Target Analytics’ Fundraising ModelsTarget Analytics’ Fundraising Models
Lawrence HenzeNicole Bechard
March 29, 2011
Today’s Agenda
• Target Analytics and Blackbaud
• Predictive Modeling for Direct Marketing
• Predictive Modeling for Donor Development
• Questions and Answers
About Us
• Target Analytics, a Blackbaud Company since 2001
• Backed by Blackbaud’s reputation and experience
• More than 25 years of practical experience exclusively with nonprofits
• Superior software and services from one source• Donor predictive modeling• Prospect research tools such as wealth screening and prospect management software• Donor benchmark comparison reports and program assessments• Integration with The Raiser’s Edge and BBEC
• With the addition of NOZA, we’ve added more prospect research solutions, such as file • With the addition of NOZA, we’ve added more prospect research solutions, such as file screening and subscription to the searchable database of over 50 million gifts
• Our Mission
• Help nonprofits maximize fundraising results…at every stage of the donor life cycle!at every stage of the donor life cycle!
Target Analytics has helped almost 3,500 organizations
American Cancer SocietyNational Baseball Hall of Fame
Stanford UniversityBrown University
Harvard UniversityThe Metropolitan Museum of Art
Habitat For Humanity International
healthcare • human services • k-12 private schools
higher ed • cultural • recreation/social • religious
Habitat For Humanity InternationalAmerican Breast Cancer Foundation
Mayo ClinicWorld Society for the Protection of
AnimalsAmerican Junior Golf Association
Christian Broadcasting Network
MJ2
Slide 4
MJ2 UPDATE THE LIST OF ORGS, DOES 2,500 WORK?Meredith Johnson, 12/02/2007
Supporting the Donor Pyramid
Maximize fundraising results
at every stage of the donor
life cycle with the help of
Target Analytics™
Predictive Modeling for Direct MarketingMarketing
Proactive Research Begins With Data Mining
• Data Mining: Automated or manual extraction or query of information from a constituent database: segmentation analysis, correlation studies, descriptive predictive modeling
• Predictive Modeling: Discovery of underlying meaningful relationships and patterns from historical and current relationships and patterns from historical and current information within a database; using these findings to predict individual behavior
The Benefits of Data Mining and Modeling
• A comprehensive view of your database• Jump starting prospect identification and classification• Potential cost savings• Clean your database • Understand donor/non-donor characteristics• Understand donor/non-donor characteristics• Create cost-effective appeals• Increase gift revenues • Staffing and resource allocation• Knowing your institution, turning knowledge into results
Limitations with House File Data
Do
no
r In
form
ati
on
Active Donors
� Abundant data� Models can help
optimize giving amounts and frequency
Do
no
r In
form
ati
on
House File Segment
Warm Prospects Active Donors Lapsed Donors
Warm Prospects
� Very little data� Can’t differentiate
responsive donors from non-responsive donors
New to File
� Minimal data� Difficult to predict
donor behavior
Lapsed Donors
� Old data� Less predictive
donor behavior
Deep Lapsed Donors
� Very old data� Obsolete for
meaningful selects
Solving House File Challenges: The Coop Component
The models spread and sort the larger donor population into smaller groups ranked by relative response rates
Donor universe beforeapplying models
Donor universe afterapplying models
“Likely” and “unlikely” responders mixed together limits ability to treat donors differently
“Likely” and “unlikely” responders identified and separated into manageable groups
Building Models with the Nonprofit Cooperative Database
Most likely to respond:
Least likely to respond:
Target Model Applied
Building Models with the Nonprofit Cooperative Database
o Some model variables can be fairly straightforward (RFM):
• Days since last gift
• Total number of gifts ever
• Size of last gift
• Etc...
o Many variables are complex measurements of trends created from the
basic data elements on both your file and the Coop:
Recency of giving across organizations, relative to others on your file
The importance, or lack of importance, of disaster giving over time
Change in the pace and trajectory of giving over multiple years
Trend in relative giving to various types of organizations
Ratio of premium gifts over non-premium gifts
Giving density in the past 2, 5 or 10 years
“Share of wallet” at your organization
Giving during trying economic times
Seasonal Giving
Building Models with the Nonprofit Cooperative Database
Using Target Tags with a straight forward approach will increase revenue and response rates
> Contact higher Tag-ranked donors more frequently
< Contact lower Tag-ranked donors less frequently
Tag Score Contact Frequency Tag Score Contact Frequency
A High Appeal as frequently as possible without annoying donors B C Medium High Appeal frequently D E Medium Determine optimal frequency based on response rates F G Medium Low Reduce the number of appeals H I Low Appeal infrequently or not at all for an entire year.
