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

Twitter

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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Thank YouJane FrameDirector of Fundraising & Marketingjaneframe@sfh.org.uk

Dawn VarleyConsultantdawn.varley@purple-vision.com

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