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 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.

Can Natural Language Processing techniques help identify which funding opportunities would be best suited for different researchers? Learn how The University of Chicago is using Doc2Vec and advanced text analysis to help match faculty members with research funding.

The New York University (NYU) Development and Alumni Relations (UDAR) team and the University of Washington (UW)Advancement Analytics team will take you on a journey and share how they were able to jumpstart unique data analytics initiatives from inception to maturity in less than a year. Their journeys center on aligning technology with leadership visions and goals, while also empowering front-line fundraisers and marketers to uncover opportunities and take action on critical business insights. They will share an approach that helps them accelerate the implementation and deployment of specialized dashboards, improve the endorsement of their colleagues, and secure high adoption by diverse teams and non-data users. They will demonstrate how their approach can help instill a data-driven culture, and share strategies on how to tackle unique challenges presented by digital data and legacy systems while trying to provide flexible self-service analysis tools. They will also showcase UW’s Email Marketing Dashboard that serves over 50 units, three campuses and over 100 marketers and advancement staff, and NYU’s BI portal which contains more than 60 Dashboards and self-service reports that serve more than 300 fundraisers and development associates and staff and more than 20 schools and global camps.