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Maximising DataA hospice success story
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Saint Francis Hospice
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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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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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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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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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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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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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Supporter Segmentation
• Types of information to collect to enable better segmentation:
• How would you like to receive our newsletter/
• Frequency• Format• Raffles – like to receive?• Light up a Life – take
part?
• Social media – Facebook, Twitter.
• Frequency of use• Know about hospice
facebook page?• Cards and Gifts?• Events –
Challenge/Corporate (Ball, Golf )
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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
Profile Model – closeness of fitSegment 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)
Profile Model – closeness of fitAssembli Model Customers Base Penetration Z-Score Index
Counts % Counts % % 0 100 200
Segments
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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Closest fit
Segment geography
Furthest fit
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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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The Approach
The proposal recommended three distinct phases:Phase 1 – Profile the data & understand the current supporter basePhase 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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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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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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The classic donor pyramid
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A key to success is…
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Three things…
1. I want2. I give3. I get
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Individual Giver
Consumer
Enquirer
Supporter
Repeat Supporter
Regular Gift Supporter
Super-Supporter
Legacy Pledger
Part of Saint
Francis Hospice Family
Joe and Joanne Public
Active interest, eg web, request
info
Welcome Pack
Thank You Letter
Raffle/ Lottery
Appeals
Thank You Product (& GA)
Passive Interest
eg leaflet
Thank You Product (& GA)
Other ways to support, eg events
I want to…know what this is all
about
I want…to know about
‘my local hospice’
I want…to help
I want…to give when I
can and maybe get something
back
I want…to make this an
on-going thing
I want…to do more than give money
I want…to keep helping
once I’m gone
Newsletter and e-newsletter (with targeted content)
Sponsor a Nurse (etc
RG)
Co
mm
un
it
y
Ev
en
ts
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Eventer
Event Enquirer
Event Info Enquirer
Event Enroller
Event Participant
Event Hero
Event Addict & Ambassador
Engaged supporter with Saint
Francis Hospice
Thrill-seeker/go-
getter /activist
Website Facebook
Welcome Pack
Event Pack
Event Support
Info Pack/ Leaflet/
website/ mag etc
Event Info
I want…to do something*
I want…to know more
about what I can do
I want…to know the
details
I want…to do this and do this well!
I want…people to recognise what I’ve
done
I want…to do this again,
this was fun and makes a
difference
Newsletter and e-newsletter (with targeted content)
Regular Giving eg
SAN, Payroll
Co
mm
un
it
y
Even
t H
appe
ns
In
d G
iv
er
*It is acknowledged that the motivation for a potential eventer isn’t always to support Saint Francis Hospice – it can simply be to do the event offered, via Charity of the Year, etc
WebsiteFacebook
Just Giving
Distinct Event
types, eg Treks
Thank You Evening
Online Welcome
Pack & Reg.
Distinct Event
types, eg Challenges
Lifetime Giving
Legacy & Lifetime Giving
Sponsorer Thank You
(opt-in)
I n d G i v e r
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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Individual Giver - Development
Consumer
Enquirer
Supporter
Joe and Joanne Public
Active interest, eg web, request
info
Info Pack/ Leaflet
Passive Interest
eg leaflet 1. Code all response devices,
record all interactions
2a. Record Gift2b. Record Welcome
Pack response & tailor & target comms
accordingly
Newsletter and e-newsletter (with targeted content)
3. Record gifts/response &
use to derive next prompt. If no gift in
x months offer Lottery?
Lottery
2nd Appeal
Sent within x days
Sent within x weeks
Sent within x weeks
Repeat Supporter
Thank You
If Lottery have delayed upgrade/ conversion plan
Welcome Pack
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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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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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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Any Questions?
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