Dicing the Database Applying Analytics to Optimize the Advancement Model Association of Fundraising Professionals September 13, 2007

Page 1 Contents Background Master Segmentation Model Solution to New Challenges Pilot Closing

Page 2 Constituents 723,197 Individuals  408,569 Graduates  249,566 Friends  65,062 Attended 278,185 Current and Lapsed Donors Development Personnel College/School/Unit (CSUs)  23 units have a Development function  121 staff  49 field officers Central Development Office  104 Staff  18 Field Officers  1-3 Analytics Function Overview of UT

Page 3 Scope of the Analytics Function Portfolio Optimization Project and Program Prospecting Geography Prioritization Solicitation Prioritization Analytics Gift History Wealth Screening Involvement Data Student Activity Data Services Data Programs Planned Gifts Major Gifts Special Giving Annual Giving Stewardship and Events 3 rd Party Interest Data Biographical Partial Implementation Online Activity Data

Page Focus Identify Next Generation of Major Gift Prospects Segment the University’s donor base of 492,000 households of alumni and friends Evolve prospect identification from a one-dimensional score to two a dimensional matrix model Prioritize willingness to give and ability to give to identify the most attractive prospects More effectively allocate resources and target giving opportunities to prospect donors 2007 Focus Apply Analytics Tools to All Decision-Making Maintain security and confidentiality of data and results Empower the user by providing critical data to the desktop to improve decision- making Develop repeatable solutions that can be deployed across the University Enhance the tool sets to find better techniques and technology to deploy analytics Use of Analytics is Growing in its Deployment Across Development

Page 5 Analytics is a Tool to Help Decision-Makers Increase Their Odds of Success Philanthropic Opportunity Known Relationships Unknown Relationships “Local Knowledge Trumps Analytics Data” Identify the Prospects with the Most Likely Potential

Page 6 The Matrix Model is the Core of our Segmentation and Reporting Giving Index* Capacity Index* Assign every household a giving score and capacity score Assists us to allocate our resources to optimize results Assign every household a giving score and capacity score Assists us to allocate our resources to optimize results

Page 7 Giving Index (Y-Axis) Giving index developed using RFM framework giving index  Largest lifetime gift  Number of lifetime gifts  Recent factor for past seven years weighted towards most recent gift. Removed giving to Athletics and KUT Percentile score (1-100%) Household scoring Capacity Index (X-Axis) Capacity Index developed using statistical analysis on third-party wealth data  Home value (Property Screening)  Household income  Net worth (Securities Screening) Used Research team to validate and update data on key donors Normalized data into a percentile score (1-100%) to more easily compare households Matrix Model is Built with Two Unique Sets of Data that is Readily Available in our Database

Page 8 Defined Segments for the Entire Database and Identified High Potential Areas Potential Major Gifts Prospects Potential Planned Giving Prospects Potential High Net Worth Prospects C 3,526 D 1,840 Giving Index* Capacity Index* 99% 90% A 388 G 6,485 E 18,663 H 14,136 J 35,713 F 8,410 I 11,030 K 74,476 L 62,870 M 90,590 50% B 8,608

Page 9 Combining the Segmentation Model with Other Data Elements in the Database to Identify Opportunities for Improvement Biographical Data - Good address, phone number, employer name, etc. Geography - State, MSA, Zip Code, etc. Organization – College, School, Unit, etc. Membership status - Active, Expired, Renewal, etc. Other Unlimited Views of the DataSegmentation Model

Page 10 Applying the Segmentation Model to Identify the Growth of the Most Attractive Segments 492,837 Households 158,801 Unscored 334,036 Scored 320,131 Outside of Model 13,905 Model 376 A 8,383 B 3,312 C 1,834 D VIP Database in ,837 Households 156,102 Unscored 336,735 Scored 322,373 Outside of Model 14,362 Model 388 A 8,608 B 3,526 C 1,840 D VIP Database in 2006

