identifying wealth & philanthropy in your database · identifying wealth & philanthropy in...
TRANSCRIPT
Segmenting for Success: Identifying Wealth & Philanthropy
in Your Database
Melissa Bank Stepno
Director, Client Services
Great Debates
• Cats vs. Dogs
• Yankees vs. Red Sox
• Liberal vs. Conservative
• Predictive Modeling vs. Wealth Screening
Framework:Definitions & Hypothesis
Descriptive Statistics
Predictive Modeling
Wealth Screening
ResearchField
Qualification
Segmentation & Identification: Best Practices
Where have we been?
Looks at PAST PERFORMANCE
Tells the story
What has happened?
How has it happened?
Examples: How many $1K+ donors did you have last year?
What percentage of $1K+ donors retained, upgraded,
downgraded or lapsed?
What is the comparison between the number of donors
who retained last year compared to two years ago?
What is the average age of our donors?
Definition: Descriptive Statistics
Where might we be able to go?
Suggests the LIKELIHOOD of something happening
Forecasts the future
What might happen?
Who might do something?
Examples:
Who is most likely to give to my organization in the
future?
Who is likely to give $10K+ to my organization?
Is a donor more likely to make an annual gift, become
a monthly donor, or put us in their estate plans?
Definition: Predictive Modeling
Quick scan of prospect(s) through multiple public data
sources
Focused on indicators of WEALTH AND ASSETS
Identifies and describes, does not predict
Types of Data:
Assets
Real estate, insider stock, private company
ownership
Biographical information
Board membership, community involvement,
interests, academics
Philanthropy to other non-profits & politics
Definition: Wealth Screening
Predictive Modeling
Definition: Uses past behavior, like giving to your organization, to predict future behavior
Hypothesis: those who look like your best donors are more likely to become your best donors
Downfall: Outliers. Statistics are not 100% accurate
Wealth Screening
Definition: Uses the name/address in your database to ‘match’ to data stored in publically accessible databases
Hypothesis: those who have wealth, asset and philanthropic indicators
Downfall: A large percentage of wealth is hidden. Conversely, just because someone is wealthy, or philanthropic elsewhere, doesn’t mean they will want to support your mission
Forward Looking Strategy: Traditional Approach
Other industries rely heavily on
predictive models that estimate
wealth and assets.
What could this data help us understand about PHILANTHROPY?
Target Analytics Research: Our HypothesisRather than PREDICITIVE MODELING vs. WEALTH SCREENING….
We know the benefits and limitations of traditional
Predictive Modeling and Wealth Screening used for
Philanthropic purposes.
Step One: Obtain Wealth Attributes
Mean $72,000
Median $55,000
Top 10% $142,000
Top 5% $179,000
Cap $10,000,000
14% of the highest income
are in the lowest 10% of
spending
17% of the lowest income
are in the highest 25% of
spending
Annual
Income
Details:
Discretionary
Spending
Details:
Wealth Attributes of U.S. Households
Mean $272,000
Median $38,000
Top 10% $216,000
Top 5% $1,468,000
Cap $20,000,000
Mean $381,000
Median $112,000
Top 10% $498,000
Top 5% $1,702,000
Cap $50,000,000
Net
Worth
Details:
Invested
Assets
Details:
Wealth Attributes of U.S. Households
Interesting ObservationsFocusing on “TRADITIONAL” RFM
Donors who give more RECENTLY, more FREQUENTLY
or who have given more MONEY over their lifetime:
• Skew older
• More likely to be married
• Highly educated
• Home values are higher
• Invested Assets higher
• Net Worth higher
• Less likely to be active on Social Media
Step Two: Create Philanthropic Based Segmentation
PhilanthropyTarget Analytics data assets,
including over 4 billion donor
transactions from over 75 million
US households
Wealth AttributesLeverage Wealth Attributes
(Income, Net Worth, Invested
Assets & Discretionary Spending)
and additional descriptive
demographics on over 200 million
US consumers
Insightful AnalyticsApply analytics to describe and
predict philanthropic behavior
The Analysis
5 Donor Groups and 13 Segments
E. The MassesE1. Blue Collar Masses
E2. Non-starter Masses
A. PhilanthropistsA1. High Net Worth Philanthropists
A2. Financially Secure Philanthropists
A3. Upwardly Mobile Philanthropists
B. HumanitariansB1. Steady Humanitarians
B2. Devoted Humanitarians
B3. Faithful Humanitarians
C. Casual DonorsC1. Middle Class Casual Donors
C2. Working Class Casual Donors
C3. Marginal Casual Donors
C4. Sporadic Casual Donors
D. EnigmasD1. Affluent Enigmas
A. Philanthropists
Motto
Power should be used wisely.
