CPRA Summer Conference 2016 –

Zen and the HeART (error) of Data Mining/Predictive Modeling
& other topics
Friday, June 17, 2016, 9:00 am -3:00 pm
Colorado School of Mines, Golden, CO

CFRE 2016 approved logoCFRE approval, 5 points

Morning:

Introduction Workshop: Developing Simple Yet Powerful Predictive Models using Microsoft Excel (Predictive Simple Characteristic Modeling)
For those interested in data mining and predictive modeling but confused with all the statistical jargon, this session will demonstrate how the beginner can start with a tool they already have on their desktop: Microsoft Excel. The participant will come away with a better understanding of the predictive modeling process employing descriptive statistics and simple linear regression. A working knowledge of Excel would be beneficial but not necessary.

Presenter: David Robertson Jr., Director of Operations Research, Syracuse University

Contact Reports: What are they, why are they important, and what should be included (or not)??
Ever read a contact report that says Had lunch with donor and wish there was more? Learn about how the University of Colorado’s Advancement Strategy Department created contact report policies, procedures, and guidelines, and implemented those through outreach and training to improve the quality and consistency of contact reports entered by fundraising staff.

Presenter:Sarah Wuorinen, Prospect Research Manager, Advancement Strategy, University of Colorado

Eye on the Prize: Portfolio Analysis for Focusing Major Gift Efforts
Faced with multiple prospects to potentially engage and limited staff time and travel budgets to do so, Colorado State University employed a four-quadrant portfolio analysis approach (based on estimated gift capacity and affinity measures developed in-house) to focus development officers in their major gift efforts. This framework has been captured in automated tools operating within a larger prospect/suspect management system. Featured is a discussion of how prospect development and front-line development teams employ these tools in collaboration.

Presenters: Eric Patterson, Associate Director, Prospect Analytics, Colorado State University & Sue Lenthe, Associate Director, Prospect Management, Colorado State University

Afternoon:

Advanced Workshop: Multiple Characteristic Modeling – Analysis of Multiple Donor Characteristics in the Data (Predictive/Multiple Variable Modeling)
The second half of the workshop employs the simple analytics of part 1. We’ll build upon our understanding and formulate a multiple variable profiling tool leading to a true predictive model. The participant will come away with a better understanding of the predictive modeling process employing multiple regression techniques.?A working knowledge of Excel would be beneficial but not necessary.

Presenter: David Robertson Jr., Syracuse University

The event is being hosted on the beautiful campus of the Colorado School of Mines, in Golden, Colorado. We look forward to seeing you there!

 

 

 

About David Robertson, Jr.:
David is Director of Operations Research at Syracuse University, where he was hired in 2002. His area of expertise is in data mining, forecasting and predictive modeling. He continues to present statistical methods and modeling at APRA and CASE conferences throughout the US and Canada. David has a BS and MBA from Le Moyne College in Syracuse, NY where he teaches statistics (quantitative methods)?and business management strategy and social research methods (qualitative) as an adjunct professor in the Business and Sociology Departments. He is currently a doctoral student in the Social Science Ph.D. Program within the Maxwell School at Syracuse University. His research examines the unrecognized and informal charitable contributions of the underrepresented; focusing on the philanthropic activities of disenfranchised or marginalized members of society. Currently his doctoral research investigates New York State Mexican migrant farmworkers and their charitable contributions within their temporary micro-communities as well as their charitable contributions offered to their ancestral homeland. He utilizes both qualitative and quantitative approaches as well as visual ethnography.

For both research interests — the philanthropic working poor and philanthropic citizenship — he incorporates qualitative research, statistical modeling and data mining techniques to qualify and quantify his research findings. Through predictive models, he identifies statistically significant indicators of philanthropic behavior. His research investigates and brings to light the predictive nature of one’s philanthropic tendencies (or lack of) using indicators of human behavior and one’s ownership of community, building social capital.

About Sara Wuorinen:
Sara is the Prospect Research Manager in the University of Colorado?s Office of Advancement, where she has worked since 2014. Previously, she was a Development Research Analyst for the University of Colorado Foundation from 2008-2014, when Advancement services were moved from the Foundation into the University. Sara has been a member of CPRA since 2008, and a board member since 2013. She feels passionately about the field of prospect research and the impact it can have on the future of the non-profit industry. Sara holds a B.A. in History from the University of Michigan.

About Eric Patterson:
Eric serves as Associate Director of Prospect Analytics in Colorado State University?s Advancement division. His accomplishments include designing a portfolio analysis tool for supporting prospect management and developing a gift capacity index derived from multiple wealth indicators. Eric previously served as Prospect Research Coordinator, where he supported the successful pursuit of several seven-figure and six-figure gifts in partnering with front-line development officers. Eric holds a B.S. in computer science from Carnegie Mellon University and an M.A. in journalism from the University of Colorado.

About Sue Lenthe:
Sue serves as Associate Director of Prospect Management in Colorado State University’s Advancement division.