Amid rapid growth in data complexity and technology advancement, many organizations still struggle to manage, understand, and measurably improve their legacy and evolving data ecosystems. In this hands-on data lab, you will learn how to build flexible data enrichment models, reusable tools and repeatable processes to 1) establish a constituent data integrity index/baseline 2) explore, visualize and measure constituent data integrity 3) identify and prioritize opportunities to improve your overall data quality in systematic and sustainable ways towards insights, equity and efficiency.

Personalization matters, and in today's world of AI-driven technology, truly personalizing the donor experience is possible for any size organization. Join this session to see how new technology can help you change your outreach from "send everything to everyone" to producing a truly tailored experience for each donor. Hear key metrics from millions of communications about the power of personalization and how it can more than triple open rates. And hear how St. John's University transformed engagement with new technology that allows for personalization at scale. It's time to do donor engagement smarter, and if you're ready to ignite the passion of individual supporters using new tactics, this session is for you.

I will summarize some of my research findings about bequests and I will explain why I utilized the Panel Study of Income Dynamics (PSID) publicly available dataset. Specifically, I will discuss my research comparing Caucasians and African Americans on their self-rated importance of leaving a bequest to family, charity, and religion, possible reasons for the bequest gap, and what opportunities this presents for education and service to an underserved community. I will wrap up with my study that explores whether lifetime donors to different types of charities also differ in the importance they place on leaving a charitable bequest. As a whole, individuals give more during life, through annual and major gifts, than they do at death. Many of these donors are an untapped resource for planned gifts, some more than others, and this data about which donor types are most likely to be interested in a charitable bequest could inform our conversations about philanthropy.

Most Prospect Management models rely on the gift cycle to categorize prospects. But is this the most accurate way to describe a prospect's philanthropic engagement with the institution? Does a prospect being cultivated for their first major gift really fall in the same category as a seasoned donor being cultivated for their 10th? The Prospect Development Team at Temple University decided to reframe the way they code and report on prospects based on simple measurements on the donors' records. This enabled the team to engage with the fundraisers they partner with to uncover new opportunities and allowed fundraisers to approach their portfolios in a more strategic way.

RFM analysis is a great first step to analyzing and grouping your donors. By summarizing their giving history and ranking by Recency, Frequency, and Monetary values, you can cluster donors into smaller comparable groups. This can help with identifying possible future prospects, searching for a target donor audience, and allow for more personal communications to each group.

All organizations are striving for continuous improvement, and getting feedback from your stakeholders is essential to that goal. One of the best ways to get that feedback is by surveying your constituents - students, families, employees, alumni and/or donors. Learn how to conduct your own survey research, even on a modest (or non-existent!) budget, and collect actionable data to help you improve your organization.

Across industries and in our personal lives, people are exploring the possibilities of generative Artificial Intelligence. In this brave new world, organizations are often independently developing policies around the use of tools like ChatGPT and Bard. Hear about institutional approaches; what peers wish they had known; and outcomes of early projects whether they were successful or not.

Much of the conversation about improving predictive models focuses on finding the optimal model technology, such as linear regression, logistics regression, classification trees, etc. We argue that there should be just as much focus on Exploratory Data Analysis (EDA) techniques by exploring, diagnosing and transforming the data - before, during and after the modeling process. We will also demonstrate how to apply different Exploratory Data Analysis (EDA) techniques to find optimal transformation and identify outliers.