Characteristics
Success, Wisdom, Power, Intelligence, Loyalty
General Description
Stable donors with ample means, they’re educated,
environmentally conscious, tech savvy and loyal.
Attitudes toward Giving
They want to spread success to the world. Optimists, they
respond to positive-potential messaging. They seek mass
scale improvements rather than on single cases.
8% of US population
Wealth Attributes% of
US pop
Annual
Income
Net
Worth
Invested
Assets
Discretionary
Spending
All Philanthropists 8% $210k $2.1 million $1.7 million $15.8k
A1. High Net Worth 0.5% $368k $7.2 million $6.1 million $24.0k
A2. Financially Secure 0.8% $284k $3.9 million $3.3 million $20.0k
A3. Upwardly Mobile 7.0% $190k $1.5 million $1.1 million $14.8k
A. Philanthropists 8% of US population
Demographic Attributes1
College Grad: 61%
Loyalty Index: Good 6.5/9
Social: Facebook 48%, Twitter 37%
Responsiveness: Email 3.8/5, DM 2.2/5
Donation AttributesAnnual Donations: $4,000+
Donation Frequency: 1 to 3+ per year
Donation Amount: $250+
LTV per org: $1,500+
1 For explanations of various Demographic attributes, see Footnotes on final slide.
B. Humanitarians
Motto
Love your neighbor.
Characteristics
Compassion, Generosity, Faith, Kindness, Courage
General Description
More modest in means than Philanthropists, they give
much more frequently. Less educated and less
environmentally conscious, they want to maximize assets,
so are less loyal.
Attitudes toward Giving
Giving until it hurts and relating to grass roots issues,
they’re engaged by messages of need. They seek to help
their fellow man instead of changing the world on a mass
scale.
13% of US population
Wealth Attributes% of
US pop
Annual
Income
Net
Worth
Invested
Assets
Discretionary
Spending
All Humanitarians 13% $72k $336k $195k $9.6k
B1. Steady 2.7% $94k $549k $342k $10.3k
B2. Devoted 6.3% $83k $375k $222k $9.9k
B3. Faithful 4.4% $44k $151k $66k $8.6k
B. Humanitarians
Demographic Attributes1
College Grad: 44%
Loyalty Index: Average 4.5/9
Social: Facebook 53%, Twitter 18%
Responsiveness: Email 2.9/5, DM 3.5/5
Donation AttributesAnnual Donations: $500-$2,500
Donation Frequency: 4 to 6+ per year
Donation Amount: $15-$100
LTV per org: $500+
Note: Although with slightly less financial capacity, Devoted Humanitarians give much more often than Steady Humanitarians, with higher lifetime values.
1 For explanations of various Demographic attributes, see Footnotes on final slide.
13% of US population
C. Casual Donors
Motto
We’re in this together.
Characteristics
Fairness, Immediacy, Togetherness, Inclusion
General Description
Middle class with more varied incomes, they give more
casually than Humanitarians while sharing similarities in
education, environmental views, tech awareness and
loyalty.
Attitudes toward Giving
They are willing to help but do not do so consistently. They
respond to positive messages, but relate more to needs.
They want a better world, but concentrate mainly on their
more immediate part of it.
