1. Discovering, opening and developing PG relationships with donor surveys Doug Puffer, Director, Planned Giving Simon Fraser University November 9, 2012

2. Warm the Brain 7H15 M355463 53RV35 70 PR0V3 0UR BR41N5 C4N D0 4M4Z1N6 7H1N65! 1MPR3551V3 7H1N65! 1N 7H3 B361NN1N6 17 WA5 H4RD3R BU7 N0W, BY 7H15 LIN3 Y0UR M1ND 1S R34D1N6 17 4U70M471C4LLY W17H0U7 3V3N 7H1NK1N6 4B0U7 17.

3. Planning to Market Prospect Profiling Data Analysis Finding the Big Ones Overview

4. Numbers: 0 18000 X 2, 50+ 3.5 years = 7 touches 0.5 % = 630 302 info, 52 leads, 31 discoveries Get Visible

5. Words: Plan ahead Develop demographic Get the message out Benchmark standards (CPL, CPC) Results justify further investment Get Visible

6. Bequests and Leads

7. Profiling and Data Mining Best prospects for a gift by will Profile first Parameters second Data mining third Prioritize lists Targeted markets

8. In the beginning, keep it simple! [(A>50)+MI55+(R+F+M)]=PGP

9. In the beginning, keep it simple! Instant results matter Age = 50+ Title = Miss Loyal Donors = R Monthly Donors = F Generous Donors = M

10. Include: who gives, who will, who didn’t, who did, who won’t Planned Giving Propensity Who said “YES” Who said “NO” Who did but you didn’t know Giving records, degrees, job info, addresses, phones, email, sports, campus clubs, reunions,...everything!

11. Refined Profile Alumni volunteer Regular donor (3/7 years) Job title Number of actions Highest degree The SFU Algorithm

12. Finding the Big Ones Donors love you They know you Right age Good records Invisible to “Normal” prospect research Limited budget

13. Bequests and Leads

14. Finding the Big Ones Donors love you They know you Right age Good records Invisible to “Normal” prospect research Limited budget

15. Results SFU UBC QU 2008 Calling Pool 4554831 4957 Completion Rate 48% 66% 60% New Leads 428 110 117 Potential 369 208 730 Discoveries35 17 60 Expected Value$3.15M$2.04M $6.60M Actual Cost $15.7K

16. Brain Science Prof. Russell James, Texas Tech Bequest giving and current giving stimulate different parts of the brain. Different motivators and de-motivators are at work. Charitable bequest decision making engages parts of the brain associated with, what researchers call, “management of death salience.” Charitable bequest decision making involves reminders of one’s mortality. A charitable bequest decision involves the internal visualization system for recalling autobiographical events in relation to the charity.

17. References: Does Data Mining Really Work for Higher Education Fundraising? A Study of the Results of Predictive Models Built for 5 Higher Education Institutions By Peter B. Wylie and John Sammis http://www.case.org/Documents/Books/29502/Does_Data_Mining_Really_Work.pdf Data Mining and Alumni Association Membership By Peter B. Wylie and John Sammis. http://www.case.org/Documents/Publications_and_Products/Reports/DataMiningandAlum niAssociationMembership.pdf A pauper’s guide to electronic screening. Guest post by Peter B. Wylie http://cooldata.wordpress.com/2010/09/21/a-paupers-guide-to-electronic-screening/ Data Mining for Fund Raisers Peter B. Wylie C.A.S.E. (2004).ISBN-10: 0899643809 or ISBN-13: 978-0899643809 Fundraising Analytics: Using Data to Guide Strategy Joshua M. Birkholz (April 4, 2008)AFP/ John Wiley pub. ISBN-10: 047016557X or ISBN-13: 978-0470165577 Charitable Estate Planning as Visualized Autobiography: An fMRI Study of its Neural Correlates (February 6, 2012). James, Russell N. and O'Boyle, Michael W., Available at SSRN: http://ssrn.com/abstract=2000345 or http://dx.doi.org/10.2139/ssrn.2000345

18. Thanks for listening!