A N A L Y T I C S E R V I C E S Expanding the Scope of Prospect Research: Data Mining and Data Modeling Chad Mitchell Blackbaud Analytics September 2,

A N A L Y T I C S E R V I C E S

Expanding the Scope of Prospect Research:

Data Mining and Data Modeling

Chad MitchellBlackbaud Analytics

April 19, 2023

A N A L Y T I C S E R V I C E S 2

Game Plan

• Definitions, Overview and Why?• Data Mining vs. Data Modeling• In-house Solutions• Outsourcing Options • Examples and Cast Studies• Benefits and Risks• Q and A


Background – Chad Mitchell

• Iowa State University– Annual Phone-A-Thon– Alumni Association Ambassador– Major Gifts and Special Event Ambassador

• Experian– Data Modeling and Demographic Data– Blackbaud – Develop Prospect Screening

Service

• Blackbaud Analytics– 250 Clients


Definitions

• Data Mining: Investigating and discovering trends within a constituent database using computer or manual search methods

• Data Modeling (Advanced Statistical Analysis) : Discovery of underlying meaningful relationships and patterns from historical and current information within a database; using these findings to predict individual behavior


Specific Applications of Data Modeling

• Determine subsets of similar individuals from a larger universe

• Segment by characteristics– Interests, finances, location, etc.

• Target marketing• Predicting future behavior


Why Use It?

• Classify donors & prospects by factors other than wealth (or major gift potential):– Lifestyle/life-stage– Affinity– Interests/behaviors– Cultural– Demographics– Psychographics


Go Beyond Capacity

LIK

EL

IHO

OD

CAPACITY

Wealth Screening

Results

Annual Giving

Major Giving

Minimal Investment Cultivate


Benefits of Data Modeling

• Reduce solicitation costs• Increase Response Rates • Understand donor/non-donors

characteristics• Create cost-effective appeals• Increase gift revenues • Staffing and resource allocation• Turn knowledge into results


Why Me? … New Roles for Researchers!

• Prospect research is more than prospect identification

• Leadership role of research– Introduce new analytical/evaluation tools– Results oriented change– Giving is more than major gifts


What Are My Options?

• Do It Yourself– Simple statistics – Data Mining– In-house Data Modeling

• Outsourcing– Advanced Data Modeling– Regression Analysis– Consulting


Simple Statistics

• What is simple?– Frequency distributions– Trend analysis– Segmentation analysis

• Tools– Existing Donor Management Application– Microsoft Excel or Access


Simple Data Mining - Examples

• Time of year giving– Application: anniversary date

solicitation• Giving by solicitation type

– Application: segmented solicitations• Geographic Analysis

– Application: special event and trip planning


Anniversary Date Solicitations

• Objective: reduce solicitations to loyal donors

• Methodology: identify loyal donors with time consistent giving patterns– Contact donors at appropriate renewal

time– Mail or call these donors less frequently– Increase value of their gifts


Segmented Solicitations

• Objective: Increase solicitation effectiveness by using ‘asking’ method appropriate to donor

• Methodology: Factor analysis – Identify common characteristics of those

who give by phone, by mail, etc.– Target groups sharing those

characteristics– Eliminate ineffective solicitations


Special Event Planning


Analyze Every Area of Giving

• Annual Giving– Frequency at lower levels, highest propensity– Most important donor segment

• Major Giving– Determine an appropriate ask amount– Maximize potential of each donor

• Planned Giving– Frequency of giving – 10+ years– No Major Gift giving history


Case Study – Higher Education

Past Giving

Gender

UpscaleCredit Card

Wealthy Zip Code

Graduation Year

DemographicIndicator

Past Giving

Alumni Member

Campus Leader

0

20

40

60

80

100%

University A University B

Two similar organizations with vastly different profiles


Data Modeling – How Do You Do It?

• Challenge yourself• Identify the behavior to be predicted

– for example, annual giving likelihood• Identify variables to be used• Create a file (random sample)

– validate fields to be used• Utilize statistical software package

– SPSS– SAS


Types of Data Modeling

• Clustering• Decision Trees (CHAID)• Neural Networks• Logistical Regression• Probit Regression


How To (continued)

• Split the file in half at random– modeling sample– holdout sample

• Build model• Apply algorithm to holdout sample • Test the model• Score the database• Implement the model


Yes, There Are Risks

• Bad or misleading data• Off the shelf modeling programs• Time intensive• Test, test, test• Applying Generic models

– PRIZM, P$CYLE and MOSAIC


Acceptable Risk

• Potentially rich data in your file • Understanding the big picture• Bringing focus to your development

efforts


Levels of Information

• Individual• Household• ZIP + 4• Block• ZIP

Tip: start at smallest level possible - individual


Types of Data

• Types of Client Data– Demographic– Giving History– Activities/Relationships– Transactional– Attitudinal– Interests


Types of Data

• Sources of External Data– Demographic/Census– Single source databases

- credit– Consumer transactional – Aggregated (avoid

aggregated age)– Cluster

• Vendors– Experian– Acxiom– InfoUSA– D&B– KnowledgeBase

Marketing– List Brokers


Creating Variables

• Additive• Dichotomous (yes/no)• Continuous/quadratic• Composite variables

– State/city• Missing data


Maximizing Your Data


Appended Data

Determine best candidate variables

for modeling process; create new

Composite and dummy variables

Identify best

models and test

results

Client Data

Blending Data into Models

Identify attributes with the

greatest explanatory value;

select and weigh data in

unique algorithm

Final Unique

Algorithm(s)


Case Study – Family / Human Services

• Challenge– Decrease direct mail

expense while increasing annual contributions

• Before BBA– Pieces mailed = 1,200,000– Total No. of Gifts = 3,000– Contributions = $300,000

• After BBA– Pieces mailed = 200,000– Total No. of Gifts = 10,000– Contributions = $1,200,000

• ROI– Contributions = 398%


Outsourcing – Why?

• Models specific to your donors and prospects

• Speed• Cost• Accuracy• Consulting


Vendor Qualification

• Methodology and Philosophy • Experience

– Number of clients– Personnel – Ph.D. Level Statisticians– References– Case Studies

• Integration with Existing Software• Broad Range• Deliverables, Follow-up and Consulting


Outsourcing Examples

Annual Giving Propensity

478

Major Giving Propensity

849

Planned Giving Propensity

250

Cash Capacity for Org in 12-mo. Period

$5,000-10,000

10000

10000

10000

Every donor…


Annual Giving Model


Visualize Your Database


Chart Your Ask Amounts


Summary

• Data Mining vs. Data Modeling• In-house vs. Outsourced Solutions• Risks and Benefits


Contact Information

• Chad Mitchell– Account Executive– Blackbaud Analytics– (800) 468-8996 x.5854 Toll-free– (404) 888-9353 Direct– (843) 216-6100 Fax– [email protected]– www.blackbaud.com

Documents

A N A L Y T I C S E R V I C E S Expanding the Scope of Prospect Research: Data Mining and Data Modeling Chad Mitchell Blackbaud Analytics September 2,