Ten tips for driving engagement, retention and income using your data from Purple Vision.

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

3. Contents
What
How
So
• Strategy
• Silos
• People
• DP
• Segmentation
• Tools
• Integrate
• Analyse
• Journeys
• Evangelism
3

4. 1. Know what you’ve got
•
•
•
•
What data?
Who decides collection/coding?
Who knows it?
Org retains info?
Data must be driven by strategy
4

5. The classic donor pyramid
Fundraising 101.
But *completely useless* if you don’t know what the data
says…
5

6. IoF National Convention 2013
6

7. 2. Avoid keeping data in silos
7

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

12. The key to success is…
12

13. 5. Recognise diversity in data
One size doesn’t fit all

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

16. Classic segmentation - RFV
Recency
Frequency
Value
16

17. Creating segments
Recency
Frequency
Value
17

18. Creating segments
4
3
2
Recency
8
1
9
6
7
Frequency
Value
18

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

21. 21

22. 7. Integrate

23. 7. Integrate
23

24. Email & social media integration
• Add-ins – eg. Outlook
• CRM integration “bridges”
• Benefits
•
•
•
•
Track friends and followers
Major donors
Advocates and viral “buzz”
Measure level of influence
24

25. IoF National Convention 2013
25

26. 26

27. Systems integration
27

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
30

31. Look alike logic
Universe
Non-profit
supporters
Your Database
Your Sector
31

32. Profile variables
•
•
•
•
•
•
Income
Housing Tenure
Spending Power
Education
Occupation
Social Grade
•
•
•
•
•
•
Age
Children
Household Size
Property Type
Urbanicity
Retail Accessibility
32

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

34. Profile Model – closeness of fit
Assembli Model
Customers
Counts
Base
%
Counts
Penetration
%
Z-Score
Index
%
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
34

35. Segment geography
Closest fit
Furthest fit
BUT use past data analysis/organisational knowledge to
inform strategy too!
35

36. 9. So – Supporter Journeys
Volunteered
Became
committed
giver
Joined
membership
First Gift
Volunteered
Legacy
Pledge
Became
committed
giver
36

37. First engagement = purchase
37

38. First engagement = committed gift
38

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

43. Recap…
What
How
So
• Strategy
• Silos
• People
• DP
• Segmentation
• Tools
• Integrate
• Analyse
• Journeys
• Evangelism
43

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

47. Questions?
steve.thomas@purple-vision.com
dawnvarley@league.org.uk
Find us on LinkedIn
Follow us on twitter
@nfpdawnv
@stevethomas393
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