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It’s not the “same old, same old” in planned giving these days. The digital revolution is driving many changes in how nonprofits are communicating with donors about the planned giving opportunity. Some brave stations are experimenting with new techniques to identify donors, build relationships, and solicit planned gifts. Learn about some of the pioneering work going on in the world of planned giving, including more sophisticated approaches to data analytics, use of social media in communicating with donors and prospects, marketing planned giving opportunities to younger folks in their 40’s (no, that’s not crazy), and creative ways to enable donors help build the buzz by telling their own stories.
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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