This document discusses data analysis and segmentation of Saint Francis Hospice's supporter database. It provides information on the hospice's current supporter base and identifies opportunities to better segment and profile supporters to enhance relationships, grow the supporter base, and make marketing more effective. Key data points like location, length of support, donation amounts, and demographics are suggested to better understand supporter types and needs.

1. Maximising Data
A hospice success story
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2. Saint Francis Hospice
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3. About Saint Francis Hospice
• Catchment of 750,000 people
• 25,000 active supporters
• 20,000 inactive supporters (lapsed 2 - 6 yrs)
• 20,000 comatosed (not given last 6 years +)
• Hospice needs £7.7 million
• £10 per person (catchment)
• CRUK £6 per person (catchment)
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4. Objectives for the Project
• Enhance existing relationships
• Grow supporter base
• More effective marketing
• ThankQ
• Skills and experience and time were
limited – Purple Vision
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5. Drivers for the Project
• Awareness levels patchy over such a large
catchment area
• Three childrens’ hospices who compete
heavily
• Cold recruitment difficult/costly particularly in
low awareness areas
• Cause creates a natural source of new
supporters – but does it encourage them to
keep on giving?
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6. Other influencing drivers
• Tried multiple approaches to engage
audiences – raffles, sponsor a nurse, light up a
life, general appeal- bit hit and miss.
• Lots of supporters making one off donations.
• Nature of cause – doesn’t assist with the need
for continued support?
• More than 70% of our IMO givers don’t give
again – need a reason to repeat.
• At least 20% of people affected are under 40!
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7. Pulling together data
• Source – where have they come from?
• Length of time on database – when did they first
interact with the hospice?
• How do they support – what type of giving – IMO,
regular, value?
• What have they been asked to do?
• What are the motivations for giving?
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8. Supporter Segmentation?
• Different types of supporters
• Where are they now in their life cycle?
• Where do we want to take them?
• How old are they really?
• How loyal are they really?
• Is the hospice a mainstream charity for them?
• 4000 people cared for – does that mean at
least 4000 new supporters on the database
each year?
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9. Supporter Segmentation
• Types of information to • Social media –
collect to enable better Facebook, Twitter.
segmentation: • Frequency of use
• How would you like to • Know about hospice
receive our newsletter/ facebook page?
• Frequency • Cards and Gifts?
• Format • Events –
• Raffles – like to receive? Challenge/Corporate
• Light up a Life – take (Ball, Golf )
part?
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10. Assembli Supporter Profile
Analysis
• Penetration assessment by Post Code
• Socio demographic profile by Post Code
compared to region
• Mapping of
– Penetration
– Concentrations of matched potential supporters
• Assessment of potential supporter base
• Implications for future strategy

11. Profile Model – closeness of fit
Segment 4 (71<Tenure) AND (54<Age) AND (60<Urbanicity<=65)
Segment 16 (85<Tenure) AND (54<Age) AND (65<Urbanicity<=83)
Segment 7 (71<Tenure<=85) AND (54<Age) AND (65<Urbanicity<=83)
Segment 10 (71<Tenure) AND (Age<=54) AND (72<Property) AND (60<Urbanicity<=83)
Segment 8 (40<Tenure<=71) AND (56<Age) AND (62<Urbanicity<=83)
Segment 3 (71<Tenure) AND (Age<=54) AND (Property<=72) AND (60<Urbanicity<=83)
Segment 15 (32<Tenure<=71) AND (45<Spend) AND (Age<=56) AND (60<Urbanicity<=88)
Segment 9 (40<Tenure<=71) AND (Education<=46) AND (56<Age) AND (83<Urbanicity)
Segment 11 (71<Tenure) AND (63<Age) AND (83<Urbanicity)
Segment 20 (11<Income) AND (Tenure<=40) AND (56<Age) AND (Children<=50)
Segment 18 (71<Tenure) AND (82<Spend) AND (Urbanicity<=60)
Segment 14 (32<Tenure<=71) AND (Spend<=45) AND (Age<=56) AND (60<Urbanicity<=88)
Segment 19 (40<Tenure<=71) AND (46<Education) AND (56<Age) AND (83<Urbanicity)
Segment 6 (40<Tenure<=71) AND (56<Age) AND (Urbanicity<=62)
Segment 22 (Tenure<=32) AND (25<Spend) AND (Age<=56) AND (60<Urbanicity<=88)
Segment 17 (Tenure<=40) AND (Education<=29) AND (56<Age) AND (50<Children)
Segment 5 (Income<=11) AND (Tenure<=40) AND (56<Age) AND (Children<=50)
Segment 2 (71<Tenure) AND (Age<=63) AND (83<Urbanicity)
Segment 0 (Tenure<=71) AND (Age<=56) AND (Urbanicity<=60) AND (Retail<=43)
Segment 1 (71<Tenure) AND (Spend<=82) AND (Urbanicity<=60)
Segment 24 (Tenure<=40) AND (29<Education) AND (56<Age) AND (50<Children)
Segment 13 (Tenure<=32) AND (Spend<=25) AND (Age<=56) AND (60<Urbanicity<=88)
Segment 23 (Tenure<=71) AND (38<Age<=56) AND (88<Urbanicity)
Segment 12 (Tenure<=71) AND (Education<=36) AND (Age<=38) AND (88<Urbanicity<=90)
Segment 28 (Tenure<=71) AND (36<Education) AND (Age<=38) AND (88<Urbanicity<=90)
Segment 27 (Tenure<=71) AND (38<Spend) AND (Age<=56) AND (Urbanicity<=60) AND (43<Retail)
Segment 26 (Tenure<=71) AND (39<Occupation) AND (Age<=38) AND (90<Urbanicity)
Segment 21 (Tenure<=71) AND (Spend<=38) AND (Age<=56) AND (Urbanicity<=60) AND (43<Retail)
Segment 25 (Tenure<=71) AND (Occupation<=39) AND (Age<=38) AND (90<Urbanicity)

