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Expanding the Scope of Prospect Research: Data Mining and Data Modeling. Chad Mitchell Blackbaud Analytics November 27, 2014. Game Plan. Definitions, Overview and Why? Data Mining vs. Data Modeling In-house Solutions Outsourcing Options Examples and Cast Studies Benefits and Risks

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Expanding the scope of prospect research data mining and data modeling

Expanding the Scope of Prospect Research:Data Mining and Data Modeling

Chad Mitchell

Blackbaud Analytics

November 27, 2014

Game plan

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

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



  • 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

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

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


Wealth Screening



Go Beyond Capacity

Annual Giving

Major Giving

Minimal Investment


Benefits of data modeling

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

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

What Are My Options?

  • Do It Yourself

    • Simple statistics – Data Mining

    • In-house Data Modeling

  • Outsourcing

    • Advanced Data Modeling

    • Regression Analysis

    • Consulting

Simple statistics

Simple Statistics

  • What is simple?

    • Frequency distributions

    • Trend analysis

    • Segmentation analysis

  • Tools

    • Existing Donor Management Application

    • Microsoft Excel or Access

Simple data mining examples

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

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

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

Special Event Planning

Analyze every area of giving

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

Case Study – Higher Education

Two similar organizations with vastly different profiles

University A

University B

Data modeling how do you do it

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

Types of Data Modeling

  • Clustering

  • Decision Trees (CHAID)

  • Neural Networks

  • Logistical Regression

  • Probit Regression

How to continued

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

Yes, There Are Risks

  • Bad or misleading data

  • Off the shelf modeling programs

  • Time intensive

  • Test, test, test

  • Applying Generic models


Acceptable risk

Acceptable Risk

  • Potentially rich data in your file

  • Understanding the big picture

  • Bringing focus to your development efforts

Levels of information

Levels of Information

  • Individual

  • Household

  • ZIP + 4

  • Block

  • ZIP

Tip: start at smallest level possible - individual

Types of data

Types of Data

  • Types of Client Data

    • Demographic

    • Giving History

    • Activities/Relationships

    • Transactional

    • Attitudinal

    • Interests

Types of data1

Sources of External Data


Single source databases - credit

Consumer transactional

Aggregated (avoid aggregated age)







KnowledgeBase Marketing

List Brokers

Types of Data

Creating variables

Creating Variables

  • Additive

  • Dichotomous (yes/no)

  • Continuous/quadratic

  • Composite variables

    • State/city

  • Missing data

Maximizing your data

Maximizing Your Data

Blending data into models

Appended Data

Client Data

Determine best candidate variables

for modeling process; create new

Composite and dummy variables

Identify attributes with the

greatest explanatory value;

select and weigh data in

unique algorithm

Blending Data into Models

Final Unique


Identify best

models and test


Case study family human services


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


Contributions = 398%

Case Study – Family / Human Services

Outsourcing why

Outsourcing – Why?

  • Models specific to your donors and prospects

  • Speed

  • Cost

  • Accuracy

  • Consulting

Vendor qualification

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


Major Giving Propensity


Planned Giving Propensity


Cash Capacity for Org in 12-mo. Period








Outsourcing Examples

Every donor…

Annual giving model

Annual Giving Model

Visualize your database

Visualize Your Database

Chart your ask amounts

Chart Your Ask Amounts



  • Data Mining vs. Data Modeling

  • In-house vs. Outsourced Solutions

  • Risks and Benefits

Contact information

Contact Information

  • Chad Mitchell

    • Account Executive

    • Blackbaud Analytics

    • (800) 468-8996 x.5854 Toll-free

    • (404) 888-9353 Direct

    • (843) 216-6100 Fax

    • chad.mitchell@blackbaud.com

    • www.blackbaud.com

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