces of information available to append to customer base

Extra contact info available also e.g. phone/email

The profiling information and extra information that we can append is powered by the Orchard Consumer Database

What is Orchard?

'About Me' ProfileThe following pages show the profiling results of your data. The individuals supplied in the input file have been matched to our profile base and the results gathered, analysed and split into the categories below.

1 About Me Personal Categorical information E.g. Age

2 Where I Live Geographical / Household Information e.g. Region

3 My FinancesIncome and investment information

4 My LifestyleInterests, Holidays and Charitable Donations

5 Shopping HabitsHow do they spend their money?

?

What is the Index Score?An Index Score is a way of comparing two dataset of unequal size

An Index Score of over 100 means that there were relatively more customers in that category than the population.

An Index Score of lower than 100 means that there were relatively fewer customers in that category than the population

An Index score of exactly 100 means that there were the same proportion of customers in the category as the population

For example, say 33% of the customer file was in the 35-45 age category and 30% of the population were in that category - the index would be 110

'About Me'

?0 50 100 150 200

18 - 24

25 - 34

35 - 44

45 - 54

55 - 64

65 - 74

75+

Index

Age Band

65%

35%

Gender

Male

Female

% of File

0 200 400 600

Other

Retired

Company Director

Director

Housewife

Unemployed

Armed Forces/police

Education/Medical

Middle Management

Public Services

Manual Factory

Professional

Office Admin

Craftsman

Self_Employed

Retail

Student

Index

Occupation

0

50

100

150

200

A B C1 C2 D E

Ind

ex

Social Economic Class

0

20

40

60

80

100

120

140

160

180

0 1 2 3+

Ind

ex

Presence of Kids

0 50 100 150 200

Widowed

Single

Divorced

Married

Living With Partner

Index

Marital Status

Where I live

?

0 50 100 150 200 250

Region : Scotland

Region : Northern Ireland

Region : North East

Region : North West

Region : East Midlands

Region : West Midlands

Region : Wales

Region : South West

Region : South East

Region : Greater London

Region : Islands

IndexRegion

0 50 100 150 200

Tied Occupancy

Council Renters

Home Owners

Private Renters

Living with Parents

IndexHome Ownership

0

50

100

150

200

1 2 3 4+

Ind

ex

Number of Bedrooms

0 50 100 150 200 250 300

<100k

100-150k

150-250k

250-500k

500k plus

Index

House Value

0

50

100

150

200

Up to 2 Years 3-5 Years 6-10 Years 11+ Years

Ind

ex

Length of Residency

0

50

100

150

200

Bungalow Flat/Maisonette

Semi-Detached

Detached Terraced

Ind

ex

Property Type

About My Finances

?0 50 100 150 200 250

Has loan

Has unsecured loan

Has 2+ loans

Has loan for home improvement

Has loan for consolidation

Has loan for other purchases (incholidays)

