Data Enhancement Panel

1. What do we mean by data enhancement?

• Making more of your data– Cleaning deduping– Contact data e.g. email, suppressions

• Widening your own dataset– Using your own data first– Adding in external variables

Fundraising Area – F2F, Legacy, RG, Trading

2. External data: why buy it?• Where in house data is

limited

• Supporter understanding – Profile segments– Targeting – Developing and informing

creative content/offers etc

• Targeting– Up sell, cross sell, lapse

model development– Static selections

• Extending available contact channels

• Segmentation mapping

CACI’sInsight Kaleidoscope

Consumers.Locations.

Communities.

Individual

Postcode

AffluenceAffluence

DigitalDigital

Current Demographics

WorkforceACORN

Retail Cat chments

Public Transport Access Levels (PTAL)

Retail Spend Estimates

Online Spend Estimates

Census Demographics

CO2 Emissions

Retail Outlets

Job Seekers Allowance

British Crime Survey

FRS: GFKNoP’s Financial Research Survey

BHPS: British Household Panel Survey

Scottish/IrishACORN

Environ mental behaviour

& attitudin al survey

Online Panel

CACI’sInsight Kaleidoscope

Consumers.Locations.

Communities.

Individual

Postcode

AffluenceAffluence

DigitalDigital

Current Demographics

WorkforceACORN

Retail Cat chments

Public Transport Access Levels (PTAL)

Retail Spend Estimates

Online Spend Estimates

Census Demographics

CO2 Emissions

Retail Outlets

Job Seekers Allowance

British Crime Survey

FRS: GFKNoP’s Financial Research Survey

BHPS: British Household Panel Survey

Scottish/IrishACORN

Environ mental behaviour

& attitudin al survey

Online Panel

What’s available?

What’s available?

• Individual, household & postcode level• Real & modelled

Demographic & lifestyle

•Date of birth

•Income

•Household composition

•Interests

Contact

•Infill information

•Email

•Phone (Landline or mobile)

Behavioural & transactional

•Online browsing

•Buying behaviour

•Donation value

•Subscriptions

Locational

•Distance to nearest store or town

•TV or radio region

Property value & tenure

•Owner occupier/renter

•Owned outright

•Equity

Age Household composition

Charitable donations

Property tenure

Length of residence

Preferred channel

£££

Useful variables

Newspaper readership

3. Application

Segmentation and Profiling Mosaic Name Mosaic Description % Supporters %

UK Index vs. UK

Rural Solitude Residents of isolated rural communities 4.9% 4.5% 110 Small Town Diversity Residents of small and mid-sized towns with strong local roots 9.1% 9.1% 99

Alpha Territory Wealthy people living in the most sought after neighbourhoods 5.2% 3.6% 144

Professional Rewards Successful professionals living in suburban or semi-rural homes 11.6% 9.0% 129

Suburban Mindsets Middle income families living in moderate suburban semis 13.3% 12.0% 111 Careers & Kids Couples with young children in comfortable modern housing 7.7% 5.5% 140

Liberal Opinions Young, well-educated city dwellers 10.1% 8.9% 113 New Homemakers Couples and young singles in small modern starter homes 4.7% 4.3% 110

Terraced Melting Pot Lower income workers in urban terraces in often diverse areas 5.8% 7.3% 79 Industrial Heritage Owner occupiers in older-style housing in ex-industrial areas 7.5% 7.9% 96

Ex-Council Community Residents with sufficient incomes in right-to-buy council houses 7.0% 9.4% 75

Active Retirement Active elderly people living in pleasant retirement locations 3.9% 4.0% 97 Elderly Needs Elderly people reliant on state support 3.2% 4.6% 70

Upper Floor Living Young people renting flats in high density social housing 3.0% 4.8% 63 Claimant Cultures Families in low-rise council housing with high levels of benefit need 3.1% 5.2% 59

100.0% 100.0% 100

Age Band

% Female UK Penetration

% Male UK Penetration

% Overall UK Penetration

0-14 3.9% 0.1% 2.0%

15-24 17.3% 1.5% 9.2%

25-34 29.0% 4.3% 16.8%

35-44 28.4% 6.0% 17.5%

45-59 24.4% 6.8% 15.9%

60-64 22.6% 8.3% 15.9%

65-74 16.2% 8.1% 12.7%

75+ 15.8% 13.0% 15.1%

23.7% 7.7% 16.4%

•Age is integral to profiling, targeting and also applying supporter segmentations

