PLANNED GIVING:
BREAKING NEW GROUND
Julie Feely
Katherine Swank
Name Julie Feely
Title Director Gift Planning Oregon Public Broadcasting
Development Background
• Public Broadcasting, Higher Education • Raised $150 million
Interesting Facts • Board member of NWPGRT • Co-chair NW Planned Giving Conference 2014
Publications & Presentations:
• Conference presenter at CASE, PBS DevCon, PMDMC, and NWPGRT
Your Facilitator
Name Katherine Swank, J.D.
Title Senior Fundraising Consultant Target Analytics, a division of Blackbaud, Inc.
Development Background
• Public Broadcasting, Health and Higher Education • Raised over $200 million
Interesting Facts
• Past president, Colorado Planned Giving Roundtable • Affiliate faculty, Regis University’s Masters in Global
Nonprofit Leadership program • Member, Partners for Philanthropic Planning
Publications & Presentations:
• www.npENGAGE.com fundraising blog • Creating a Legacy: Building a Planned Giving Program
from the Ground Up @ www.blackbaud.com/resources • Presentations @ www.slideshare.net/kswank
Your Facilitator
Special thanks to our
Platinum Sponsors
Session Objectives
• Collect Useful Data for Your PG Program
• Use Data to Understand Your PG Donor
• Over Time - Increase Your Data IQ
• Targeted Marketing by Age Groups
• Incorporating Social Media into Your
Marketing
Collecting Data • Getting started with data
• Types of data available
• Choosing data by your current
sophistication
Getting Started with Data
Easy: Define Your Current PG Donors
Simple: Apply a Prescribed Formula
Technical: Build Distinctive Models
Types of Data Available Partial List
INFORM
DELIVER
Internal
• Demographic
• Giving history
• Membership history
• Relationship
• Activities/ Transactional
• Attitudinal
• Interests
External
• U.S. Census
• Age/Lifestyle Clusters
• HH Wealth & Income
• HH Philanthropic Data
• Modeled Wealth &
Income
• Social Media Influence
Putting Data Into Action D
ata
Min
ing • Picking out
information from databases
• Doesn’t answer specific questions
• Analyzes trends and profiles
• What data is available for my analysis?”
De
scri
pti
ve S
tati
stic
s • Mined, collected and/or purchased data
• Builds descriptions for identification
• What characteristics do our current CGA donors have in common? or,
• Which records have certain prescribed characteristics?
Pre
dic
tive
Mo
de
ling • Discovery of
meaningful relationships and patterns from profiles that answer a specific question
• Who are the most likely individuals on my database to consider a charitable gift annuity?
What’s Your Sophistication Benchmark
(Data Mining)
Surveys
(Descriptive Statistics)
Models
(Predictive Modeling)
• Simple “picture” of your current PG donor
• Good start to using your own data
• Applies findings of outside source; doesn’t
define your organization’s unique donor
• Requires you to start using outside data
• Vendor conducts sophisticated analysis of
millions of combinations of data to define
your organization’s unique donor
Using Data to Understand Your
Planned Gift Donor
• Simple uses of data
• Using surveys and prescriptive
formulas
• Predictive Philanthropic Data
• Advancing to predictive
behavior modeling
Simple Uses of Data
Univariate Analysis
Uses a single variable for descriptive purposes
You’re already using single variable analyses
• Averages, sum of values divided by observations
• Medians, the middle value
• Modes, most common value
• Ranges, from lowest to highest
Why use them?
• Comparative purposes
• Understand the data you’ve collected
Case Study #1
Age Analysis
for Planned
Gifts
All planned gift donors plotted by age
• This example is normal for most organizations
8%
9%
14%
12%
8% 9%
16%
24%
Cluster E
Cluster I
Cluster M
Cluster N
Cluster S
Cluster Y
Cluster X
All Other Clusters
Case Study #2
Cluster
Analysis for
Gift Annuity
Donors
Append clusters; find % of CGAs in each cluster
• 76% of gift annuities were in 7 clusters
• Market to all records also in those clusters
67 Average Age
$91,000 Average Income
Gardening
$146,00 Average Home Value
Retired
Art
Mail Respon-
sive
College educated
Golf, Watches Sports
Stock Market
Cluster Information C
lust
er:
Em
pty
Nes
ts/D
eep
Po
cket
s
Case Study #3
Real Estate
Analysis for
CRT Gifts
All CRT donors plotted by real estate holdings
• Uses prospect research to better understand
specific groups of donors in your database
9% 8%
12%
27%
23%
11% 10%
Unknown < $500,000 $500K -$999K
$1 M - $2M
$2 M - $3M
$3 M - $5M
$5 M+
Total Real Estate Holdings50% of
your CRT
donors
Surveys & Formulas
Multiple Data
Points
Uses multiple variables for segmentation
purposes
Surveys and formulas are easy to understand
• Specific data points are used
• Can collect or purchase
• Easy to apply
Why use them?
• Methodology using your collected data
• Focuses your attention on a general profile
Case Study #4 20-year Study
on Planned
Giving
Behavior
Highest Likelihood to Leave a Gift • Graduate degrees
• Volunteers
• Increased activity for ages 55-64
• Married households and single women
• Households with incomes of $100,000+
Facts about Bequests • 93% of decedents reported having made their gift at
least one decade prior to death
• 80% of $$$$$ comes from those who have reached 80+
• 40% of bequests come from those who made their first designation in their 40s or 50s
Source: Inside the Mind of the Bequest Donor, Professor Russell James, Texas Tech University, 2013
Predictive modeling answers a specific question, such as
• Who are my best potential bequest donors?
• The results provide a ranking or ordering tool for prospect identification, assignment and marketing
Applies a statistical analysis which allows data to identify itself as important
• Data points support your program in a non-biased way
• Often these models are probit regression analyses vs. recency, frequency, amount formula
Predictive Behavior Modeling
Modeling Results Provide
Prospect Prioritization
Each individual is
scored which
creates a rank
order of most
likely prospects to
least likely
Case Study #5 A ‘Sister’
Public Radio
Station’s
Actual Bequest
Donor Model
• Pinpoints which exact pieces of data define their unique bequest donor
• Pie-slice ‘weight’ shows the value of the variable compared to others in the model
Yrs of Giving
Assets
Interest in News/Financial
CC Balance to Limit Ratio
Age 65-74
# of Loans
Social Media
Images by Pierre Rattini
Reality Check
• 46% of seniors use
social networking
sites
• More woman using
social networking
• Facebook is the
network of choice
Planned Giving + Social
Networks
• Build a community not a site
• Avenue for sharing ideas
• Visually driven
Collaboration
Works
Include planned
giving message
into existing
e-news or
Facebook page
Overview & Take-Aways • Data-driven planned giving increases
efficiency, effectiveness, revenue
• Start by getting your arms around simple
uses of data
• Grow your use of data and sophistication
over time; make a plan to grow your level
of sophistication
Overview & Take-Aways
• Use social media to reach your
target audience
• Plant the seeds but don’t expect
to track gifts to social media
• Visually driven
Thank you!
• Julie Feely
• Oregon Public
Broadcasting
• Director Gift Planning
• 503-293-1935
• Located in Portland, OR
• Katherine Swank, J.D.
• Target Analytics, a division of
Blackbaud, Inc.
• Senior Consultant III
• 843-670-7278
• Located in Denver, CO
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