The document provides 12 tips for making the most of a database, including understanding what data is currently known and unknown, tracking supporter journeys, cleansing data, taking a single view of supporters, segmentation, integrating email marketing, social media integration, reporting, improving business processes, data mining, engagement tracking, and profiling supporters. It discusses each tip in more detail and provides examples and recommendations for non-profit organizations to better utilize their supporter databases.

1. Making the most of your
database
Paul Jackson
Steve Thomas
25th April 2012

3. About Purple Vision
• independent consultants established 2003
• charities, associations, schools and universities
• services include:
– fundraising consultancy and research
– data analytics
– Appeal and campaign planning
– business process improvement
– project management and training
• ASI, Blackbaud, Salesforce
• fundraising | technology | change

4. About Purple Vision

5. What’s your Database?

6. “From here on in I want to really ‘get to
know’ my data. Who is on there? Why are
they on there? What do I know? What do
I need to know? How can I target certain
groups?”

7. 12 Top Tips
1. What you don’t know 1. Social Integration
2. Supporter journeys 2. Reporting
3. Data cleansing 3. Business Processes
4. Single supporter view 4. Data Mining
5. Segmentation 5. Engagement
6. Email integration 6. Profiling

8. You don’t know what you don’t know!
• Contact details • Products purchased
• Employ & other bio • Services accessed
• Donations made • Areas of interest
• Type
• Date • Mailing preferences
• Value
• Pay method • Comms in & out
• GAD
• Membership
• Event attendance
• Demographics
• Volunteer details
• Online interaction
• Prospect info
What & Who should you ask to find out?

9. Supporter journeys
Volunteered
Became
committed
giver
Joined
First Gift membership Legacy
Pledge
Volunteered
Became
committed
giver

10. First engagement: purchase

11. Database Cleansing
• When was this last done?
• Why do it?
• Check list of options:-
• Deduplication of contacts
• Suppressions
• Address correction (nb Postcode Anywhere)
• NCOA
• Email checking
• Is there ‘stuff’ that’s never used?
• What about ‘Old’ information?

12. From silos to one view
• All charities do it!
An existing client...
Originally Now
3 contact files 11 files

13. From silos to one view
• Main reason for your own files?
• How to control your contacts
• Data ‘amnesty’...and the benefits
• Consider a database champion
(in Fundraising)

14. One size doesn’t fit all

15. What is Segmentation?
Classification of the
population into
subgroups that are:
•Distinguishable
•Identifiable
•Manageable
•Fit for purpose

16. How segmentation works
Recency
Engagement Value
Rate

17. Creating segments
Recency
Engagement Value
Rate

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

19. So what?
Targeting:
• Make targeting more appropriate to audience
• Avoid scattergun communications
• Protect against unsubscribes and lapsing
• Makes internal expectations realistic

20. Email integration
• Easy to record emails
in most systems
• Aids ‘360o view’ for
contacts
• What about email campaigns?
• Raisers Edge: “chimpegration”
• Cloud systems tightly integrated
• Benefits
• Easily record campaign against contacts
• Update preferences, unsubscribes & bounces
• measure level of engagement

22. Social media integration
• Add-in – eg. Outlook

23. Social media integration
• Add-in – eg. Outlook
• CRM integration “bridges”
• Monitor online activity
• Benefits
• Track friends and followers
• Major donors
• Advocates and viral “buzz”
• measure level of infleunce

24. Social media integration
• Add-in – eg. Outlook
• CRM integration “bridges”
• Monitor online activity
• Benefits
• Track friends and followers
• Major donors
• Advocates and viral “buzz”
• measure level of infleunce

25. Social media integration

26. Reporting
• Reporting with today’s systems should be
easy:-

27. Reporting
“a report, of % of bookings in a year that are made
by an organisation that also booked in the previous
year AND the % of bookings by an organisation
that have made another booking within a two year
period. Ability to specify start and end dates and
look at summary or details”.

28. Reporting
• If it isn’t easy – Why?
• Consider using a reporting tool...or a
consultant!
• What reports do you need
– Segmenting/targetting/campaigns
– Performance: financial, KPIs
• What reports do you need?
What 5 reports would you find most useful?