Page 11 Applying the Segmentation Model By MSA to Allocate our Regional Gift Officers 80%+ in Giving and 80%+ in Capacity Households

Page 12 Apply the Segmentation Model to Identify the Improvement in the Management of Gift Officer Portfolios 5,711 Households 180 Unscored 5,531 Scored 3,278 Model 2,253 Out of Model 299 A 1,644 B 1,233 C 102 D Prospect Management in ,315 Households 660 Unscored 4,655 Scored 2,360 Model 2,295 Out of Model 193 A 999 B 1,107 C 61 D Prospect Management in 2005

Page 13 Applying the Segmentation Model to a CSU Pool of Prospects to Optimize Their Portfolios

Page 14 Applying the Segmentation Model to an MSA to Identify Potential Prospects

Page 15 Our Goal is to Find Analytics Approach to Meet these Challenges Emerging Challenges That Do not Fit the Traditional CSU Structure Increasing Development Needs for Non-alumni Units Growth of UT’s ServicesGenerational and Competitive Changes in the Marketplace Alumni Not Responding to Traditional Loyalty Appeals

Page 16 Providing services to campus and the community Integrating with the educational mission of the University Attract a set of constituents that overlap with alumni Attract a new set of constituents that did not attend the university Non alumni units developing their annual giving and major gift programs Non-Alumni Units Are Growing in Size and Relevance at UT

Page 17 University View Pride and loyalty perspective will continue to be a factor for alumni Constituent View Understanding interests and behaviors will be increasingly important Segments of Alumni are Not Responding to the Traditional Appeals to Support the Colleges or Schools Relative to Previous Generations X Y Z Capacity Giving Affinity X Y Z Capacity Giving CSU Each View Complements the Other

Page 18 Solution: Apply Several Statistical Analysis Techniques to Develop Scoring Systems to Develop Alternative View to CSU Principal Component Analysis 54 giving designations Demographic data Affinity data Etc. Regression Analysis Donor regression models developed from giving data Non-donor models developed from demographic and interest data Donor Model(s) Non- Donor Model(s) Scoring and Prioritization Apply regression models to database to score donors and non-donors Sort database to evaluate and prioritize best candidates

Page 19 Our First Donor Model Develops Sample Groups of Potentially Like Minded Donors that Give to Similar Causes Orange Bloods +President +Graduate School +Texas Exes +Athletics +UT Press +Blanton +Fine Arts +Performing Arts +Financial Aid Arts & Culture +Blanton +HRC +KUT -Athletics -Football -Business -Texas Exes Graduate Education +Jackson School +Liberal Arts +Natural Sciences +Student Affairs +Graduate School -Blanton -Performing Arts -Football -KUT -Athletics Progressive Investors +IC2 +UT Elementary +Business -Undergraduate Program -Neighborhood Longhorns -Center for American History Education & Well Being +Health Center +Police Department +College of Education +UT Elementary School +Nursing +Social Work Identifies Potential to Find New Donors from Similar Gift Designations

Page 20 Non-Donor Model will Attempt to Identify Data Elements that Can be Used as Predictor Variables for Future Donors of Each Grouping Biographical Data Geography Organization Involvement or Activity (i.e. Events, Volunter, etc.) Affinity and Interest Online Behavior Other Data Available on All Non-Donors Education & Well Being +Health Center +Police Department +College of Education +UT Elementary School +Nursing +Social Work What data elements can we use to potentially identify attractive non-donors?

Page 21 and Online Behavior is a Growing Source of Constituent Data for Database Analytics Science and Technology Texas Exes Campus News Society Education Development Interest Category

Page 22  It is not about the models  Its about understanding your constituents  Understand the source and quality of your data  Wealth of data available across the institution  Security and accessibility  Analytics is a high risk area  Start small and slowly  The demand for understanding data can be overwhelming In Closing, Lessons Learned from the Past Three Years