The CASE Library has compiled this sample collection of prospect management policies and procedures. Prospect management policies outline procedures on prospect assignments and the movement of prospects through the stages of the development cycle.

The CASE Library maintains this collection of online samples for all CASE members. It includes examples of prospect research policies, templates, and sample reports.

Amid shifts like the growing importance of authentic donor connection and the race for artificial

intelligence, fundraisers are gearing up to adjust their strategies to reach goals.

As we move further into 2024, the landscape of donor engagement continues to evolve.

The release of ChatGPT in November 2022 spurred renewed discussions on the use of artificial intelligence (AI) in the workplace and academia. This resource collection from the CASE Library introduces the basics of AI, takes a look at some of the latest trends and ethical questions, and provides information on how AI is being leveraged by educational institutions and nonprofit organizations. 

The CASE Library has created this collection to assist members producing a Data Protection Impact Assessment (DPIA) as a requirement under the General Data Protection Regulation (GDPR). Additional resources are available in the Sample Collection: GDPR Initiatives and Policies.

The CASE Library has created this collection to provide resources about the European Union’s General Data Protection Regulation (GDPR), which went into effect on May 25, 2018. Included are examples of GDPR initiatives and policies at educational institutions.

The CASE Library has developed a resource collection on prospect identification and qualification. The collection offers a selection of books, book chapters, articles, and other resources on prospect identification and research, and prospect qualification. This includes introductory information on wealth screening and privacy issues.

Many agree that collecting, storing, and analyzing qualitative data is vital to advancement’s growth and an important balance to departments’ continually growing quantitative data capabilities. But is there a method that’s scalable? How can advancement professionals work with data that is, by its definition, difficult to measure? 

By and large, advancement professionals stop at AI tools that are definitive and descriptive in nature. That leaves plenty of improving space before we bump our collective heads on the technology ceiling.