The phenomenal growth of social media, mobile applications, sensor-based technologies and the Internet of Things is generating a flood of  “Big Data” and disrupting our world in many ways. Simultaneously, we are seeing many interesting developments in machine learning and Artificial Intelligence  (AI) technologies and methods. In this talk, Dr. Ram will examine the paradigm shift caused by recent developments in AI and Big Data and ways to harness their power to create a smarter world. Using examples from health care, smart cities, education, and businesses in general, Ram will highlight challenges and research opportunities for developing organizations of the future.

Dell Medical School at the University of Texas at Austin was established in 2013 and accepted its first cohort of students in 2016. It has just recently begun seeing patients in earnest. Without an alumni base or patients to draw for prospecting, we had deployed an out-of-the-box solution using a two-step factor-cluster analysis to finding prospects for philanthropic support. This session will first present a problem statement, fields involved, Factor Analysis solutions and interpretations, graphs on clustering, and some examples of Python codes. At the end of the session, we will discuss challenges, pitfalls, and strategies to implement on the ground.

This topic explores how to tame the annual giving fundraising fury with a more data-driven approach and better data handling. It covers four elements of the annual giving data cycle and best practices in using donor data assets: from analysis, through segmentation and personalization, to better data leverage and automation. It provides the most impactful highlights of data usage in Annual Giving at McGill University Advancement, applied to various cohorts, throughout different solicitation channels and programs.

This is the era of the customer. Every experience is designed around our needs: from Netflix queues and Spotify playlists dictating what we consume, Amazon and StitchFix suggestions guiding our purchases, or Carvana, Zillow, and RedFin making buying autos and homes easy, sight unseen.

These businesses deliver tailored experiences to millions of users by capturing data and putting it into action. Advancement must do the same. In order to confront donor decline and build the future major gift pipeline, we must go beyond traditional segmentation like class year or degree. We have to speak to donors’ interests and deliver the same personalized, high-impact experience they see in the rest of their lives.

The University of Iowa is doing this by introducing a new persona-driven approach to communicating with alumni, donors, and friends. By monitoring more than 400 ever-changing data points, including a newly refined machine-learning-driven engagement score, the team has defined new audience segments. Each of these personas reflects how those people interact with the University, its sports teams, or its medical center.

With these personas built and evolving as donors demonstrate new interests or engage with the university, Iowa is delivering custom digital experiences, programming, and appeals that create better experiences for every donor.

In this session, learn how they built their personas and engagement score, early returns as they roll out a new communication strategy, and how this approach fuels giving and prospect discovery. You’ll learn best practices for every shop, regardless of size, as we all seek to create more personal experiences at scale.

With the development of advanced CRM’s and the increase in availability of alumni and donor data, we have the opportunity to become smarter marketers if we learn to listen to what the data is telling us. The University of Connecticut Foundation is using advanced analytics to better understand their constituents’ giving behavior, improve segmentation and shift annual giving strategy to a more donor-centered approach. This session will look at how to create and analyze metrics with the use of dashboards, and how to use these to produce actionable insights for both short-term and long-term success.

AMAtlas, CASE’s resource for data, metrics, and analytics has laid the foundations for a global set of benchmarking data on educational philanthropy, alumni engagement, and educational fundraising campaigns. This session will bring together a panel of advancement visionaries to discuss the ways they are putting CASE data to work in their development and alumni relations programs and explore ways they might leverage the Alumni Engagement Metrics (AEM) and new Core Metrics going forward. The session will also provide quick overviews of AEM and Core Metrics and discuss how institutions are adapting their processes to capture the data needed to put metrics to work.

Many advancement executives are excited by the potential of AI, big data, and data analytics. While data can improve decision support, there can be significant challenges in shifting from ideas to working reality. How can you—the data leaders and experts in your organizations—bridge the gap between exciting new capabilities and the complex realities of working with data? How can you effectively surface the infrastructure needs, governance quandaries, literacy gaps, data integrity challenges, and resource limitations that block these aspirations? Whether you are communicating to senior leaders, peers, or your team members, data projects often need some degree of audience translation. Join us for an engaging and interactive conversation on how to effectively communicate your needs to executives and decision makers in order to drive your data initiatives forward.

The ability to measure activity and understand the relationship between that activity and fundraising productivity are critical to the strategic deployment and maximization of fundraising resources. Furthermore, the capability to identify early intervention points for coaching new gift officers and the continued measurement of seasoned gift officer performance are key in ensuring gift officer success. But what should we be measuring? How should we be measuring it? Who should have access to the metrics? Can predictive solutions be deployed to assist in early intervention? And finally, how can these gift officer metrics be used effectively to inform decision making? The University of Texas at Austin has just completed an 18 month exploratory exercise aimed at building a predictive model to better quantify gift officer performance, establish baseline expectations, and identify early intervention points for coaching new gift officers. Multiple algorithms were explored in partnership with graduate students from UT Austin’s School of Information and members of the university development office’s analytics team. This presentation will detail the process undertaken to arrive at a solution, discuss the insights acquired along the way, and walk attendees through the mechanism produced to distribute scores. Considerations for generating equitable metrics based on role, unit assignment, officer type, and the impact of COVID-19 will be discussed along with an exposition of the final results.

Ever find yourself running the same report for multiple departments or prospect pools? Wanting to re-run an analysis, but forgetting how you undertook it several weeks, months, or years back? Frustrated over how best to combine multiple data sources? Rmarkdown may be a solution for you. This open source solution provides users with a format to combine analytical processes with documentation. It's flexible enough to run workflows in a multitude of languages (R, Python, and SQL, among others), produce various output media, and automate reporting. 

This session will be followed by a Part Two Workshop at 4:00 PM.