In this session, we will review the ins and outs of data mining, including best practices for reviewing both internal and external sources. The goal is to provide a comprehensive overview of the information that fundraisers have residing in their constituent database systems while also reviewing external sources that can help in uncovering prospects as well. The session will continue with a skill lab that covers how the information should then be disseminated and managed for both major and annual giving. Lastly, we will cover key metrics that can help direct development staff to areas of success and those needing improvement.

1. T
YOUR DATA GOLD MINE
“IT’S CLOSER THAN YOU THINK”
Page Bullington, MPA
Target Analytics
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2. AGENDA
• Getting Rid of Silos
• Data Mining Defined
- A closer look at inside information
• External Sources
- Thinking outside the box
• Final Discussion and Questions
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3. GETTING RID OF SILOS
• Data does not belong here…
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4. WHEN SEGMENTING IS BAD…
• When it does not allow for institutional knowledge to be shared
• When it prevents consistent data entry
• When it only allows for one dimensional fundraising
• When it results in data being double, triple…entered
• When it becomes “ours” or “theirs” but not both
• When it makes your work harder
• When it prevents moves management
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5. CAN WE MOVE TO THIS?
Yes…through a
combination of focused
internal and external data
mining.
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6. D ATA M I N I N G D E F I N E D
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7. DEFINITIONS
• Data Mining
Investigating and discovering trends within a constituent
database using computer or manual search methods. Simple
trend analysis.
• Predictive (Statistical) Data Modeling
Discovery of underlying meaningful relationships and patterns
from historical and current information within a database; using
these findings to predict individual behavior
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8. WHY DATA MINE?
• Your data is one of your greatest assets
• The lens through which you view your donors
• Impacts how your donors view your organization
• Like other assets, requires maintenance
• Can be easily mismanaged
• Effects your ability to allocate scarce resources
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9. THINGS TO THINK ABOUT…
• Capacity and donor affinity are the keys to transformational giving
• Donor Affinity is the great unknown
• So, what “affinity” data is vital for you to track, code and report on?
• What data is hiding in your database that can be used today?
• What are best practices for querying and eventually analyzing this
data?
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10. DONOR DATA THAT MATTERS
• Data that goes beyond gift transactions
• Data offered up by the donor – have you been listening?
• Data that is hard coded – storing it all in the “notes” field doesn’t count
• Data that indicates loyalty or affinity for your mission over other
organizations
• Extraordinary behavior – hand-written notes, calls of praise, etc.
• Data that captures donor engagement
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11. THE DATA CHALLENGE
• Data is frequently messy or missing
• Incomplete data
• “We don’t have any place to enter those fields”
• “We’d never get the users to key it in”
• That information is managed by a different department and stored in
their database
• What’s important to one department may not be important to another
The challenge is using all of this data in a meaningful way…
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12. HIDDEN GEMS
•Constituency Codes
•Source of Gift
•Address Coding
•Event Participation
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13. CONSTITUENCY CODES
• Alumni •Ticket buyer - Performance or
• Degree Athletics
• Major •Event Participation
• Faculty •Online Community Membership
• Class Year •Number of Student Activities
• Alumni Non-Grad •Number of Alumni Activities
• Current or Former Parent •Number of Reunions Attended
• Board Member •Marital Status
• Friend •Birth Date
• Volunteer •Occupation
• Subscriber •Requests for Information
• Employee •Number of Communications
• Professor •Quality of Communications
• Committee Member •Portfolio Assignments
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14. SOURCE OF GIFT
• Fund/Appeal Tracking
• Online Giving
• Mail Response Giving
• Event Giving
• Payroll deduction
• Honor/Memorial giving
• Planned Gifts
• Event or Ticket Sales, Registration Fees
• Individual giving and Household giving
• Foundation giving versus Individual giving
• Corporate giving versus Individual giving
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15. ADDRESS CODING
• Mailing address
- Home Address versus Business Address
- Change offered by donor versus vendor-purchased addresses
• Individual or household solicitation
• Email address
- Change offered by donor versus vendor-purchased addresses
• Phone number
- Change offered by donor versus vendor-purchased phone number
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16. EVENT PARTICIPATION
• Event attendance versus type of event
- Major giving prospects attended a major giving event
- Does their attendance predict their likelihood to make a major
gift?