•Geodems also provide useful profiling for supporters and can be used to link online, market, non supporters and supporters

The Importance Of Postal Geography

Postcode

BS8 4RU1.6 million postcodes15 households in each

Postal Sector

BS8 49,000 sectors2,600 households in each

Postal District

BS8 2,700 districts8,600 households in each

Postal Area

BS120 areas194,000 households in each

Household

Mr & Mrs Fowler22 million households

Data now available at person and household level

Free data!

GOSH

• Liquid Assets (household)• Household income (household)• Lifestage (household)• Experian’s Mosaic (household)• Age (individual)• Location data http://data.gov.uk/dataset/os-code-point-open

Cash & Regular Giving

• External data - adds depth– Understand who your supporters are– Understand how they may behave– Determine next best action

• Predictive modelling– Past behaviour > geodems (usually)– External data most useful when little behaviour

• New recruits (no past to track)• Reactivation (No recent behaviour – are they still active

elsewhere?)

Cash & Regular Giving

• GOSH Experience• Appending internal survey data

– Motivations– Attitudes– Interests

• After behaviour Liquid assets is one of the biggest drivers

Events

• Locality to event– Use open code-point and the Pythagorean

theorem– Age – lifestage– Drive time

Legacy• TARGETTING

– Those who are warmest to you (longevity and activeness of support)

– Age• TIMING Identifying life changing -> Will rewrite

– Buy house– Have a family– Spouse death

• VALUE– Family composition– Value of assets

High Value

• High value profiling – “Action Planning” and “Factory” profiling

• Information on wealth, disposable income, director, individual or partner

• Combine with behaviour

Charity Shop Networks

• Create “Town Types” using Acorn • Different stock offerings for different Town

Types

Financial Products

• Insurance – Pet, Home, Motor, Travel• Funeral Plan• Credit cards• Equity release

Online & Social

• Email appending• Twitter handles• Facebook flag• Inmem & tribute • Event • Hitwise profiling

Other questions

What should you consider? What do you need

from your

supplier?How will you use it?

What codes do you

need?Which records must

be appended?

How has the data

been collected & how

long ago?

What is the aim?

What supporting

information is provided?

Cost

Can billing be

staggered?

How quickly will the

investment payback?

Could you club

together with another

charity?

What level of data is

practical?

What is the likely

match rate?

What does it cost?Cost variables

Volume

Type of data

Level (postcode/household)

Number of variables

Costs range from £3,350 to append postcode

level codes to 99,999 records or £58,275 for

appending 100’s of lifestyle variables to millions

of records

Data triggers

• Treadmill of campaign• Feedback of data…..• Collecting & using VPI (Volunteered Personal

Information)• Relevance of data to use…

Donor Lifecycle Analysis

1st Donation•ROI•Media Effectiveness•Campaign

Welcome• Value• Recency

Repeat Gift• Response• Recency• Frequency• Value• Complaint Regular Gift

• Frequency• Payment Method• ResponseCommitted Giving

• Frequency• Value• LTV High value donors

• Value Bands

Upgrades• Loyalty• Value• Uplift• Complaint

Legacy• Gender• Location• LTV• Demographics

Major Gifts• Value/LTV• External Research• Demographics

Lapsed Donors• Recency• Frequency• Value

Postcode-level geo-demographics eg Acorn

Acorn profiler

Profiler is Underlying data also

VERY useful, see below

Acorn data-set

Individual-level geo-dems e.g Ocean

Individual level data More ‘attitudinal’ Reflects the fact that all people

living in the same postcode will be

Different Full listing of variables here

DISCUSSION• What geodems to people use and do they find them effective?• What variables have people found effective for targeting models?• What suppliers have people used for HV prospecting and how much

success have they had in gennerating new high value prospects?• What other variables have people found useful to append to their data?• How have people used their own data/ collected data effectively?• What suppliers of data are good and how do you get the best deal?• What are the main challenges people find in completing their view of the

customer.