Throughout the years, financial stability remains the highest priority for sustainability. This year, every organization has been impacted by the effects of Covid-19 and many universities have already begun to feel the pinch caused by economic uncertainty. The impact Covid-19 has had on fundraising efforts for colleges and universities is yet to be felt completely.
Many specialists are providing best practices for university fundraising departments to succeed in the difficult environment that we are currently living. We can all agree that the fundraising team has a tough road ahead; a situation which, as a country, we have never seen before in our lifetimes. An organization may have had a one-off PR nightmare in the past from which valuable lessons were learned. Now, though, every institute of higher learning in the country will feel the impact of Covid-19 on donations.
University fundraising organizations must apply all their learned lessons of communication, donor involvement, listening campaigns and more as they look to navigate their organizations through the difficult economic times. However, they need to ensure sufficient focus on those donors with the highest likelihood of giving to maximize donations efficiently.
In the past, potential donors were cultivated with some knowledge of prospects’ current position in life. Prospective big donors, typically defined by basic wealth screening and a high-level knowledge of affinity for the institution, might be assigned to high profile school representatives. The data used in ‘prospecting’ was typically available as static, flat information where gift officers, managers and executives developed preconceived ideas about the donors’ likelihood of giving and set priorities based on those feelings.
The next important evolution in fundraising removes the undependable practice of ‘gut feel’ and creates a dynamically data-driven, focused approach to cultivating donors through a sophisticated, complex data modeling process using machine learning and Artificial Intelligence (A.I.). Utilizing a university’s unique data, many different influential factors such as gift officer engagement and evolving prospect behaviors contribute to a precise probability scoring of donation opportunities and ranking prospective donors.
When precise A.I. tools are deployed, the results are remarkable: The highest likely prospects to donate contribute at the prescribed percentage rate. Think of it this way. If you have 10 prospective donors with a score of 80%, then eight prospects out of the ten should contribute.
This means that gift officers who cultivate those prospective donors with highest likely A.I. scoring get to ‘Yes’ quicker by appropriately focusing efforts where the highest return will be yielded.
A data-driven, predictability model provides gift officers and management with a direct line of sight to the most likely prospects. Using the ‘old’ method of prospecting, time and money is wasted by soliciting from prospects that might ‘feel like good prospects’ or are next on an abstract list rather than focusing efforts on those with the highest likelihood of giving.
Not only does use of a precise predictability model that is constantly updated to reflect changing data help with finding the most likely donors, it also helps to identify those donors that might be receiving too much attention from gift officers. In this scenario, a different list of 10 names have a scoring of 10%. This translates to 1 out of those 10 people becoming donors. A lot of time is spent in courting those other 9 people whose probability is a low likelihood of giving. A view to less likely prospects encourages a gift officer to decide about continuing to pursue the prospects or to move on based on information.
Probabilities in a vacuum can lead to uncertainty and distrust. Transparency to the factors that influence scoring provide insight for gift officers about the prospect and the reasoning behind the probability score. Armed with detailed knowledge of the most impactful factors upon which the probability is based, gift officers are better prepared to approach prospects and more informed to make decisions.
The outcome from using an A.I. data-driven mechanical learning platform that leverages gift officer behaviors, prospects behaviors, demographic information, and proprietary data, is that colleges and universities save money, generate funding efficiently, and save time by identifying the donors with the most promise. In brief, the university does a better job of cultivating donors and raising donations.