1. David Lamb, Senior Consultant, Target Analytics DATABASE SCREENING

2. Why Warren Buffet is not a prospect for you! Your best prospects are already on your own database Corollary: Just because someone is wealthy doesn’t mean he’ll give anything to you Database screening provides a way to filter your database Who is more likely to give Who is more capable of giving Two basic approaches Statistical modeling List matching

3. Strange History Of Modeling Early openness to the technology – late 80s The disappointment factor Development staff often misunderstood the purpose of data mining “These people can’t give” “These people won’t give” “Who are these people?” New improvements in wealth screening resulted in disillusionment with modeling in early 90s

4. Modeling Reconsidered Modeling addresses the “interest” and “linkage” pieces of the major gift puzzle Threw out the baby with the bath water?

5. A Mathematical Way To Predict Behavior Annual giving Planned giving  Bequests  Annuities  Trusts Major giving Gift size Patient response Mail response Phone donors Mail donors Electronic donors

6. Method Behind The Model Most donors have certain things in common Minimum requirements for a model Data quality Data quantity Collection over time Types of models RFM analysis Home grown models Vendor supplied models Giving pattern Age Participation Major Degree Location Volunteer Income Credit use # of children in the home Constituent type Real estate value

7. Modeling Traps Not every variable that seems predictive is Some variables with correlations may not be “weighty” enough to influence a person’s score Endogenous variables are caused by the behavior you’re trying to predict You collect email addresses on those who are closest to you Presence of an email address is correlated with giving Not predictive!

8. Data Quantity What size gift is “major” Must have at least 200 examples of gifts in the last year at a particular level for valid statistics Don’t include gifts from corps or founds Gift LevelGift Count% of total Cumulative CountCumulative % 0 Dollars109,13590.85 109,135 90.85 1-49 Dollars2,4972.08111,63292.93 50-99 Dollars1,9021.58113,53494.52 100-249 Dollars3,8673.22117,40197.73 250-499 Dollars1,1530.96118,55498.69 500-999 Dollars7280.61119,28299.30 1000-2499 Dollars5820.48119,86499.79 2500-4999 Dollars1130.09119,97799.88 5000-9999 Dollars720.06120,04999.94 10000+ Dollars730.06120,122100.00 One year gift table

9. RFM Analysis Recency – when was the most recent gift? Score 0 if more than 3 years ago Score 1 if 3 years ago Score 2 if 2 years ago Score 3 if 1 year ago or less Frequency – how consistently has the donor given? Score 0 if none of the last three years Score 1 if only one of the last three years Score 2 if only two of the last three years Score 3 if each of the last three years Monetary Value (must be customized) Score 0 of largest gift is $0 Score 1 if largest gift is $1-$999 Score 2 if largest gift is $1,000 – $4,999 Score 3 if largest gift is >= $5,000

10. RFM Analysis If a prospect scores >= 6 Top priority for additional research to estimate capacity Consider the person a high likelihood prospect If a prospect scores 3 – 5 Second priority for research to estimate capacity Consider the person a moderate likelihood prospect If a prospect scores 0-2 Do not do additional research unless specific indicators come to light Consider the person a low likelihood prospect

11. Home Grown Model Materials Software like SPSS or SAS Statistical education Data sources or enhancements if you plan to use info beyond your database Age overlay Address updates Other datasets (census data, marketing data)

12. Home Grown Models: Frequency Distribution Major Donors Variable Entire Population % yes% no% yes% no 1189Income > $100,000397 1486Current or past parent1585 2575Live within 50 miles8317 3169Home value > $500,0001486 4060Age > 503763 5941Attended >= 3 events2278 6634Alumni7030 8416Gave in each of the last three years991 955Credit is in satisfactory status9010

13. Frequency Distribution Results Your best major gift prospects Close calls Age? Alumni? Satisfactory credit? Yes = 1, No = 0 Have incomes > $100,000 1 Live more than 50 miles from your institution 0 Have home values > $500,000 1 Attended >=3 events 1 Gave in each of the last three years 1 4

14. Problems With Frequency Distribution Not all variables have equal weight Major Donors Variable Entire Population % yes% no% yes% no 1189Income > $100,000397 1486Current or past parent1585 2575Live within 50 miles8317 3169Home value > $500,0001486 4060Age > 503763 5941Attended >= 3 events2278 6634Alumni7030 8416Gave in each of the last three years991 955Credit is in satisfactory status9010

15. More Statistical Power Must run correlations between your dependent variable (major giving) and all available independent variables Regression analysis allows you to compare strength of correlation of the variables in relation to each other Weighted correlations yield a score for each person Statistical package like SPSS or SAS will facilitate calculations and reporting

16. Vendor Statistics – 3 Options Generic Prescriptive Custom

17. Generic Model Statistical profile of the entire country Aggregate data based on many datasets Not specific to any one organization Geo-demographic data is in this category Claritas Prism Clusters  Gold Coast  Blueblood Estates  Shotguns & Pickups Equifax Niches  Chic Society  Diamonds to Go  Oodles of Offspring

18. Prescriptive Model Hybrid of generic and a custom model Includes standard variables Includes donor-specific giving data Provides a more targeted analysis than generic for who gives to an organization like yours

19. Custom Model Constructs a profile of giving behavior unique to your organization Examines the people in your database who have done what you’re trying to predict Capability to contribute Likelihood to make a gift Compares those people to the ones who did not behave the same way.

20. List Matching – aka Wealth Screening An automated process that matches the names on your database to those on other databases Public company insiders Private company owners & officers Real estate Biographical sources Donor lists Any list in electronic form Simple minded, but fast Yields the same kind of specific research that human researchers seek to find Information returned requires verification

21. It’s Not A Perfect World Matching issues False matches Things you can find easily are not picked up Sources of matching error Source data are flawed and incomplete Source data are chaotic Programming issues are not trivial Modeling issues Good at describing groups, not individuals Endogenaity Sample size

22. Comparing the approaches ResultsPer Record Cost Emphasis ModelingA score or other indicator about every record on the database Lower Prospect Identification Inclination & Linkage List matching Specific information about a few very capable prospects Higher Prospect qualification Capacity

23. What’s Right for Me? You should do list matching if: You have a well established major gift operation Your constituents are wealthy You are located in the midst of wealth You need to qualify people for gifts of at least $25,000 You should do modeling if: You want to segment your entire database Your major gift operation is less developed Your constituency is unlikely to be in lists You need to improve your annual fund strategy You need to improve your planned gift strategy

24. Best Of Both Worlds Ideal approach is to pre-screen the database with one method, then go deeper with another Start with a model Do list matching with records that score well On a pre-screened database, 1 in 10 may end up looking like major gift prospects. If you need 4,000 major gift prospects, screen 40,000 constituents