Take the Guesswork Out of Personalized Gift Array Values
Successful direct response fundraising campaigns feature many finely tuned elements, including demographically appropriate appeals, images, materials and keen insights about the best timing of solicitations. All these attributes — and many more — convey a deeper understanding of the donors and their connections to the cause. They are intentionally selected to connect on a more personalized level at scale with individual donors. However, there remains one campaign rudiment that is still often left to rote standardization — the personalization of gift array values.
Gift array values — those strings of suggested gift amounts that nonprofits propose to donors — are powerful tools for guiding a donor’s next gift. This is especially true for direct response campaigns, whose respondents are not in regular, personal contact with a nonprofit’s staff in the same way that major donors enjoy.
Yet until recently, fundraisers faced few good options that allowed them to easily employ individually personalized gift arrays at scale. Unique values calculated for each donor that accurately reflect donor sentiment in real-time. Amounts that would uniquely resonate with each person and optimize fundraising with the scores of individual donors that typically account for more than half of nonprofit operating budgets.
The most traditional approach to creating personalized gift arrays is to manually assign them to each donor. This method is extremely laborious, time intensive and — given the lack of insight about the donor — highly ineffective at optimizing fundraising. At Swarthmore College outside of Philadelphia, the director of marketing for alumni and parent engagement reported spending 30 to 40 hours every August manually creating gift arrays for each individual donor — a tedious experience that many fundraisers cannot even imagine doing. Moreover, no matter how the donor responded to the solicitations, those gift arrays remained static for the balance of the year. It was simply too much work to recalculate them.
Most fundraisers eliminate the idea of personalization at scale. They resort instead to recency, frequency and monetary (RFM) modeling to segment donors and then create gift array values based on a multiple of historic giving levels. RFM modeling produces gift array values, typically three per solicitation, that are based on previous gifts plus some standard escalations.
While RFM modeling aims to create more relevant gift arrays based on prior giving history, it is still a relatively static approach to appeals that fails to provide insight around donor sentiment. The formula typically boils down to a blanket ask for the last amount donated and then multiplied by 1.25 over the donor’s last gift amount for each of the slots in the gift arrays. It does not get any more dynamic or insightful than that. RFM modeling and standard increments in gift array values based on last gift amounts cannot learn and adapt to an individual donor’s sentiment at a specific moment in time. It also cannot enhance donor relationships over the long term because it’s purely a standardized approach with a value that lies in simple automated scalability.
Both methods have always been insufficient in optimizing fundraising, and this issue is amplified in today’s automated direct response fundraising environment. According to the latest figures from Giving USA, only 60% of American households made a charitable gift in 2020 — down from 70% in 2000. Contributions from the average donor are also shrinking, and, as a result, every dollar from your smaller donors now counts for more than ever before. Fundraisers and nonprofits literally cannot afford to leave their gift arrays to an imprecise and impersonal guessing game.
The good news is that technology has evolved, and artificial intelligence services are now available to produce individually optimized, time sensitive, insightful, gift arrays at scale.
Personalized gift array values set through behavioral economics models and artificial intelligence (AI) services enable nonprofits to work smarter, effectively gauging donor sentiment in real-time and creating personalized gift array values that optimize giving today and over a lifetime. AI service can constantly evaluate donor sentiment and optimize giving without changing existing fundraising systems, processes or practices.
After implementing AI service, Swarthmore College saw a 5.5% lift in its giving within the first six months. This resulted in an additional $38,000 of revenue, a return on investment of $29 for every $1 invested. Hours of stressful calculations were reduced to milliseconds of algorithmic computations that were dynamically updated with each round of solicitations.
As new capabilities, such as the personalization of gift array values at scale, emerge through innovations in AI services, nonprofit professionals now have the opportunity to enhance fundraising with new, invaluable insights on donor sentiment at the cost efficiency of direct response fundraising models.
Rachel Michele has been Arjuna Solutions’ chief technology officer and head of operations for seven years. She delivers a unique blend of technical, managerial and executive experience in support of product development, AI, process automation, data visualization, infrastructure, strategic planning, forecasting and company operations. Prior to joining Arjuna Solutions, Rachel led a forensic data analytics team as a manager at PriceWaterhouseCoopers. Her team was deployed in response to some of the largest cyber breaches, financial crimes and regulatory investigations in U.S. history, routinely requiring her team to parse through billions of records across disparate databases and data sets. Rachel has also led numerous multidisciplinary teams on projects concerning matters of U.S. national security.
Rachel has served as a guest lecturer at universities and professional organizations throughout the U.S. and has served on the data quality board for a national nonprofit. She has a bachelor’s of decision science and a Master of Business Administration from the University of Maryland.