Data Enhancement Panel. 1. What do we mean by data enhancement? Making more of your data –...
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Transcript of Data Enhancement Panel. 1. What do we mean by data enhancement? Making more of your data –...
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
•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.