Overcoming Challenges with Warm Prospects
Problems
• Warm prospects populations are tough to segment.
• Performance from prospect files is often lower than anticipated.
Solution
• Using outside philanthropic data from the public domain and the • Using outside philanthropic data from the public domain and the
Coop, models can be built that identify prospects more likely to
respond.
• With these models, segments are formed that rank the relative
responsiveness of these warm prospects.
• Efficiencies are created by focusing more efforts on segments with
more responsive prospects and less on segments with the least
responsive.
Select responsive prospects from marginal prospect lists for
higher returns with less waste with Donor Conversion Tags.
Overcoming Challenges with Warm Prospects
Target TagQty
Available
Quantity
Mailed
Response
Rate
A1 23,500 19,577 1.07%
Model applied to advocacy warm prospects
A2 23,500 19,633 0.94%
A3 23,500 20,639 0.82%
B1 23,500 26,695 0.72%
B2 23,500 19,844 0.70%
B3 23,500 20,701 0.66%
C1 23,500 19,507 0.51%
C2 23,500 14,664 0.45%
C3 23,500 15,866 0.52%
D1 23,500 7,408 0.79%
D2 23,500 8,479 0.48%
D3 23,500 13,586 0.53%
E1 23,500 8,372 0.46%
E2 23,500 4,611 0.45%
E3 23,500 12,235 0.43%
TagTotal
Available
Total
Mailed
Total
Responses
Average
RR
A 128,143 93,526 1,690 1.81%
B 216,671 188,127 1,962 1.04%
C 224,816 175,560 1,466 0.84%
D 224,665 117,956 862 0.73%
E 224,371 32,271 137 0.42%
Model applied to event participants
Overcoming Challenges with Lapsed and Deep Lapsed Donors
Problems
• Outdated information on former donors require greater precision in direct mail efforts
• Mailing more often to lapsed donors who are most likely to return will help streamline these efforts
• RFM can only go so far in lapsed populations, especially in deep-lapsed donors where the RFM is too old to be useful.
Solution
• Use external giving data as well as your organization's own transactional histories to dive deep into lapsed donors behavior.
• As with many of the house file models, the scores allow you to alter your contact frequency strategy.
• Discover who to mail more and who to contact less
Efficiency with Lapsed and Deep Lapsed Donors:
Identify the best prospects for reactivation amongst your lapsed donor
population with Lapsed Tags.
TagTotal
AvailableTotal Mailed
Total
Responses
Average
RR
A 146,885 104,746 1,588 1.52%
B 146,885 106,475 1,357 1.27%B 146,885 106,475 1,357 1.27%
C 146,885 107,623 1,082 1.01%
D 146,885 108,468 845 0.78%
E 146,885 110,451 673 0.61%
F 146,885 112,119 670 0.60%
G 146,885 113,533 586 0.52%
H 146,885 115,324 520 0.45%
I 146,885 117,166 401 0.34%
J 146,885 118,699 300 0.25%
Total: 1,468,853 1,114,604 8,023 0.72%
Contact Strategy Using Lapsed Target Tags
• Overall performance improves with segment and contact strategies by score
Predictive Modeling for Donor DevelopmentDevelopment
Reality Check – What Shape is Your Pyramid?