Has payday loan

% of file

Type of Loan

0 50 100 150 200 250 300

£0-£9,999

£10,000-£19,999

£20,000-£29,999

£30,000-£39,999

£40,000-£49,999

£50,000-£59,999

£60,000-£100,000

£100,000+

Index

Household Income

0 100 200 300 400

Has savings

Has ISA

Has Unit Trusts

Has Stocks and Shares

Has Investments

High Risk Investor

Lump Sum Investor

% of file

Type of Investment

74%

26%

Disposable Income

Yes No

0 50 100 150 200 250 300

Has credit card

Credit card balance 2000+

Has been refused credit in the past

Has 2+ credit cards

Has 2+ store cards

Spent 0-50 in last month on a credit card

Spent 100-250 in last month on a credit card

Spent 500+ in last month on a credit card

IndexCredit Card

About My Lifestyle

Broadsheet

Tabloid

0 50 100 150

Newspaper Type

Index

0

50

100

150

200

250

Ind

ex Holiday Destination

020406080

100120140160180

% o

f fi

le Holiday Type

0 50 100 150

Animal Welfare

Childrens Welfare

Disability

Disaster Relief

Elderly

Environment

Medical

Third World

Wildlife

Cancer

Homeless

The Blind

Mental Health

Regulary Donates toCharity

Consider CharityLegacy

% of file

Ch

ari

ty T

ype

Charities

0

50

100

150

200

0 1 2 3+

ind

ex

Number of Cars

0 50 100 150 200 250 300

angling

antiques or fine art

reading books & magazines

cars & vehicles

charity / voluntary work

cinema

competitions & gambling

cookery

DIY

drinks alcohol at home

eating out

environment / wildlife

exercise / sports

fashion & clothing

football supporter

foreign travel

gardening

playing golf

healthy eating

hiking & walking

home & family

internet & technology

music

news & media

organic foods

other

pets

photography

pub

self improvement / education

shopping

stocks & shares

theatre

TV & films

vegetarian products

vitamins and minerals

wine

Index

Inte

rest

Interests

Shopping Habits

0 100 200 300 400

Does not use mail order

Uses mail order

Uses mail order 5+ times per year

Buys clothes by mail order

Buys groceries by mail order

Buys home furnishings by mail order

Buys plants or bulbs by mail order

Buys vitamins by mail order

Buys wine by mail order

Products bought online/by Mail Order

0

50

100

150

200

250

Shop at Asda Shop at Morrisons Shop at Sainsburys Shop at M&S Shop at Tesco Shop at Waitrose Shop at Co-op

% o

f Fi

le

Preferred Supermarket

0

20

40

60

80

100

120

140

160

180

200

£0-£24 £25-£49 £50-£74 £75-£99 £100+

Ind

ex

Weekly Shopping Spend

Variable Category Z-Score (*100)

Credit Card : Has credit card 25

Has Investment : Has savings 26

Has Investment : Multiple Choice Answered 26

Has Mortgage : Have Mortgage 37

Variable Category Z-Score Holiday Booking : Holiday : 1+ times per year 23

Disposable Income : No -19

Gender : Female -20 Holiday Booking : Multiple Choice Answered 23Has Mortgage : No -46

Has Pension : No -32 Holiday Destination : Multiple Choice Answered 23Holiday Average Spend : 250 to 499 -11

House Value : <100k -13 Preferred Supermarket : Shop at Sainsburys 32Newspaper : No -12

Preferred Supermarket : Shop at Asda -18 Preferred Supermarket : Shop at Tesco 39

Preferred Supermarket : Shop at Morrisons -20

Recency : greater than or equal to 61 months -14 Region : South East 53

Top Performing VariablesWe have analysed which variables best describe your customers and would perform best if taken forward for segmentation / modelling:

Top 10 Variables?

What is the Z-Score?

The Z-Score is based on a statistical test used to see if one population is significantly different from another. Used with Index, the Z-Score can be used to rank variables to see which would be most powerful for segmentation/modelling.

A score of +/-3 means that, at the 99% confidence level, the customer set is significantly different to the wider population.

Bottom 10 Variables

How do customers like to receive information?

Marketing with personality

Personar is an attitudinal segmentation tool that is designed to help with messaging and creative communications to your customers

It allows creative content and execution to be tailored to a customer based on the type of personality they have.

It offers the opportunity to talk to customers and potential customers in a fresh and personalised way , talking to them in the way that is relevant to them and doesn’t turn them away. This allows you to deliver the right creative to the right customer segment to increase response

How do customers make their decisions?

Personar™ is a psychographic segmentation that groups individuals based on their personality types. Models are built using surveys from a representative YouGov panel. The results are overlaid onto Orchard™ to give coverage of the UK

How It Works

Marketing with personality

0 50 100 150 200

Meercat

Swan

Lion

Penguin

Cat

Elephant

Dolphin

Jaguar

Beaver

Bear

Dog

Panda

Owl

Squirrel

Fox

Chimp

Index

Pe

rso

nar

Typ

e

Personar™ group

Example of using Personar

For further details call +44 (0) 2380 227117

All processing has been performed in line with our standard terms & conditions

Simon Lawrence : simonl@uncommon-knowledge.net

www.uncommon.agencyUncommon Knowledge Global Marketing