Make-A-Wish was stuck in a story rut. Learn how See3 helped it tell new stories across local chapters.
Make-A-Wish’s mission is a familiar one: to deliver strength and inspire joy to millions of kids with critical illnesses. However, the impact of a wish extends far beyond the Wish kid, and can change the lives of wish families, donors, and volunteers. However, in 2015, over 50 percent of stories being told across Make-A-Wish digital channels only focused on the impact of a wish on a Wish kid.
In order to unlock the powerful storytelling potential of this diverse range of impact, Make-A-Wish needed to understand who it’s supporters were. In partnership with See3, Make-A-Wish developed nine Audience Personas, detailed audience profiles that uncover the demographic and psychographic behaviors of an organization’s supporters.
Rolling out new strategies and messaging can be hard for any organization, not to mention a global nonprofit like Make-A-Wish. With 63 chapters across the United States, the job of operationalizing the organization’s new content strategy was no small task.
See3 designed and implemented a program called the Content Strategy Collaborative, which used project-based, cohort-based learning to train 24 chapter leaders how to utilize the organization’s new audience personas. By the end of the Content Strategy Collaborative, Make-A-Wish was telling 22 percent more stories that focused on the impact of a wish on supporters.
What we didn’t anticipate was how much chapter participants would enjoy - and thrive from - being able to work directly with their peers from across the country. See3 is now in the process of developing a program called the 2020 Collaborative, which is building a long-term structure of cross-chapter collaboration focused on sharing best practices and showcasing high-performing content.
When your chapters are empowered and aligned, your organization can make a bigger impact. Let us know how we can help you roll out your next big initiative across an enterprise using the power of cohort-based learning.