TAAS 2021 Speakers
The theme for TAAS 2021 is “Theories of Philanthropy – Developing Sophisticated Analytics Programs to Better Understand & Predict Donor Behavior“. Speakers and topics have been selected that address this theme to create a program that focuses on both theory and practice. We look forward to the conversation!
Leveraging Relationships in University Fundraising: An Analysis of Faculty Advisee Groups and Alumni Networks
Fundraising is based on relationship building, yet university fundraisers often underuse data about alumni connections to faculty and to other alumni. Fundraising offices have not prioritized the collection of relationship data, and so despite its importance, it is often missing, hidden, or inaccessible. However, the discrepancy underscores an opportunity. This project used a combination of recommender systems, network graphing, and data visualization to help a university fundraising department discover and leverage its network of alumni and post-docs. The project resulted in two dashboards, one focused on faculty advisee groups and the other an alumni network map. The advisee group dashboard made it easy for fundraising analysts to see each faculty advisor’s alums and post-docs, compare the philanthropy of the groups, and strategize about potential fundraising initiatives based on the data. The network map was created using the results of a relationship recommender system, which suggested relations based on the shared campus experiences of alumni, such as living in the same dorm or participating in the same student club. The network dashboard helped identify previously unknown connections between engaged alumni and their unengaged peers. The fundraising department then used these relationships to create new strategies for connecting with alumni who had been difficult to engage. Together, the dashboards enabled the department to take advantage of relationship-based fundraising opportunities that would have otherwise been missed. The project demonstrated the value in treating fundraising not just as an art, but as a data-driven science.
Kelly Douglas loves to advance fundraising efforts through data. She transitioned into analytics from prospect research and remains enthusiastic about both fields. She has worked for more than a decade at the California Institute of Technology and is currently a Senior Data Analyst in Prospect Analytics and Reporting there. She encourages data-driven strategies, builds reports and predictive models, leads data projects, and serves as a subject matter expert on the database and prospecting.
Conferences first sparked Kelly’s interest in the ways data can transform fundraising activity. A lifelong learner, she earned her Master’s degree in Data Science from the University of Wisconsin and has completed multiple data mining and predictive analytics certificates.
Kelly especially enjoys being part of a university community, whether traveling into the outdoors with students, listening to campus talks, or playing music in the concert band. She also appreciates being part of the prospect development community and has served on the Apra Awards Committee and the Board of Directors of Apra’s California chapter.
She’s a winner of Apra’s inaugural Data Science Dashboard Challenge and the #ResearchPride Scavenger Hunt. On weekends, she can often be found hiking mountain trails or volunteering with the dogs, cats, and bunnies of Pasadena Humane.
Jim Dries & Juan Garcia
Proposals of Hope: Optimism Versus Analytics
A multi-year evaluation of proposal data uncovers a tendency of gift officers to over invest their time, and resources on prospects with a low probability to donate. Analytics modeling and data analysis uncovered this tendency and provides a roadmap for how to improve the alignment of resources to address the challenge. Revising proposal strategies based on prospect segments creates opportunities to improve proposal effectiveness and align resources with the highest ROI opportunities.
Jim Dries is the CEO of piLYTIX, an Austin based artificial intelligence tech company that generates revenue-enhancing insights for users in several industries. Jim credits piLYTIX’s success to the company’s team of exceptional data scientists and a collaborative client base that strives to be difference makers for their organizations.
Jim’s 20-year career has spanned leadership roles in finance, product development, sales and marketing. Until recently, however, none of those roles intersected with the world of university fundraising. As piLYTIX began working with collegiate sports teams to improve the efficiency of their ticket sales organizations, several university clients inquired whether piLYTIX’s A.I. engine could be leveraged to drive similar efficiencies in their fundraising organizations. In 2019, The University of Texas – Austin became the first university to implement piLYTIX’s A.I. software to complement the work of its internal fundraising analytics team.
Jim is now on a mission to elevate the role of the analytics team in higher education fundraising. As fundraising organizations are called on to achieve greater financial targets than ever before with tightening budgets, Jim believes that smart, capable analytics leaders armed with cutting edge toolsets need to be seen as the driving force that guide every organization’s fundraising strategy.
Originally from Wisconsin, Jim has degrees from Yale University and the University of Chicago. Jim and his family live in Austin, TX, where piLYTIX is headquartered.
