Amidst the expansion of non-profit organisations, their financial support is paradoxically decreasing. The decade from 2013 to 2023 saw a 25% increase in the number of non-profits registered with the Internal Revenue Service. However, the past year has been marked by a decline in fundraising, with a 3% drop in the amount of money raised and the number of donors. This trend highlights a growing concern about the sustainability of funding for these organisations.

Vijay Mahajan, a marketing professor at Texas McCombs, identifies the primary challenge facing non-profits as the low response rates to fundraising solicitations. He attributes this mainly to a lack of quality donor data, which complicates the targeting of fundraising appeals. Non-profits often have detailed records on active donors, such as how much and how frequently they contribute. However, the cost of gathering and maintaining similar data on potential donors, such as those who have yet to contribute, is typically prohibitive.

In his recent research, Mahajan suggests a novel approach to this problem. Instead of relying on historical donation data to forecast who might respond to fundraising appeals, non-profits could use community-clustered profiles. These profiles are built using publicly available data and can identify potential supporters’ locations based on demographic, financial, and social characteristics.

Mahajan explains that these community profiles provide deep insights into donor behaviour. By accessing secondary data sources at the state and ZIP code levels, non-profits can comprehensively understand who is likely to respond to their appeals. This method of targeting potential donors is innovative and potentially transformative for fundraising strategies.

The study categorises potential donors into three groups: current active, lapsed, and new prospects. Mahajan argues that community-clustered data can be instrumental in effectively targeting all three categories. He hypothesises that people with similar backgrounds and lifestyles tend to cluster in specific communities, which can be identified through this data.

Mahajan collaborated with Shameek Sinha from the University of Auckland, Sumit Malik from the University of Liverpool, and the late Frenkel ter Hofstede from Texas McCombs to test this theory. They used a large dataset provided by the Direct Marketing Educational Foundation, which included data on 429,310 donors who had contributed at least once over 15 years. The researchers mapped 44 different characteristics to create detailed profiles for each community, finding that factors such as gender, household size, and financial status were significant predictors of donation potential.

These findings suggest practical applications for non-profits. Fundraising teams can enhance donor data with information from community-clustered profiles to improve response rates. Where information on potential new donors is limited or costly, these profiles can help identify where these individuals are likely to reside, allowing for more targeted and cost-effective fundraising strategies. According to Mahajan, this approach not only maximises the use of available data but also significantly reduces the need for expensive data collection and analysis, making it an invaluable resource for resource-strapped non-profits.

More information: Vijay Mahajan et al, Retain, reactivate or acquire: Can nonprofits reliably use community profiles as an alternative to past donation data?, Journal of Business Research. DOI: 10.1016/j.jbusres.2024.114997

Journal information: Journal of Business Research Provided by University of Texas at Austin