NACCDO Using Analytics to increase Efficiencies of Portfolio Growth and Management - Hibler, McGirk

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NACCDO Using Analytics to increase Efficiencies of Portfolio Growth and Management - Hibler, McGirk

  1. 1. Using Analytics to Increase Efficiencies of Portfolio Growth and Management Cindy McGirk, RN, MBA, JD Manager, Strategic Initiatives H. Lee Moffitt Cancer Center Foundation Michael C. Hibler, MPA Sr. Associate Director of Development The Johns Hopkins Kimmel Cancer Center
  2. 2. Johns Hopkins Kimmel Cancer Center Matrix Cancer Center in Baltimore, MD 6,000 New Patients / 72 inpatient beds 7 Fundraisers / 1 Professional Support / 4 Admin FY 13 - $98M Raised over half of $500M Campaign 2010-2017
  3. 3. H. Lee Moffitt Cancer Center “Stand-Alone” NCI Comprehensive Cancer Center in Tampa, Florida More than 17,500 new patients (FY13)/206 Inpatient beds Goal FY14 $23.3 Million $300 Million Comprehensive Campaign Mission: “…to contribute to the prevention and cure of cancer…”
  4. 4. Moffitt Cancer Center Foundation  Vice President 4 Management Team (3 with revenue goals) 5 Gift Officers (3 MGO, 2 PGO) 1 Annual Fund/Direct Mail Staff 2 Grant Writers (1 PT) 1 Prospect Research/Development Staff 3 Special Events Staff 5 Operations/Data Analyst Staff 3 Support Staff 25 TOTAL
  5. 5. Overview Introduction to analytics Mythology around big data/predictive modeling Analytics as philanthropic opportunity Utility of analytics based on program
  6. 6. Analytics All about analysis Putting data into decision making • Business Intelligence Replaces “I think…”
  7. 7. Big Data & Predictive Modeling Big data is normal data Is your data good? Ask a question of your data – data mining Predictive Modeling – scoring data
  8. 8. Predictive Modeling Identify patterns in data Strength of variables and correlation -quantitative There is a correlation between years on file, frequency of giving, lifetime giving amount, and whether a donor is likely to lapse. The stronger the correlation between variables, the more likely that the model will predict the outcome correctly. Causation and human element – qualitative Taller people make more money. If we ran an analysis of this, we would find that there is a high correlation between taller heights and higher incomes. This does not mean that height causes higher incomes, but more likely that the largest population of unemployed in the United States are children, and children tend to be shorter than adults. It is better and more accurately correlated with age.
  9. 9. Philanthropic Opportunity Build it, Buy it, or Borrow it Find new donors • Campaign analysis Segment donors • Annual / direct mail / social media New way to visualize data
  10. 10. Case Study – Direct Mail Comprehensive Direct Mail Program FY13 - • $735K gross • 12,000 total gifts
  11. 11. Case Study – Direct Mail Donor loyalty • 10+ lifetime gifts • Lifetime revenue of $100-$4,999 • Largest gift of $99.99 or less • Most recent gift between August – December, 2013 • First gift 6 or more years ago • All made gifts in FY14 and then 3,4,5+ consecutive years in a row prior to that
  12. 12. Case Study – Direct Mail 399 Donors Identified Made 6,977 gifts representing $173,444 Average Gift - $24.86 Planned Giving Prospects
  13. 13. H. Lee Moffitt Cancer Center Case Study
  14. 14. Moffitt Case Study …the biggest challenge of managing data is making sure it’s not just a data dump…
  15. 15. Case in Point • Wealth screened and assigned highest scored to MGO/PGO • Suppressed from all mailings and “strategic calling” • Theoretically would receive personalized communications from MGO/PGO, including personalized high-end packets The results….
  16. 16. Results • Inconsistent follow-up • Names suppressed from other modalities • MGOs/PGOs had unmanageable portfolios • Move toward Campaign necessitated new, strategic thinking • Enter Analytics and Predictive Modeling
  17. 17. Comparing Screening and Modeling Wealth Screening • Identifies wealthy constituents • Public asset data • Never tells the whole story, but classifies into bands effectively Modeling • Provides filtering and prioritization according to likelihood • Comparative analysis to existing donors • Never tells the whole story, but classifies into high-yield segments effectively Slide courtesy Bentz Whaley Flessner
  18. 18. The Moffitt Foundation is moving toward an analytics model which will move us to the “Science of Development”
  19. 19. Analytics and Modeling • We are statistically identifying our donors • Using analytics to “data-mine”…our own data • Removes “personality”…however….
  20. 20. …we still close gifts in the living room…
  21. 21. Using Connectivity • Multiple points of touch • Example: event vs. education • Re-adjusting and rebalancing portfolios
  22. 22. Content courtesy Bentz Whaley Flessner Estimated Capacity Very Connected Not Assigned Connected Not Assigned $100M+ 1 0 3 0 $10M-$99.9M 3 0 6 0 $5M-$9.9M 1 0 6 2 $1M-$4.9M 24 3 96 53 $500K-$999K 27 9 178 102 $100K-$499K 107 10 2,507 1,693
  23. 23. New Prospect Research Role • Traditional research role that includes prospect pipeline management • More robust and analytics-based prospect research
  24. 24. New patient DOES NOT “opt out” • Proceed with HIPAA compliant process Wealth Screening • Demographic info screened High capacity patients identified • Wealth indicators assigned as “A” sent to Foundation for evaluation Leadership visit may reveal cues • Feedback from Moffitt experience evaluated and triaged LEADERSHIP VISITS Engaging High Capacity Patients
  25. 25. New patient DOES NOT “opt out” • Proceed with HIPAA compliant process Wealth Screening • Demographic info screened High capacity patients identified • Wealth indicators rated as “B” and “C” sent to Foundation for evaluation Development Staff Remind Faculty to Listen for Cues • Follow-up by Development Staff as appropriate “B” and “C” Rating Engaging High Capacity Patients
  26. 26. And a word about HIPAA… • Recent changes present even better opportunities to refine data • But compliance continues to be critically important
  27. 27. “Success is a science; if you have the conditions, you will have the result.” Oscar Wilde
  28. 28. Thank you!

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