- Were they prior donors or new cultivation prospects?
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17. DATA MINING EXAMPLE
• In the midst of a campaign an organization had been
coding that donors were attending philanthropy driven
events. Development office worked to have more types of
events included so additional information was available
about this type of participation by potential donors.
• Looked at combined event attendance as well as other types of
participation
a) Alumni Event
b) Campaign Event
c) Cabinet-Only Events
d) Staff Attended Events – as part of their job
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18. DATA MINING EXAMPLE
Event Participation
- Donor A – Event data lives outside main database
– Attends an annual event for a $100 ticket price
– Single interaction with your organization each year
- Donor B – Event data lives inside main database
– Attends the same annual event as Donor A
– Participates in Alumni Reunion every year
– Gives over $1,000 level and upgrades giving every year
– Has given 2 telemarketing gifts
– Called the call center twice
Pairing event data up with other donor interactions helps you distinguish average
donors from extraordinary donors
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19. DATA INTEGRITY
Involve the Entire Team…
• More individuals working on data means more information but
also…more room for error
• Set standards and use fundraising system to maintain them
• May try using specific forms. Can act as a “preview for data”
• Batch entry can help make the process more efficient but should be
tightly controlled
• Develop documentation that guides those placing information in the
system
• Do you have a secondary research database? This type of approach
can allow for qualification while helping to avoid erroneous data in the
main system
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20. DATA INTEGRITY
Monitor your data quality regularly
• Identify 100 donors randomly, each year, and thoroughly review
their data
- Incorrect data (typos, moved, married, dead)
- Duplicate records
- Missing information
- Correct treatment (clubs, tracks, expire dates)
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21. EXTERNAL SOURCES
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22. SINGLE SCOOP OR LARGE SUNDAE?
You may want both but in most cases there is a best fit.
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23. EXTERNAL SOURCES – WORKING WITH VENDORS
• Questions to ask…
- What is our budget?
- What do we need to know (major vs annual capacity)?
- How many records will we screen?
- Will we screen all at once or do we want to do ad hoc
screenings?
- Who will manage and qualify the new data?
- How much time can they devote to the process?
• Make sure to explore the full range of options when thinking
through the resources you will need
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24. EXTERNAL SOURCES – FREE (FOR THE MOST PART)
OPTIONS
•External sources are a great place to look for a combination of free and
cost options
•Remember there are also local sources in addition to better known
options (i.e. Business Journals)
•Do not forget peer review as a great option as well
•Establishing a form that helps guide the specific types of information
that you are looking for can assist
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25. MAKING IT ALL WORK
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26. ORGANIZING DATA
Begin with a data dictionary
•Think through common terms for both internal and external data
sources
•Document these terms and be prepared to perform data audits to
monitor quality
•Example: Nurse, Nurse Manager, Personal Care Technical =
Employee
•Can be accomplished through committee but should have a “lead”
from Prospect Research
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27. ORGANIZING DATA
Think through where data is stored naturally
•i.e. – Most CRMs will support proper salutation information and this
does not need to be duplicated in notes
Think through the export process
•Data will not be meaningful unless we can export or use the
information for queries or reports
•Standardization will help here
•Ask…should it be text field? A number field? Do I want to be able
to order the information?
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28. QUESTIONS
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29. CONTACT INFORMATION
Page Bullington, MPA
Resource Manager
______________
Target Analytics, a Blackbaud company
2000 Daniel Island Drive
Charleston, SC 29492
Phone 800. 443.9441, ext. 3996
Cell 843.408-6768
Fax 843.216.6100
page.bullington@blackbaud.com
www.blackbaud.com/targetanalytics
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