Juan is the Assistant Vice President for Advancement Strategy and Campaign Director for The University of Texas at Austin. His experience includes previous campaign and advancement leadership positions with Texas Athletics, Wake Forest University, and Arizona State University Foundation. His experience includes campaign planning and goal setting, case statement development, analytics modeling. business intelligence, and process optimization.
Prior to joining the development community, Juan spent eleven years as a management consultant with PriceWaterhouseCoopers, Arthur Andersen and BearingPoint. His client experience focused on strategy formulation, merger integration, revenue growth enhancement, and operational improvement projects.
His education includes a B.S. in Industrial Engineering from Purdue University and an MBA from The University of Texas at Austin.
Online Buzz Evolution Patterns and Crowdfunding Campaign Performance
Online crowdfunding (a type of fundraising where groups of individuals collectively contribute money to back different projects and endeavors through the internet) has proven to be a viable alternative for raising funds. Businesses, entrepreneurs and individuals have used this alternative fundraising channel to raise finances to support either their businesses, ideas, creative projects or personal goals. Despite the growing importance and popularity of crowdfunding, and the documented impact of online buzz in online crowdfunding performance, we are not clear on what types of evolution patterns (dynamics) online buzz exhibit during online crowdfunding campaigns and the relation between these buzz evolution patterns to crowdfunding campaign performance. In this study, I explore the buzz evolution patterns associated with online crowdfunding and establish a relationship between the identified buzz evolution patterns and crowdfunding campaign performance. I use functional data analysis (FDA) techniques to capture the evolution patterns and properties of online buzz and backing activity in each crowdfunding campaign. The exploratory analysis reveals interesting online buzz evolution patterns in crowdfunding campaigns. Further, the analysis shows a link between the buzz evolution patterns and backer activity. The results suggest that an increasing buzz intensity (rate of change of buzz per unit time) leads to an increasing backer rate. Comparatively, the model based on buzz intensity (evolution pattern) was better in predicting crowdfunding campaign outcome than the model based on the campaign organizer’s network size.
Onochie Fan-Osuala is an assistant professor of information technology (IT) in the College of Business and Economics at the University of Wisconsin, Whitewater. He holds an MS in Electrical Engineering from Florida International University, Miami and a Ph.D. in Business Administration with a concentration in information systems from the University of South Florida, Tampa. He is interested in using ideas from statistics, econometrics, and data mining to solve business problems cutting across the information systems, marketing, entrepreneurship, operations and supply chain domains especially in areas focusing on online platforms and marketplaces. Prior to joining academia, Onochie worked as a technology consultant. He has collaborated and consulted with industry on various projects including developing predictive analytics and software solutions.
Data Modernization: A Case Study in Improving Data Architecture for Advanced Analytics
It seems fitting that we associate our data stores with bodies of water or with warehouses. There is within both analogies the concept of substance moving from one place to another, at times in motion, and at others in rest. All the while, being held in reserve for some final consumption either as water in a watershed or as a product to a consumer. It is this final consumption that is often overlooked as we find ourselves immersed in data, hoarding it against some future need, without necessarily any conceptualization of what that need might be. Other times, we find ourselves confronted by a need without the necessary store to supply it, not realizing the deficit until having invested resources into the development of a solution that is forcibly incomplete. Adopting these analogies, this paper is a case study of one advancement office’s efforts to modernize their data architecture in a bid to develop advanced analytics solutions that are both automated and scalable. Detailing efforts undertaken over a 24-month period to develop and adopt cloud architectures for advancement data, this paper will extend the metaphor to include downstream consumption, arguing that much like the electric turbines on a river dam, our data stores are only as good as the insights they help generate.
John is the Senior Executive Director for Advancement Data Operations and Strategy, University of Texas at Austin. He has previously held positions in data analytics at UT Austin and in institutional advancement at the University of Illinois at Urbana-Champaign. In his current role, John oversees the reporting and analytics, gift processing, data quality, and data engineering teams.
John holds an MS in Library and Information Science, Data Curation, UIUC; an MA in French Literature, NYU; and a BA, University of Utah. He has served as an adjunct faculty lecturer at the University of Illinois at Urbana-Champaign’s iSchool, teaching courses in business analytics and database design. He has also served as a visiting lecturer in the Department of Economics at the University of Texas at Austin.
