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

• JFeely@opb.org

• Located in Portland, OR

• Katherine Swank, J.D.

• Target Analytics, a division of

Blackbaud, Inc.

• Senior Consultant III

• 843-670-7278

• KatherineSwank@Blackbaud.com

• Located in Denver, CO