PMDMC Conference: Planned Giving: Breaking New Ground_July 2014

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It’s not the “same old, same old” in planned giving these …

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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  • 1. PLANNED GIVING: BREAKING NEW GROUND Julie Feely Katherine Swank
  • 2. 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
  • 3. 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: • fundraising blog • Creating a Legacy: Building a Planned Giving Program from the Ground Up @ • Presentations @ Your Facilitator
  • 4. Special thanks to our Platinum Sponsors
  • 5. 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
  • 6. Collecting Data • Getting started with data • Types of data available • Choosing data by your current sophistication
  • 7. Getting Started with Data Easy: Define Your Current PG Donors Simple: Apply a Prescribed Formula Technical: Build Distinctive Models
  • 8. 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
  • 9. Putting Data Into Action DataMining • Picking out information from databases • Doesn’t answer specific questions • Analyzes trends and profiles • What data is available for my analysis?” DescriptiveStatistics • 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? PredictiveModeling • 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?
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. Case Study #1 Age Analysis for Planned Gifts All planned gift donors plotted by age • This example is normal for most organizations
  • 14. 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
  • 15. 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 InformationCluster:EmptyNests/DeepPockets
  • 16. 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 - $2 M $2 M - $3 M $3 M - $5 M $5 M+ Total Real Estate Holdings 50% of your CRT donors
  • 17. 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
  • 18. 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
  • 19. 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
  • 20. Modeling Results Provide Prospect Prioritization Each individual is scored which creates a rank order of most likely prospects to least likely
  • 21. 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
  • 22. Social Media Images by Pierre Rattini
  • 23. Reality Check
  • 24. • 46% of seniors use social networking sites • More woman using social networking • Facebook is the network of choice
  • 25. Planned Giving + Social Networks • Build a community not a site • Avenue for sharing ideas • Visually driven
  • 26. Collaboration Works Include planned giving message into existing e-news or Facebook page
  • 27. 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
  • 28. 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
  • 29. 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