29. Business Process Improvement
Enquirer Passive
Interest eg
Sent within x
leaflet
1. Code all days
response Info
devices, Pack/
Active record all Leaflet
interest, eg interactions
Consumer
web,
request info
Welcome Sent within x
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
2nd
Repeat use to derive next
Appeal
Supporter prompt. If no gift
in x months offer If Lottery have
Lottery? delayed
upgrade/
conversion plan
Lottery

30. Data mining
• “...the purpose of data mining is to discover
hidden patterns in large amounts of data in
order to use these for data analysis and
forecasting”.
• Beers and Nappies!
• In our world..
• RFM
• How long Data Probability that
• Membership supporter will
• Engagement Mining stop giving
• Events
.
.
• Can I use it?.........Excel

31. Engagement
7119
2790 super close
5311 7 on holiday
2525 on sabbatical Segment 7
7
Segment 6
2295 keen but stuck
Segment 5
4183 activists
Segment 4
6671 first biters
Segment 3
9457 Potentials
Segment 2
Zeros Segment 1
Segment 0

32. Engagement - understanding 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
6 79.48 4.62
5 0.01 87.23 7.16 4.62
4 96.75 3.57 2.27
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
0 1 2 3 4 5 6 7

33. Engagement – moves and blocks
8845
3511 super close
3301 7 on holiday
3976 on sabbatical Segment 7
7
Segment 6
3111 keen but stuck
Segment 5
2213 activists
Segment 4
6649 first biters
Segment 3
7250 Potentials
Segment 2
Zeros Segment 1
Segment 0

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

37. Example profile - Age
Supporters Regional Base Penetration Index
TotalSketch Attributes
Counts % Counts % % 0 100 200
Age – Example 1
Rank 91-100 (High) 933 16.8% 23092 11.2% 4.04 150 █████
Rank 81-90 1012 18.2% 19816 9.6% 5.11 190 █████████
Rank 71-80 852 15.3% 20846 10.1% 4.09 152 █████
Rank 61-70 697 12.5% 20417 9.9% 3.41 127 ███
Rank 51-60 643 11.6% 23081 11.2% 2.79 104
Rank 41-50 459 8.3% 22491 10.9% 2.04 76 ██
Rank 31-40 316 5.7% 22152 10.7% 1.43 53 █████
Rank 21-30 202 3.6% 17995 8.7% 1.12 42 ██████
Rank 11-20 201 3.6% 19192 9.3% 1.05 39 ██████
Rank 1-10 (Low) 245 4.4% 17650 8.5% 1.39 52 █████
TOTAL 5560 206732 2.69
Age – Example 2
Rank 91-100 (High) 601 14.5% 23382 11.1% 2.57 130 ███
Rank 81-90 662 15.9% 21810 10.4% 3.04 154 █████
Rank 71-80 465 11.2% 18343 8.7% 2.54 128 ███
Rank 61-70 557 13.4% 23014 10.9% 2.42 123 ██
Rank 51-60 493 11.9% 22896 10.9% 2.15 109 █
Rank 41-50 375 9.0% 20015 9.5% 1.87 95 █
Rank 31-40 387 9.3% 22721 10.8% 1.70 86 █
Rank 21-30 270 6.5% 22811 10.8% 1.18 60 ████
Rank 11-20 171 4.1% 17574 8.4% 0.97 49 █████
Rank 1-10 (Low) 174 4.2% 17887 8.5% 0.97 49 █████
TOTAL 4155 210453 1.97
Age – Example 3
Rank 91-100 (High) 20 2.3% 10642 8.7% 0.19 27 ███████
Rank 81-90 14 1.6% 11145 9.1% 0.13 18 ████████
Rank 71-80 37 4.3% 10021 8.2% 0.37 53 █████
Rank 61-70 133 15.6% 12234 10.0% 1.09 156 ██████
Rank 51-60 144 16.9% 12409 10.1% 1.16 167 ███████
Rank 41-50 124 14.5% 11515 9.4% 1.08 155 █████
Rank 31-40 139 16.3% 14290 11.6% 0.97 140 ████
Rank 21-30 94 11.0% 14826 12.1% 0.63 91 █
Rank 11-20 69 8.1% 12608 10.3% 0.55 79 ██
Rank 1-10 (Low) 80 9.4% 13232 10.8% 0.60 87 █
TOTAL 854 122922 Sample

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

39. Where are they?
New areas may have
a different socio-dem.
profile to the existing
donorbase
Different motivations
require different
communication
strategies
Missing all the towns!

40. Summary - 12 Top Tips
1. What you don’t know 1. Social Integration
2. Supporter journeys 2. Reporting
3. Data cleansing 3. Business Processes
4. Single supporter view 4. Data Mining
5. Segmentation 5. Engagement
6. Email integration 6. Profiling

41. Any questions?
0845 458 0250
info@purple-vision.com
www.purple-vision.com
@purple_vision