Klaus Mueller & Eric Papenhausen
Patterns of Philanthropy: Using Pattern Mining for Predictive Analysis in Advancement and Fund Raising
To support their academic mission universities and colleges have become increasingly dependent on raising capital via advancement channels. As these institutions compete for the attention and the funds of donors advanced data analysis is a key to success. We outline a methodology and tool that allows analysts to identify specific groups of donors which share sets of demographic, academic, and other features. This information can then be used to shape specific fund raising efforts and evaluate the expected profitability of these drives. Our paper details the use of our tool with a dataset we obtained via collaboration with a public university with over 50,000 undergraduate and graduate students and over 24,000 faculty and staff. The dataset has 168 attributes covering demographic and academic information as well as donations for 2,054 donors. Central to our tool is a pattern mining engine which looks for regions in this feature space that are occupied with similar donors who all respond in a similar way to a given target variable of interest, in this case the amount of the donations. We show that our automated pattern analysis can be highly effective in defining the specific characteristics of donors more likely to make philanthropic contributions to a university. These patterns were extracted without any manual tuning of parameters. An important component of our tool is also its visualization suite which is very effective in helping analysts to understand the statistics that underlie these patterns and make the findings more accountable and explainable. The visualizations are automatically produced, are highly interactive and are designed to appeal even to analysts without specific visualization skills.
Klaus Mueller received a PhD in computer science from The Ohio State University in 1998. He is President and CEO of Akai Kaeru LLC. He is also a professor in the Computer Science Department at Stony Brook University and a senior scientist at the Computational Science Initiative at Brookhaven National Lab. His current research interests are visualization, visual analytics, computational causality, data science, explainable AI, computational imaging, and high-performance computing. To date, he has authored more than 200 peer-reviewed journal and conference papers, which have been cited more than 10,500 times. Klaus co-founded Akai Kaeru to bring academic research into the real world to benefit decision makers in their everyday data-driven applications. He won the US National Science Foundation Early Career award in 2001, the SUNY Chancellor Award for Excellence in Scholarship and Creative Activity in 2011, and the Meritorious Service Certificate and the Golden Core Award of the IEEE Computer Society in 2016. In 2018 Klaus was inducted into the National Academy of Inventors. Klaus is a frequent speaker at international conferences, has organized or participated in 18 tutorials on various topics, chaired the IEEE Visualization Conference in 2009, and was the elected chair of the IEEE Technical Committee on Visualization and Computer Graphics (VGTC) from 2012-2015. He currently serves as the Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics and is a senior member of the IEEE. Please see https://www3.cs.stonybrook.edu/~mueller/ for more info on Klaus and https://akaikaeru.com/ for more info on Akai Kaeru LLC.
Eric Papenhausen is a co-founder and CTO at Akai Kaeru. He holds a MS and PhD degree in Computer Science, both from Stony Brook University. Eric has over 10 years of industry experience in Software Engineering at small and large companies alike. He has authored 20 papers in performance optimization and visual analytics that have over 100 citations to date. The focus of his PhD studies was primarily related to researching techniques for computational performance optimization. This included optimization case studies such as GPU accelerated CT reconstruction, GPU accelerated clustering, etc. as well as automatic and semi-automatic methods for performance optimization. At Akai Kaeru, Eric has been the chief architect and developer of Akai Kaeru’s pattern mining engine and visual analytics interface designed to uncover the driving forces underlying a complex data set. A key component of this software is causal inference. Eric has studied the field of causal inference from several angles — from inferring causal networks to estimating treatment effects. As part of his work at Akai Kaeru, Eric has developed several performance optimizations as well as algorithmic improvements to make these tools more tractable with real world data.
Marianne Pelletier & Gregory Duke
The Nimble Annual Giving Program: A Responsive Approach to Fundraising
Artificial Intelligence has disrupted all sectors of the fundraising industry, from changing fundraising methods to include text to give to just-in-time major gifts modeling. However, annual giving programs still tend to solicit on a set schedule instead of in response to constituent behaviors. That habit puts a solicitation into the hands of a prospect not at the time that the prospect is most likely to give but at a random time (and using a random method) in the prospect’s giving year.
This paper will explore methods for creating an annual giving program that meets the challenge set forth by Penelope Burk in her book, Donor Centered Fundraising, by exploring artificial intelligence and forecasting techniques that may improve annual giving results by responding with an ask at the time that a prospective donor is thinking about – and feeling good about – the nonprofit.
We hope to provide, in this paper, an avenue for making the shifts to a nimble annual giving program that speaks to both the data scientists and the annual giving staff at an organization. We hope to identify and interview nonprofits in education, healthcare, and cause or arts focused organizations who are using some of these tools and the results from doing so.