Pyramid Power
Annual
Defining Mid-Level Giving for Modeling
o Mid-Level Giving falls between annual fund (or direct marketing) and major givingo Prime Upgrades are ready to move from Annual Giving into Mid-Level Givingo Transitional Donors are traveling up the pyramid to Major Giving
Major Annual Giving
Major Giving
Prime Upgrades Transitional Donors
Predictive Modeling - How it Works
• Giving profiles are complex
• Profiles vary by constituency/organization
• Profiles vary by giving level/type
• Giving propensity and capacity are different• Giving propensity and capacity are different
• Propensity and capacity scores will enable you to identify prospects to strengthen your donor pyramid
Data Mining – Internal Data
• Look for internal and transactional data to tell us donor/non-donor characteristics
• Internal
• Age
• Gender
• Major
• Degree• Degree
• Type of Relationship
• Number of relationships
• Transactional
• Membership
• Premiums
• Special events
Data Mining – External Data Adds Depth and Breadth
• Data appended to your file:
• Census
• Cluster data
• Equifax Niche data
• Summarized credit data• Summarized credit data
• Wealth
• Hard asset data
How Modeling Works: Identify the Action to be Predicted
Building the Profile
Scoring the Database
Most Common Target Analytics Fundraising Models
Planned Gift Likelihood
Major Gift Likelihood
Mid-Level Giving Likelihood
Annual Giving Likelihood
Target Gift Range
Likelihood To Give
Target Analytics Provides an Immediate Strategy for Segmentation and Cultivation
� Highest scores and high assets
� Further qualification and research
� Immediate individual cultivation
� High likelihood scores and mid-level target giving ranges
� Implement targeted upgrade, mid-level major and planned gift strategies
� Increase annual giving
� Lower likelihood scores, but high target giving ranges and assets
� Need to be sold on your mission
� Longer term cultivation
� Low likelihood scores and low target giving ranges
� Minimize investment� Consider reduced
resource application
Major Giving Model
Major Giving Score Distribution
Target Gift Range Model
� The capacity model looks at the inclination combined with the capacity a prospect has to make a gift at a certain level to your organization
� Gift range projected by the predictive model for a one year period
� Target Gift Ranges are numbered 1 to 12, from $1-50 to $100,000+
7: $2,501 - $5,0001: $1 - $50 7: $2,501 - $5,000
8: $5,001 -$10,000
9: $10,001 - $25,000
10: $25,001 - $50,000
11: $50,001 - $100,000
12: $100,001 +
1: $1 - $50
2: $51 - $100
3: $101 - $250
4: $251 - $500
5: $501 - $1,000
6: $1,001 - $2,500
Target Gift Range
WealthPoint Rating
Identified Major and Transitional Giving Prospects
340 568
58
Implementation Recommendations
Techniques for using scores for assignment
• Mid-Level Prospects have a high mid-level likelihood and capacity fits in mid-range
• Prime Upgrades are highly likely but capacity is just below mid-range
• Transitional giving prospects have high likelihood and capacity just below major giving threshold
• Prospects with the highest scores are ripe for assignment are poised to move to the next level
• Assign newly rated prospects to fill pipeline
Capacity in Mid-Level Giving Range - 12 months
Mid
-Level G
ivin
g
Lik
eli
ho
od
$1,001-2,500
$2,501-5,000
$5,001-$10,000
$10,001-25,000
$10,000+
Very Good+
Good
The Power of Combining Wealth and Modeling
• A recent study completed by one of our senior statisticians showed that Wealth and Modeling together account for higher gift potential in a database than either method by itself
Planned Giving Likelihood Model (PGL)
Based on our national research of individuals that have made planned gifts to charitable organizations, your best planned giving prospects have the following characteristics. They:
• Are past givers to you
• Tend to be mid-age and older
• Live alone
• Live in neighborhoods where many of the residents are retired
• Maintain high incomes• Maintain high incomes
• Maintain a low mortgage balance or have paid off their mortgage
• Do not apply for additional credit
• Keep their credit balances low even if their credit limits are large
• Are direct mail responsive
PGL Variable Distribution
Organizational Publications
� Use what you have available
� Seek dedicated “internal inventory” so that you know the printing schedule and can plan your marketing
Summary and Questions
• Contact:[email protected]
510-227-5325
[email protected]@Blackbaud.com843-991-9921
• White Papers: http://www.blackbaud.com/company/resources/whitepapers/whitepapers.aspx