Target Analytics’ Fundraising ModelsTarget Analytics’ Fundraising Models

Lawrence HenzeNicole Bechard

April 26, 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:Nicole.Bechard@Blackbaud.com

510-227-5325

Lawrence.Henze@Blackbaud.comLawrence.Henze@Blackbaud.com843-991-9921

• White Papers: http://www.blackbaud.com/company/resources/whitepapers/whitepapers.aspx