35% of US population
Wealth Attributes% of
US pop
Annual
Income
Net
Worth
Invested
Assets
Discretionary
Spending
All Rank and File 35% $72k $273k $159k $9.3k
C1. Middle Class 5.7% $119k $555k $360k $10.9k
C2. Working Class 8.3% $55k $150k $70k $8.7k
C3. Marginal 4.7% $70k $336k $201k $9.7k
C4. Sporadic 16.7% $65k $220k $121k $9.0k
C. Casual Donors 35% of US population
Demographic Attributes1
College Grad: 42%
Loyalty Index: Average 4.4/9
Social: Facebook 53%, Twitter 20%
Responsiveness: Email 2.9/5, DM 3.4/5
Donation AttributesAnnual Donations: $50-$500
Donation Frequency: 1 to 2+ per year
Donation Amount: $10-$75
LTV per org: $250+
Note: C1 and C2 segments give about 3 times more often than C3 and C4, with significantly higher LTVs.
1 For explanations of various Demographic attributes, see Footnotes on final slide.
D. Enigmas
Motto
You don’t get
what you don’t ask for.
Characteristics
Individual, Autonomous, Guarded
General Description
With no giving history, they have donor potential based on
assets alone. Otherwise they’re like Philanthropists:
financially secure, educated, environmentally conscious,
tech savvy, and even more loyal.
Attitudes toward Giving
Self-made, they think others can succeed in kind. They
may respond to positive or need based messages, but
conversion takes committed effort and convincing
arguments.
4% of US population
D. Enigmas
Demographic Attributes1
College Grad: 64%
Loyalty Index: Excellent 7.5/9
Social: Facebook 50%, Twitter 31%
Responsiveness: Email 3.5/5, DM 2.3/5
Wealth Attributes% of
US pop
Annual
Income
Net
Worth
Invested
Assets
Discretionary
Spending
All Enigmas 4% $188k $987k $693k $19.4k
Donation AttributesNo record of giving
4% of US population
1 For explanations of various Demographic attributes, see Footnotes on final slide.
E. The Masses
Motto
Life is hard.
Characteristics
Distracted, Burdened
General Description
With little means and no giving history, they offer poor
donor potential. Among all groups, they have the lowest
levels of education, environmental awareness, tech savvy
and loyalty.
Attitudes toward Giving
They tend to lack perspectives that drive giving
considerations. Unaffected by messages of potential, they
may respond to needs, yet maintaining their support will be
problematic.
40% of US population
E. The Masses
Demographic Attributes1
College Grad: 30%
Loyalty Index: Poor 3.5/9
Social: Facebook 45%, Twitter 11%
Responsiveness: Email 2.8/5, DM 3.7/5
Donation AttributesNo record of giving
40% of US population
1 For explanations of various Demographic attributes, see Footnotes on final slide.
Wealth Attributes% of
US pop
Annual
Income
Net
Worth
Invested
Assets
Discretionary
Spending
All The Masses 40% $54k $175k $95k $8.7k
E1. Blue Collar 17.4% $62k $246k $148k $9.4k
E2. Non-starter 22.0% $48k $119k $54k $8.1k
Putting it All Together:Recommendations
1. “Tried and true” methodology may NO LONGER BE SUFFICIENT. Big
data and analytics will continue to push fundraisers to think differently,
more creatively, and more strategically – this is good for our individual
organizations and for our industry!
2. Understand that a MULTI-FACETED strategy for prospect identification is
important. People are not ‘one-sized-fits-all’ and neither should be your
strategy.
3. Knowing WHO to contact is just as important as understanding
HOW to contact them and WHAT messaging might resonate
4. Conversely, knowing which segments are LEAST WORTHY of your time,
talent and treasure will help your organization be more effective
Top Take Aways
Read more about it: Predictive Modeling vs. Wealth Screening: Effective Segmentation Programs Require a Healthy Mix
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Appendix
Footnotes:
1 Results are based on repeated independent tests with large financial services companies.
2 Measured correlation between values and respective verified values for a large random sample of consumers.
3 Explanation of various demographic attributes: Loyalty index is based on a score from 0 to 9, with lower scores indicating less and higher
scores indicating more loyalty. For example, a score of 8.1 indicates a very loyal consumer not likely to switch to a competitor, and a 3.0
means a person who is relatively likely to switch. Similarly, email and direct Mail (DM) responsiveness scores are based on a 0 – 5 scale,
with lower scores indicating less responsiveness than higher scores.