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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