On February 16th, 2023, the U.S. Department of Veterans Affairs announced that Stop Soldier Suicide was a first-place winner in the Mission Daybreak grand challenge.
Stop Soldier Suicide’s winning solution, Black Box Project, is a pioneering innovation that that leverages artificial intelligence to redefine our understanding of suicide risk among veterans.
Stop Soldier Suicide set off to build a comprehensive marketing communication strategy. This incorporated traditional public relations efforts, paid ads, and bespoke content to support Stop Soldier Suicide’s accomplishments in the channels where their supporters are spending time.
After 24 hours, Stop Soldier Suicide’s post announcing the Mission Daybreak prize had 62 likes and 11 shares.
Stop Soldier Suicide reached out to GoodUnited for help.
Within minutes, a message journey was created and delivered to Stop Solder Suicide’s 90,000+ subscriber base on Facebook to drive awareness to the post with one simple ask: "Please share."
11:01 AM: 11 shares ---> 11:06 AM: 196 shares
Using one of GoodUnited’s supporter messaging journeys not enable SSS to move fast, it produced a potentially mission changing outcome.
After 72 hours, Stop Soldier Suicide’s post had been shared more than 2,300 times.
Organic engagement was at the highest it had been in more than 30 days, and far exceeded engagement metrics that they typically see from a similar email send.
Better yet, it saved the nonprofit over $14,000 in ad spend that can be allocated towards the continued research and advancement of programs like Black Box Project.
Stop Soldier Suicide provides consistent, confidential, suicide-specific care free of charge to service members and veterans at highest risk for suicide. Their goal is to reduce the military suicide rate to the national average by 2030.
To learn more or donate, visit StopSoldierSuicide.org.
Black Box Project uses best-in-class forensic tools to extract data from digital devices entrusted to SSS by surviving family members of veterans who died by suicide (after data extraction, devices are returned intact to survivors). Machine learning techniques are then used to build models to predict both the “who” and “when” for identifying veterans at greatest risk for suicide.