Marianne Pelletier has 30 years of experience in fundraising, with the majority in prospect research and prospecting. Her prospect research experience began when she was a research analyst for Harvard and Lesley Universities. She later served as a department director for Carnegie Mellon University and Cornell University.
Pelletier’s career also includes running an annual giving program with average increased revenues of 27% per year and providing software consulting through the Datatel Corporation, teaching clients both how to use their new software and assisting them with better analysis and more efficient processing.
Pelletier is a graduate of Rockford University, and earned her MBA at Southern New Hampshire University. Her recent workbook, Building Your Analytics Shop: A Workbook for Nonprofits, was a finalist for the Terry McAdam Award in 2016.
Dr. Gregory E. Duke has been in the nonprofit sector for over 20 years, working in fundraising organizations in the United States and the United Kingdom, primarily in database management and prospect research.
Duke helps Raiser’s Edge clients to optimize their database by implementing data clean-up techniques and creating reporting structures, including dashboards and SQL queries. He also facilitates data imports into Raiser’s Edge and database administration.
Duke has worked at the University of Oxford, Niagara University, Florida International University, and the Rochester Institute of Technology. He has lectured at several APRA International and regional conferences, and previously taught a fundraising course at Niagara University.
Duke earned a D.Phil in modern history from Jesus College, University of Oxford.
Does Being Engaged Benefit the Bottom Line?
Community Engagement and its Effects on Fundraising Performance
Many institutions of higher education have a tradition of community engagement (Roper & Hirth, 2010). From service-learning (Bringle & Hatcher, 1996; Butin, 2010; Jacoby, 1996), community-engaged scholarship (Jordan, Wong, Jungnickel, Joosten, Leugers, & Shields, 2009; Saltmarsh, Giles, Ward, & Bugilone, 2009), to co-curricular engagement (Bergen-Cico & Viscomi, 2012; Keen & Hall, 2009), higher education has a long-standing history of engaging with communities, both locally and globally. Numerous studies have been conducted on the effects of service-learning on individuals, with a few studies following small groups of alumni; however, very little is known about how being a community-engaged institution affects the fundraising performance of colleges and universities.
Engaged institutions of higher education may pursue the Carnegie Foundation’s Elective Community Engagement Classification to illustrate the institution’s commitment to community engagement by meeting various standards for engagement including institutional buy-in, assessment, faculty and staff support, and other various metrics. Utilizing the Carnegie classification as a proxy for effective community engagement, this study explores the fundraising performance of classified and non-classified institutions to see if institutionalized community engagement is a potential predictor for stronger fundraising performance.
Donors have many options when choosing to make their philanthropic gifts, and many are concerned with making an impact with their philanthropic contributions. Therefore, it is worth investigating if the charitable acts of an institution of higher education (i.e., service-learning, community engagement, community-engaged research) have a potential effect on the appeal to donors. The primary questions asked within this paper focus on whether or not an institutional commitment to community engagement potentially leads to better performance within institutional advancement:
- What effect does the Carnegie Elective Classification for Community Engagement have on fundraising performance?
- Do academic institutions that have stronger commitments to community engagement outperform academics institutions that do not?
Utilizing a quantitative approach, the fundraising performance of public universities, both classified and non-classified, will be examined over three years to see if engaged institutions have better fundraising performance than those that are not classified. Preliminary analysis indicates that community engagement classification is indeed a significant predictor of raising institutional funds.
Colton C. Strawser is an independent researcher and nonprofit consultant as well as a Visiting Research Fellow with the Mulvaney Center for Community, Awareness, and Social Action at the University of San Diego. He has held various positions in the nonprofit sector including director of marketing/fundraising, director of development, and executive director.
Strawser’s research interests include community engagement within higher education, the community leadership roles of community foundations, and effective pedagogy within the disciplines of nonprofit management and philanthropic studies. His mission is to empower organizations to create change through his teaching, scholarship, and engagement.
Strawser holds a Bachelor of Arts in Philanthropic Studies from the Indiana University Lilly Family School of Philanthropy and master’s degrees in Higher Education Administration and Nonprofit Management & Philanthropy from Bay Path University. He is a PhD Candidate at the University of San Diego where his dissertation was funded by the Ford Foundation. He is both a Certified Fund Raising Executive (CFRE) and Certified Nonprofit Professional (CNP).