Identifying Wealth & Philanthropy in Your Database · Identifying Wealth & Philanthropy in Your...

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.


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


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


College Grad: 44%

Loyalty Index: Average 4.5/9



Donation AttributesAnnual Donations: $500-$2,500


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.



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.


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.



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


College Grad: 42%

Loyalty Index: Average 4.4/9



Donation AttributesAnnual Donations: $50-$500


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.


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.


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


College Grad: 64%

Loyalty Index: Excellent 7.5/9




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


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.


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.


E. The Masses


College Grad: 30%

Loyalty Index: Poor 3.5/9



Donation AttributesNo record of giving




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

https://npengage.com/nonprofit-fundraising/predictive-modeling-vs-wealth-screening-effective-segmentation-programs-require-a-healthy-mix/

Reach me at:

[email protected]

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.

Identifying Wealth & Philanthropy in Your Database · Identifying Wealth & Philanthropy in Your...

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