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, 2015

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

A N A L Y T I C S E R V I C E S 3 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

A N A L Y T I C S E R V I C E S 4 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

A N A L Y T I C S E R V I C E S 5 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

A N A L Y T I C S E R V I C E S 6 Why Use It? Classify donors & prospects by factors other than wealth (or major gift potential): –Lifestyle/life-stage –Affinity –Interests/behaviors –Cultural –Demographics –Psychographics

A N A L Y T I C S E R V I C E S 7 Go Beyond Capacity LIKELIHOOD CAPACITY Wealth Screening Results Annual Giving Major Giving Minimal Investment Cultivate

A N A L Y T I C S E R V I C E S 8 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

A N A L Y T I C S E R V I C E S 9 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

A N A L Y T I C S E R V I C E S 10 What Are My Options? Do It Yourself –Simple statistics – Data Mining –In-house Data Modeling Outsourcing –Advanced Data Modeling –Regression Analysis –Consulting

A N A L Y T I C S E R V I C E S 11 Simple Statistics What is simple? –Frequency distributions –Trend analysis –Segmentation analysis Tools –Existing Donor Management Application –Microsoft Excel or Access

A N A L Y T I C S E R V I C E S 12 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

A N A L Y T I C S E R V I C E S 13 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

A N A L Y T I C S E R V I C E S 14 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

A N A L Y T I C S E R V I C E S 15 Special Event Planning

A N A L Y T I C S E R V I C E S 16 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

A N A L Y T I C S E R V I C E S 17 Case Study – Higher Education University AUniversity B Two similar organizations with vastly different profiles

A N A L Y T I C S E R V I C E S 18 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

A N A L Y T I C S E R V I C E S 19 Types of Data Modeling Clustering Decision Trees (CHAID) Neural Networks Logistical Regression Probit Regression

A N A L Y T I C S E R V I C E S 20 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

A N A L Y T I C S E R V I C E S 21 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

A N A L Y T I C S E R V I C E S 22 Acceptable Risk Potentially rich data in your file Understanding the big picture Bringing focus to your development efforts

A N A L Y T I C S E R V I C E S 23 Levels of Information Individual Household ZIP + 4 Block ZIP Tip: start at smallest level possible - individual

A N A L Y T I C S E R V I C E S 24 Types of Data Types of Client Data –Demographic –Giving History –Activities/Relationships –Transactional –Attitudinal –Interests

A N A L Y T I C S E R V I C E S 25 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

A N A L Y T I C S E R V I C E S 26 Creating Variables Additive Dichotomous (yes/no) Continuous/quadratic Composite variables –State/city Missing data

A N A L Y T I C S E R V I C E S 27 Maximizing Your Data

A N A L Y T I C S E R V I C E S 28 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)

A N A L Y T I C S E R V I C E S 29 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%

A N A L Y T I C S E R V I C E S 30 Outsourcing – Why? Models specific to your donors and prospects Speed Cost Accuracy Consulting

A N A L Y T I C S E R V I C E S 31 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

A N A L Y T I C S E R V I C E S 32 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, Every donor…

A N A L Y T I C S E R V I C E S 33 Annual Giving Model

A N A L Y T I C S E R V I C E S 34 Visualize Your Database

A N A L Y T I C S E R V I C E S 35 Chart Your Ask Amounts

A N A L Y T I C S E R V I C E S 36 Summary Data Mining vs. Data Modeling In-house vs. Outsourced Solutions Risks and Benefits

A N A L Y T I C S E R V I C E S 37 Contact Information Chad Mitchell –Account Executive –Blackbaud Analytics –(800) x.5854 Toll-free –(404) Direct –(843) Fax –