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 2010-2017

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

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

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….

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. 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

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
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

29. 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

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!