DataKind examined projects on GlobalGiving to identify factors that correlated with fundraising success.
How can nonprofits raise more money on GlobalGiving?
If you’ve spent some time on GlobalGiving, you know that our project pages are the main hub of all fundraising activity. On project pages, nonprofits describe their needs and give their best pitch to attract potential donors. We partnered with a team of DataKind volunteers to analyze data from these pages to identify factors that may help nonprofits raise more money on GlobalGiving.
We already use data to drive our work (after all, our chief core value is Listen, Act, Learn. Repeat.), but we wanted to go deeper using data science (and some excellent data scientists) to uncover what leads to nonprofits successfully reaching their fundraising goals. We hope to use this information as we refine our search algorithm to help donors find projects they’re most interested in and also help nonprofits maximize their ability to attract donors.
Data science uses statistical and computational analysis to turn unwieldy amounts of data into actionable information to guide organizational decision making. Think of the many online services you use like LinkedIn, Netflix, or Amazon. These companies already use data generated by users on their sites to better serve their customers—making recommendations to help you use their services more effectively. We’re doing the same thing, using the same data science techniques that companies use to boost profits to advance our mission.
So what do nonprofits that raise more money on GlobalGiving have in common? Many things: They get high traffic on their project page, they have a strong social media presence, and a broad base of followers outside GlobalGiving. These factors of success are largely external; in partnership with DataKind, we wanted to focus on a component GlobalGiving could influence—the project page. Improving the project page itself (with even minor tweaks), or providing nonprofits with tips backed by data, could have a huge impact on fundraising success over time.
The DataKind volunteer team worked closely with our tech team to analyze which aspects of the project page led to higher conversion rates for donors. Looking at data from more than 4,000 project pages that had at least 100 visitors each, the volunteers looked for patterns and useful insights that could help us guide partners on best practices for maximizing donations. We studied a variety of project page features, including project title, funding amount, number of donors, photos, length, and content of project summaries.
We identified four factors that had a clear influence on a project’s conversion or donation rate:
Now, as any good stats student knows, correlation is not causation. All of these findings were based on inferential analysis of GlobalGiving’s existing data, which means we don’t know if these factors actually caused increased conversion rates. Nevertheless, the findings offer powerful information for our team to experiment with as we make recommendations for our partners going forward.
Data is everywhere. What hidden learnings are in your data? Your organization may have a web platform like ours where you’re constantly generating data, or may have other sources like program intake forms, surveys, or social media analytics. And don’t forget the wide range of publicly-available data provided by government agencies and others that can shed light into how your organization can maximize its impact.
If you’re interested in learning how your organization can tap the power of data science to improve your efforts, check out NTEN’s Data Community of Practice, Data Analysts for Social Good or reach out to the DataKind team at email@example.com for advice on how to get started. If you think a data science project might help you scale your work, apply on the DataKind website for support!
All data science journeys begin with a question. What question will help your organization move the needle on the issue you care most about?
This post was written in collaboration with Miriam Young of DataKind.
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