Journal of Advancement Analytics

Volume 4, 2024

Articles

Uncovering Actionable Insights for Customer Service Improvement: A Text-Mining Approach to Identify Key Themes
by MD Rezwan Islam and Tim Boyd

The Power of Insights: Leveraging Data Analytics to Drive Business Value and Performance
by Gokul Murugesan

Adopt DataOps to Support Development of Sophisticated Analytics Programs
by Michael Tzaperas

Prospect Management and Data Science: Partnering to build an Efficient Horizon for Fundraising
by Ruth Giles and Marianne M. Pelletier

Deployment and Integration of Machine Learning Methods with Continuous Integration/Continuous Delivery
by Drew C. Wham

Utilizing Artificial Intelligence and Machine Learning to Determine a Prediction Analytics Process for Determining Foundation Giving Gift Capacity
by Jonelle Bradshaw De Hernandez and Debra Sabatini Dwyer

Volume 3, 2023

Articles

Editor’s Notes: The Democratization of Analytics: How the Confluence of Tools, Data Literacy, and Organizational Maturity are Changing the Analytics Landscape
by John Gough

Optimizing Giving Day: A Case Study in Using Machine Learning for a Constituency-Wide Campaign
by Marianne M. Pelletier

Decentralization of Data: An Architectural Perspective
by Vijay Arora

Analysis of Alumni Giving Based on Involvement in Different Student Activity Types
by Michael Speight

Take Me Out to the Fundraising Game: What Fundraising Analytics and Analysts Learn from Sports Analysis
by Gregory E. Duke

Volume 2, 2022

Articles

Leveraging Relationships in University Fundraising: An Analysis of Faculty Advisee Groups and Alumni Networks
by Kelly Douglas

Online Buzz Evolution Patterns and Crowdfunding Campaign Performance
by Onochie Fan-Osuala

Does Being Engaged Benefit the Bottom Line? Community Engagement and its Effects on Fundraising Performance at Public Institutions of Higher Education
by Colton Strawser

Proposals of Hope: Evaluating the Investment of Time on Proposal Wins versus Proposal Losses
by Juan Garcia and Jim Dries

Data Modernization: A Case Study in Improving Data Architecture for Advanced Analytics
by John Gough

The Nimble Annual Giving Program: A Responsive Approach to Fundraising
by Marianne M. Pelletier and Gregory E. Duke

Patterns of Philanthropy: Using Pattern Mining for Predictive Analysis in Advancement and Fundraising
by Klaus Mueller and Eric Papenhausen

Volume 1, 2021

Articles

Data Science for Fundraising: A review of Analytics in Fundraising
by Ashutosh R. Nandeshwar

Predicting Capacity with Machine Learning
by David Schemitsch

When XGBoost Tree Meets Humanity: An Exploration of Implementing New Machine Learning Techniques into Everyday Fundraising Practice
by Marianne M. Pelletier

Leveraging Insights from Other Industries to Accelerate Data-Driven Success in University Fundraising
by Jim Dries and Hendrix Kits Van Heyningen

2020 Vision: Observations on How Artificial Intelligence is Disrupting the Business of Philanthropy
by Melissa Cox

Hidden Voices: Using Social Listening to Uncover Audience Insights and Boost Advancement
by Amanda K. Jeppson and Rebecca C. Stapley

TAAS provides a thoughtful, grass-roots, supplement to the already existent programming of professional organizations in the advancement analytics space. Providing a venue for in-depth discussion and topical exploration, the symposium affords an advanced perspective on industry trends, systemic problems, and methodological solutions.

To learn more about how you can become involved in TAAS please contact us at TAAS@austin.utexas.edu.

Leveraging AI in Fundraising – Opportunities and Challenges

The Texas Advancement Analytics Symposium (TAAS) invites academics, industry professionals, AI experts, philanthropy leaders, and analytics experts to contribute their insights and research on the burgeoning field of Artificial Intelligence in fundraising. As AI technologies are on the verge of revolutionizing the way we approach philanthropy and donor engagement, we seek to understand the use cases, potential benefits, risks, and ethical considerations associated with wide-scale adoption.

Topics of Interest

We are seeking original, high-quality submissions on a range of topics related to AI in fundraising, including but not limited to:

• Guidelines for Implementing AI in Fundraising: Best practices, frameworks, and standards for integrating AI into fundraising strategies.
• Early Adoption of AI in Philanthropy: Case studies, success stories, and lessons learned from early adopters of AI in the fundraising sector.
• Practical Use Cases of AI in Fundraising: Innovative applications of AI that have transformed donor engagement, prospect research, campaign strategies, and more.
• Infrastructure Requirements for AI in Fundraising: Essential technological and organizational infrastructure to support the successful implementation of AI.
• Defining AI in the Context of Fundraising: Clarification of terms, concepts, and the scope of AI technologies applicable to fundraising.
• Enhancing Efficiency through AI: How AI can streamline operations, personalize donor interactions, and optimize fundraising campaigns for better outcomes.
• Navigating Privacy and Data Loss Concerns: Strategies to address data privacy, security challenges, and mitigate risks associated with data handling in AI applications.
• Ethical Considerations in AI-Driven Fundraising: Ethical frameworks, accountability, and maintaining donor trust in the age of AI.
• Fostering a Self-Service Culture: Empowering donors and stakeholders with AI-driven tools and platforms that promote engagement and self-service.
• Vendor vs. DIY AI Applications in Fundraising: Comparative analyses of off-the-shelf AI solutions versus custom-built AI applications tailored to specific fundraising needs.

The Texas Advancement Analytics Symposium invites you to consider these questions and to submit paper proposals in response to the above stated theme. Proposals are due no later than Friday, May 31st, 2024. Accepted submissions will be presented at the virtual symposium in August of 2024 with final papers published in the Journal of Advancement Analytics (JAA), Volume V. We welcome proposals that address both theoretical and practical applications of analytics in advancement, including case studies.

Submission Criteria

Proposals should be submitted as a .DOCX file. They should contain:

1. An abstract between 250-500 words in length
2. A paper outline 1-2 pages in length

For questions on potential proposal topics and for submission guidance please reach out to TAAS@austin.utexas.edu

Evaluation Criteria

Abstracts will be evaluated based on the following criteria:

• The originality of ideas/approach and level of innovation
• Relevance for advancement analytics and the defined theme
• Presentation: coherence of clarity of structure and thought
• Contribution to advancement analytics theory/solution building