HandPickedAI

This concept is the result of a Better Giving Studio month-long collaborative sprint focusing on Hyperlocal Volunteering which you can learn more about in this article. You can read the brief for more context and to see who was on the team.

Why BGS is Excited

Focusing on geographic proximity brings organizations more and better volunteers. These volunteers are more likely to care deeply about the cause, be more accountable, and be more likely to stay involved. See Ideas42’s Behavior & Charitable Giving 2019 and Coalition of Urban & Metropolitan Universities’ Place Based Hyperlocal Engagement to learn more.

• It’s a way to bridge the huge gap between the needs of nonprofits and the supply of volunteers.
• It could help organizations source volunteers through real interests and passions.
• It has the potential to encourage repeat volunteering and remove friction in the volunteering process.

The Opportunity

People looking for volunteer opportunities in their neighborhood have to navigate a sea of options. Finding the right fit can take time, and a lot of trial and error because of outdated websites, lack of response from coordinators and other issues stemming from lack of capacity at nonprofits. Once they’re ready to move on to new opportunities, the tedious search process starts all over again. Or it doesn’t start at all, keeping would-be volunteers on the sidelines. HandPicked’s AI makes finding the right volunteer opportunities easy every time by building not only personalized recommendations but prediction models that drive sustained engagement.

The Concept

A little data can lead to big opportunities. It does for Arlo. They’re curious about volunteering in their neighborhood, but they don’t know where to start. And they want to choose the organization carefully, because their last volunteering experience just wasn’t a good fit.  

Invited by a friend to join HandPicked, Arlo downloads the app and answers just a few questions about their skills, interests, experience, and preferred volunteering schedule. Within minutes, HandPicked pairs Arlo with three relevant hyperlocal opportunities that it recognizes as a good fit based on Arlo’s data. As a great cook, Arlo decides to help prepare a Thanksgiving meal at Bay Area Shelters.

After their first volunteer experience, HandPicked asks Arlo to answer a few questions about how it went. Arlo shares that the people at the organization made it such a great experience that they’d like to volunteer there again. Their quick feedback helps the algorithm “auto-magically” suggest more volunteer opportunities at Bay Area Shelters and other organizations it recognizes as a good fit for Arlo.

As Arlo continues to volunteer and provide feedback on their experiences with various organizations, HandPicked’s recommendations for them evolve. This broadens their impact and connections, deepens their relationships in their community, and keeps them engaged in giving.

What Do You Think?

Are you working on something similar? Have feedback to give us or insights that we should know about related to this exploratory concept? Did you test out this idea, and how did it go? We’d love to hear from you! Please write us a note at [email protected]