Pitch Presentation for b2.pptxsssssssssssssssssssssss
IoF CRM & the Donor Journey - top ten tips for driving fundraising with data
1. Top ten tips for driving
fundraising with your data
(aka) how I learned to love data
1
2. Who we are
Steve Thomas
Managing Director, Purple Vision
www.purple-vision.com
@stevethomas393 @purple_vision
Dawn Varley
Director of Marketing and Fundraising,
League Against Cruel Sports
www.league.org.uk
@nfpdawnv
2
8. Data map
CMS
Social media
monitor and
broadcast
Website
Donations SG
Donations RG
Data
import
layer
Online Advocacy
Bulk Email
Campaign emails
Petitions
MPs
API
Text donation
Volunteers
Donor database
Events |Trust & Statutory |
Stakeholders | Members
Survey
Off-line
capture
Data
house
Retail
Political
contacts
Comp
laints
Finance
Opera
tions
HR
Media
9. 3. Remember data is all about people
• Gifts don’t give themselves
• The best fundraising is relational, not
transactional (Fundraising 101…)
• To grow your fundraising you have to know
your data
• And there are other people too…
9
10. Data is about people, process and technology
(in that order)
10
11. 4. Keep it clean, and respectful
You’ve got data, but is it healthy? Data cleansing is
important – budget for it and do it. It can save you
money, add value, and keep supporters happy.
You can’t talk about data with the dreaded Data
Protection subject coming up…
Data protection is just about respect for your
supporters. How would you like to be treated?
11
14. What is Segmentation?
Classification of the
population into
subgroups that are:
•
•
•
•
Distinguishable
Identifiable
Manageable
Fit for purpose
14
15. Why segment?
•
•
•
•
Make targeting more appropriate to audience
Avoid scattergun communications
Protect against unsubscribes and lapsing
Makes internal expectations realistic
15
19. Bases for Segmentation
•
•
•
•
•
•
Supporter category
Reason for support
How old are they?
How loyal are they?
Where in life cycle?
Where do we want to
take them?
• Types of information to
collect to enable better
segmentation:
•
•
•
•
•
Comms preferences
Format/media type
Event attendance
Frequency of contact
Purchases
Choose data relevant to your strategy
19
20. 6. How to manage data
You now understand the data, and know how to
make the most of it. But what systems help you
do that?
• Data lives in systems, eg CRM, CMS, Excel etc
• Know your systems (‘System Architecture’)
• Build to future proof, and this is driven by…
Fundraising/Organisational strategy (101!)
If I had a £1…
20
28. Data map
CMS
Social media
monitor and
broadcast
Website
Donations SG
Donations RG
Data
import
layer
Online Advocacy
Bulk Email
Campaign emails
Petitions
MPs
API
Text donation
Volunteers
Donor database
Events |Trust & Statutory |
Stakeholders | Members
Survey
Off-line
capture
Data
house
Retail
Political
contacts
Comp
laints
Finance
Opera
tions
HR
Media
29. Data warehouse
Off-line
capture
Website
Data
house
Online
Fundraising
CMS
Forms, HR,
Volunteers
News, Forums
Supporter Portals,
Donor Journeys
Events & P2P
E-commerce
Opera
tions
Bulk Email
Bulk email
Segmentation
Newsletter Design
Retail
Media
Directory
Data Import
Layer
Finance
API
Comp
laints
Text donation
Donor Database
Volunteers
Political
contacts
HR
Events |Trust &
Statutory | Stakeholders
| Members
Social monitor
and Broadcast
Online Advocacy
Campaign emails
Petitions
MPs
API
Data Tools
Data Warehouse
Data Analytics
& Reporting
29
30. 8. So – what does this mean?
So, you have your data, you know what it
means, and you have it in the right place…
Now you need to make the data work for you
by:
• Profiling your data
• Learning from your data
• Using it to inform your strategy eg looky-like
acquistion, targeted messages, correct
channels
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33. 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)
33
39. Loyalty ladders
7119
super close
2790
5311 7 on holiday
Segment 7
7 on sabbatical
2525
Segment 6
keen but stuck
2295
Segment 5
4183 activists
Segment 4
6671
first biters Segment 3
Potentials
Segment 2
9457
Zeros
Segment 1
Segment 0
39
40. Segment shifting
Probabilities of being present in each segment next month depending on
presence this month
7
0.12
0.47
1.10
1.85
3.25
9.20
11.08
88.74
79.48
4.62
87.23
7.16
4.62
96.75
3.57
2.27
4
5
6
6
5
0.01
4
3
0.27
0.18
0.89
92.88
2
0.42
2.22
93.74
3.97
1
0.01
97.12
4.25
1.24
0
99.18
0.01
0.05
2
3
0
1
7
40
41. Insight – snakes and ladders
8845
3511
super close
3301
7 on holiday
Segment 7
3976 on sabbatical
7
Segment 6
3111 keen but stuck
Segment 5
2213
activists
Segment 4
6649
first biters
Segment 3
Potentials
7250
Segment 2
Zeros
Segment 1
Segment 0
41
42. 10. Be a data evangelist
Now you know the power of
data, and how it can transform
your fundraising.
You have been initiated into the
club, and you must
be a data …
- advocate
- believer
- defender
42
44. Top Ten Tips
1. Get to know your data. What do you have,
what do you need?
2. Avoid data silos. What brings it together?
3. Data is people. Do what you can to build
relationships, internal and external
4. Keep relationships clean & respectful. How
you apply data protection & cleansing is key.
5. Know when and how to segment
44
45. Top Ten Tips
6. Be aware how your data is managed
7. Discover how to bring it all together
8. So learn from your data – report, analyse,
question – and use it to inform decisions
9. So apply data insights to growing your
supporters’ relationships (and their giving)
10. So now live it! Go back to the office and be a
data evangelist.
45
46. Resources
Institute of Fundraising Groups:
• Insight SIG http://insightsig.org/
• Technology SIG http://www.ioftech.org.uk/
LinkedIn for networking and Groups, inc
• Purple Patch
• UK Fundraising
• Institute of Fundraising….and more!
Events
• Purple Vision Breakfast Briefings
• IoF Insight & IoF Tech conferences
46
Have taken out PV slides, so cover off at this stage re recap on orgs & what they do. Then ask Q – Who here gets excited about data? Who gets excited about supporters? Erm, same thing.
DV to do?
DV slide. Draw donor pyramid on flipchart and show how useless unless you how who’s where, and how to find them.
DV – in slides for ease of ref for download post even
DV – operating in silos within the team, dept, org…
ST slide
DV
Shoes, hat. Put yourself in their shoes. What hat are you wearing (B2C vs B2B)
DV
ST
ST
ST
ST
ST
DV – What has caused this? Natural supporters inclined to support, or activity in those areas??