12. Profile Model – closeness of fit
Customers Base Penetration Z-Score Index
Assembli Model
Counts % Counts % % 0 100 200
Segme
nts
Segment 4 1582 11.7 10311 3.0 15.3 9 396 ██████████ >200
Segment 16 1206 8.9 10017 2.9 12.0 7 311 ██████████ >200
Segment 7 980 7.2 10008 2.9 9.8 6 253 ██████████ >200
Segment 10 958 7.1 10183 2.9 9.4 6 243 ██████████ >200
Segment 8 1418 10.5 16860 4.8 8.4 6 217 ██████████ >200
Segment 3 950 7.0 15953 4.6 6.0 3 154 █████
Segment 15 661 4.9 12749 3.7 5.2 2 134 ███
Segment 9 540 4.0 10787 3.1 5.0 2 129 ███
Segment 11 534 4.0 10760 3.1 5.0 2 128 ███
Segment 20 565 4.2 14191 4.1 4.0 0 103
Segment 18 377 2.8 10391 3.0 3.6 0 94 █
Segment 14 497 3.7 15365 4.4 3.2 -1 84 ██
Segment 19 385 2.8 12085 3.5 3.2 -1 82 ██
Segment 6 404 3.0 13376 3.8 3.0 -2 78 ██
Segment 22 267 2.0 10391 3.0 2.6 -2 66 ███
Segment 17 232 1.7 10003 2.9 2.3 -3 60 ████
Segment 5 228 1.7 10115 2.9 2.3 -3 58 ████
Segment 2 352 2.6 17560 5.0 2.0 -5 52 █████
Segment 0 215 1.6 12063 3.5 1.8 -5 46 █████
Segment 1 158 1.2 10053 2.9 1.6 -5 41 ██████
Segment 24 159 1.2 10856 3.1 1.5 -6 38 ██████
Segment 13 152 1.1 10429 3.0 1.5 -6 38 ██████
Segment 23 253 1.9 17591 5.0 1.4 -8 37 ██████
Segment 12 120 0.9 10061 2.9 1.2 -7 31 ███████
Segment 28 82 0.6 10053 2.9 0.8 -10 21 ████████
Segment 27 74 0.5 10014 2.9 0.7 -11 19 ████████
Segment 26 74 0.5 10316 3.0 0.7 -11 19 ████████
Segment 21 48 0.4 12971 3.7 0.4 -19 10 █████████
Segment 25 47 0.3 13458 3.9 0.3 -21 9 █████████
- - - 0 0
Total 13518 348,970 3.87
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13. Segment geography
Closest fit Furthest fit
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14. Observations @SFH
• Lots of supporters - lots of one off donations!
• Supporters being silo’d – Once an IMO giver,
always an IMO giver?
• Recruiting cold – becoming more costly
• Need to keep the ones we have and keep their
interest – 80/20 rule – need to maximise the
20%!
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15. The Approach
The proposal recommended three
distinct phases:
Phase 1 – Profile the data & understand the current
supporter base
Phase 2 – The Supporter Journey – reviewing the ‘As
Is’ and understanding the aspiration for the ‘To Be’
Phase 3 – The reality of the Journey – turning the
theory into practice via database and processes
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16. Phase 2 - Step 1 – ‘As Is’
Determined this via
• Workshops with teams to map ‘As is’ processes –
Events; Community; IMO; Individual Giving; LUAL
• As part of this ‘known issues’ and ‘would like to
have’ points came out
• Documented & back to teams for review
• Process allowed opportunity to get ‘buy in’ from
all teams
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17. 17

