Using Analytics to Increase Efficiencies of Portfolio Growth and Management

Transcription

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 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

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 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 Overview Introduction to analytics Mythology around big data/predictive modeling Analytics as philanthropic opportunity Utility of analytics based on program

6 Analytics All about analysis Putting data into decision making Business Intelligence Replaces I think

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 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 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

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11 Case Study Direct Mail Comprehensive Direct Mail Program FY13 - $735K gross 12,000 total gifts

12 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

13 Case Study Direct Mail 399 Donors Identified Made 6,977 gifts representing $173,444 Average Gift - $24.86 Planned Giving Prospects

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16 H. Lee Moffitt Cancer Center Case Study

17 Moffitt Case Study the biggest challenge of managing data is making sure it s not just a data dump

18 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.

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20 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

21 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

22 The Moffitt Foundation is moving toward an analytics model which will move us to the Science of Development

23 Analytics and Modeling We are statistically identifying our donors Using analytics to data-mine our own data Removes personality however.

24 we still close gifts in the living room

25 Using Connectivity Multiple points of touch Example: event vs. education Re-adjusting and rebalancing portfolios

26 Estimated Capacity Very Connected Not Assigned Connected Not Assigned $100M $10M-$99.9M $5M-$9.9M $1M-$4.9M $500K-$999K $100K-$499K ,507 1,693 Content courtesy Bentz Whaley Flessner

27 New Prospect Research Role Traditional research role that includes prospect pipeline management More robust and analytics-based prospect research

28 New patient DOES NOT opt out Proceed with HIPAA compliant process LEADERSHIP VISITS Engaging High Capacity Patients 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

29 New patient DOES NOT opt out Proceed with HIPAA compliant process B and C Rating Engaging High Capacity Patients 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

30 And a word about HIPAA Recent changes present even better opportunities to refine data But compliance continues to be critically important

31 Success is a science; if you have the conditions, you will have the result. Oscar Wilde

32 Thank you!