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Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
Your Data Goldmine: It's Closer Than You Think
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Your Data Goldmine: It's Closer Than You Think

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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 …

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.

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  • 1. T YOUR DATA GOLD MINE “IT’S CLOSER THAN YOU THINK” Page Bullington, MPA Target Analytics06/09/2011 Footer 1
  • 2. AGENDA • Getting Rid of Silos • Data Mining Defined - A closer look at inside information • External Sources - Thinking outside the box • Final Discussion and Questions06/09/2011 Footer 2
  • 3. GETTING RID OF SILOS • Data does not belong here…06/09/2011 Footer 3
  • 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 06/09/2011 Footer 4
  • 5. CAN WE MOVE TO THIS?Yes…through acombination of focusedinternal and external datamining.06/09/2011 Footer 5
  • 6. D ATA M I N I N G D E F I N E D06/09/2011 Footer 6
  • 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 behavior06/09/2011 Footer 7
  • 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 06/09/2011 Footer 8
  • 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? 06/09/2011 Footer 9
  • 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 engagement06/09/2011 Footer 10
  • 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 anotherThe challenge is using all of this data in a meaningful way…06/09/2011 Footer 11
  • 12. HIDDEN GEMS •Constituency Codes •Source of Gift •Address Coding •Event Participation06/09/2011 Footer 12
  • 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 06/09/2011 Footer 13
  • 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 06/09/2011 Footer 14
  • 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 number06/09/2011 Footer 15
  • 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?06/09/2011 Footer 16
  • 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 job06/09/2011 Footer 17
  • 18. DATA MINING EXAMPLEEvent 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 twicePairing event data up with other donor interactions helps you distinguish average donors from extraordinary donors 06/09/2011 Footer 18
  • 19. DATA INTEGRITYInvolve 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 system06/09/2011 Footer 19
  • 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)06/09/2011 Footer 20
  • 21. EXTERNAL SOURCES06/09/2011 Footer 21
  • 22. SINGLE SCOOP OR LARGE SUNDAE? You may want both but in most cases there is a best fit.06/09/2011 Footer 22
  • 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 need06/09/2011 Footer 23
  • 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 assist06/09/2011 Footer 24
  • 25. MAKING IT ALL WORK06/09/2011 Footer 25
  • 26. ORGANIZING DATABegin 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 Research06/09/2011 Footer 26
  • 27. ORGANIZING DATAThink through where data is stored naturally •i.e. – Most CRMs will support proper salutation information and this does not need to be duplicated in notesThink 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?06/09/2011 Footer 27
  • 28. QUESTIONS06/09/2011 Footer 28
  • 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/targetanalytics06/09/2011 Footer 29

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