18. Step 2 – ‘To be’
Once ‘lie of land’ known, issues aired, and ‘blue
sky thinking’ begun, investigated what was
wanted via workshop to:
• Identify all audience types going forward (not
same as past)
• Identify all products to be taken forward (not
same as past)
• Discuss, refine and model into ideal journeys
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19. The classic donor pyramid
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20. A key to success is…
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21. Three things…
1. I want
2. I give
3. I get
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22. Individual Giver Thank You
Product
(& GA) Thank You
Part of
Saint
Francis
Product Other Hospice
(& GA) ways to Family
support,
Appeals eg events
Sponsor a
Thank You Nurse (etc Legacy
Letter RG) Pledger
Raffle/
Lottery Super-
Supporter
Welcome
Active Pack
interest, Regular Gift
Supporter I want…to
eg web, keep helping
request I want…to do
Passive Repeat once I’m gone
info more than
Interest Supporter give money
eg leaflet
I want…to
Supporter make this an
Joe and on-going
Joanne I want…to thing
Public give when I
Consumer can and
I want…to maybe get
help something
back
Enquirer
I want…to Newsletter and e-newsletter (with targeted content)
know about
‘my local
I want to…know hospice’
what this is all 22
about

23. Sponsorer
Thank You
Legacy &
Eventer Distinct
Event
types, eg
Challenges
(opt-in)
Thank You
Lifetime
Giving
Engaged
Event Happens
Distinct Evening supporter
Event with Saint
Event Info
types, eg Francis
Online Treks Hospice
Regular
Welcome
Giving eg
Pack & Website SAN,
Reg. Facebook Payroll
Just
Giving Event Event Addict & Lifetime
Welcome Support Giving
Ambassador
Pack
Event
Pack Event Hero
Website I want…to do
Facebook this again,
Twitter this was fun
Info Pack/ Event
Leaflet/ Participant and makes a
I
website/ difference
want…people
mag etc
Event to recognise
Thrill- Enroller what I’ve
seeker/go- done
getter I want…to do
/activist Event this and do
Enquirer this well!
I want…to
know the
Event Info details
Enquirer
I want…to
Newsletter and e-newsletter (with targeted content)
know more
about what I
can do *It is acknowledged that the motivation for a potential
I want…to do eventer isn’t always to support Saint Francis Hospice – it
something* can simply be to do the event offered, via Charity of the
Year, etc

24. Where next?
• Partnership working to find an automated approach
to donor journey administration and management
via the database.
• Review thanking process
• Phone thank – easy to gather more intelligence
• Follow – up process (determine what information is
the most appropriate to send)
• Lapsing – monitor monthly and ask why? Record this
information.
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25. Individual Giver - Development
Joe and
Joanne Enquirer Passive
Public Interest
Sent within x
eg leaflet
1. Code all days
response Info Pack/
devices, Leaflet
Active record all
interest, interactions
Consumer eg web,
request
info
Welcome Sent within x
Newsletter and e-newsletter (with targeted content)
Pack weeks
2a. Record Gift
Supporter 2b. Record Welcome
Pack response & tailor &
target comms
accordingly
Thank You
3. Record Sent within x
gifts/response & weeks
Repeat use to derive next 2nd Appeal
Supporter prompt. If no gift
in x months offer If Lottery have
Lottery? delayed
upgrade/
conversion plan
Lottery

26. Early Days – Outcomes so far
• IMO 50% give once – value £200k per annum
• 50% made a repeat donation £100k
• 70 IMO givers moved to tribute fund
• 754 Raffle purchasers now signed up to new lottery =
£40,354
• 754 Raffle players were giving £7540
• Variance £32,814
• 1000 cash donors now signed up to Sponsor a Nurse =
£60,000 a year
• 1000 cash donors were giving £8000
• Variance £52,000
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27. Outcomes continued
• Tribute Funds 0 to 70 = £80k in first year – all
moved from IMO giving.
“One way or another, we’ve
raised over £10,000 for the
Derek Bundy Tribute Fund and
I know Derek would be proud
of us. Like so many families
who have got to know Saint
Francis Hospice, we wanted to
give something back.”
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28. Going forward
• Donor Development Manager in budget to work across all
fundraising streams looking at cross fertilisation of
information and giving opportunities.
• Plans to move towards more sophisticated journeys for
every income streams and every type of supporter
• Really still in the early stages of donor journey
development as so much more to do.
• Benefits – we know more about our supporters and they
know more about us
• Lessons learned – it takes time and resources. We will be
spending more time keeping the supporters we have.
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29. Any Questions?
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30. Thank You
Jane Frame
Director of Fundraising & Marketing
janeframe@sfh.org.uk
Dawn Varley
Consultant
dawn.varley@purple-vision.com
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