I of F South West Spring Conference 2012
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Making the most of your database Paul Jackson Steve Thomas 25 th April 2012
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Paul Jackson and Steve Thomas discuss how to make the most of your database.
Transcript of I of F South West Spring Conference 2012
Making the most of your database
Paul Jackson
Steve Thomas
25th April 2012
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
About Purple Vision
What’s your Database?
“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?”
12 Top Tips
1. What you don’t know
2. Supporter journeys
3. Data cleansing
4. Single supporter view
5. Segmentation
6. Email integration
1. Social Integration
2. Reporting
3. Business Processes
4. Data Mining
5. Engagement
6. Profiling
You don’t know what you don’t know!• Contact details• Employ & other bio• Donations made
• Type• Date• Value• Pay method• GAD
• Event attendance• Volunteer details• Prospect info
• Products purchased• Services accessed• Areas of interest• Mailing preferences• Comms in & out• Membership• Demographics• Online interaction
What & Who should you ask to find out?
Supporter journeys
First Gift
Became committed
giver
Joined membership
Became committed
giver
Volunteered
Volunteered
Legacy Pledge
First engagement: purchase
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?
From silos to one view• All charities do it!
An existing client...
Originally Now3 contact files 11 files
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)
One size doesn’t fit all
What is Segmentation?
Classification of the population into subgroups that are:
•Distinguishable•Identifiable•Manageable•Fit for purpose
How segmentation works
EngagementRate
Recency
Value
Creating segments
EngagementRate
Recency
Value
9
Creating segments
8
4
13
67
2
EngagementRate
Recency
Value
So what?
Targeting:• Make targeting more appropriate to audience• Avoid scattergun communications• Protect against unsubscribes and lapsing• Makes internal expectations realistic
Email integration• Easy to record emails
in most systems
• Aids ‘360o view’ forcontacts
• 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
Social media integration
• Add-in – eg. Outlook
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
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
Social media integration
Reporting
• Reporting with today’s systems should be easy:-
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”.
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?
Business Process Improvement
Consumer
Enquirer
Supporter
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
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
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
Zeros Segment 1Segment 2
Potentials
Engagement
first biters Segment 3
activists Segment 4
keen but stuckSegment 5
7 on sabbaticalSegment 6
7 on holidaySegment 7
super close
66714183
2295
25255311
27907119
9457
Segment 0
Engagement - understanding shifting
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
Probabilities of being present in each segment next month depending on presence this month
Zeros Segment 1Segment 2
Potentials
Engagement – moves and blocks
first biters Segment 3
activists Segment 4
keen but stuckSegment 5
7 on sabbaticalSegment 6
7 on holidaySegment 7
super close
66492213
3111
39763301
35118845
7250
Segment 0
Look alike logicUniverse
Your Database
Your Sector
Non-profit supporters
Example profile - AgeTotalSketch Attributes Supporters Regional Base Penetration Index
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
Profile variables
• Income• Housing Tenure• Spending Power• Education• Occupation• Social Grade
• Age• Children• Household Size• Property Type• Urbanicity• Retail Accessibility
New areas may have a different socio-dem. profile to the existing donorbase
Different motivations require different communication strategies
Missing all the towns!
Where are they?
Summary - 12 Top Tips
1. What you don’t know
2. Supporter journeys
3. Data cleansing
4. Single supporter view
5. Segmentation
6. Email integration
1. Social Integration
2. Reporting
3. Business Processes
4. Data Mining
5. Engagement
6. Profiling
