Jesse Pisors is dubious about data.
It’s not that Pisors, who is the vice president for advancement and external relations at the University of Houston-Victoria in Texas, U.S., thinks that advancement shops’ focus on quantitative data collection and analysis is inherently bad. But he believes an overreliance on numbers and percentages can be misleading.
In his presentation at CASE’s 2021 All Districts Conference, Pisors explained his view that even the best quantitative data-driven strategies only bring advancement professionals to the right neighborhood. A quantitative-only approach, he says, doesn’t allow them to knock on the right door. He suggests that a greater emphasis on qualitative data is the solution.
“The key with qualitative data is letting the source speak to you…and you listening,” Pisors says. “You learn from qualitative data. You let it instruct you. You let it enlighten you.”
Dwight Dozier, chief information officer at the Georgia Tech Foundation in Atlanta, U.S., understands the power of qualitative data to gain a better understanding of constituents. He uses his own experience as an example. Dozier graduated decades ago from his alma mater’s business school. But his career has taken him down the information technology path and, as a musician, he is passionate about jazz.
“My alma mater, with the data it has, is going to keep knocking on the college of business door. Knock, knock, knock. No one is there,” he says. “But if they would knock on the IT door or the music door, I would answer those doors.”
Pisors, Dozier, and many others agree that collecting, storing, and analyzing qualitative data is vital to advancement’s growth and an important balance to departments’ continually growing quantitative data capabilities. But how can leadership, advancement services personnel, and frontline gift/alumni officers collaborate to find a method that’s scalable? How can advancement professionals work with data that is, by its definition, difficult to measure?
The Nature of Qualitative Data
Speaking of definitions, let’s address what quantitative and qualitative data are and how research relying on each type of information differs. Quantitative data is information that can be counted or measured. In terms of advancement, this can mean money, donors, the number of event attendees, or the years alumni earned their degrees. Qualitative data is non-numerical information that helps a researcher understand the behavior of the subject. In advancement, this relates to how constituents feel. It’s a way to understand the relationship between them and the institution in a deeper and more nuanced way.
Pisors says there wasn’t a “light bulb” moment that made him a qualitative data evangelist for advancement. It’s simply that as he worked with qualitative research methods as part of his doctoral program at Texas Tech University in Lubbock, U.S., he acquired a better understanding about how it could apply to his day job.
“Quantitative assessment will never get you the feelings, the reactions, the thoughts which a person expresses and you perceive,” he says. “I’m so appreciative of quantitative technology…But qualitative research brings the necessary balance to the seemingly limitless world of quantitative number crunching that we have.”
In his 2021 presentation, Pisors highlighted how the two types of data can work together. A quantitative approach can measure and evaluate while a qualitative method can listen and learn. He suggests that each type can inform one another and combine to give a more thorough answer to a specific question.
He offers an example from one of his previous institutions: Pisors helped conduct a survey which found that 70% of young alumni said the institution was soliciting them too frequently. But a post-survey focus group brought more context. The young alumni in the focus group specified that it wasn’t so much that they were hearing from the institution too often, but that it felt like every time they got a call, it was only about money.
“We realized that if we were calling just as often for some purpose other than to ask for a gift, we could actually continue soliciting with the same frequency. They wouldn’t feel that it was too frequent,” Pisors says. “And that understanding came about because of qualitative research.”
He contrasts this with what would have happened if he and his colleagues had only operated on the quantitative research alone. Pisors and his team would have recommended pulling back from two phone campaigns to only one each year—defensible given the survey data. It would have looked like the right move, but it would have been only partially correct (not to mention potentially damaging to the institution’s philanthropic support).
As such, at the University of Houston-Victoria, Pisors is working to incorporate more focus groups, representative of all kinds of subsets of alumni and donors, to gain a better understanding and “fill the gaps” in the quantitative data.
A New Frontier?
Pisors is hardly the only one who perceives qualitative data and research as a growth area for advancement. For instance, Jessica LaBorde—assistant vice chancellor of advancement services at the University of California, Davis, U.S.—is excited about the potential within a more focused qualitative approach. Moreover, she thinks that donors are going to expect it.
“Online retailers are doing this in a way that’s mind-blowing,” LaBorde says. “Our donors shop on Amazon. It’s going to be: ‘You better know what I’m interested in, because otherwise I’m not interested in you.’ This will be driven by our donors as much as it will be driven by our own interest in excellence.”
Although LaBorde notes that advancement shops don’t have “Amazon budgets,” the good news is that many in advancement are already collecting tons of qualitative data, even if it’s not talked about in those terms. She, Pisors, Dozier, and others talk about how many institutions have thousands or millions of contact reports, event reports, open-ended survey questions, and more filled with qualitative data.
“Fundraising at the high end is all qualitative,” says David Lawson, CEO of NewSci and an expert on how fundraising, data, and technology intersect. “So, this is not about bringing qualitative data to fundraising; that’s always been there, but only for top donors and prospects. What artificial intelligence brings is the potential to scale the gleaning of qualitative insights and to connect those insights to actions.”
Like Lawson, Holly Peterson sees an emphasis on qualitative data as less of a new space and more of a continuation of advancement’s drive to understand impact. The head of constituency engagement at the University of London says she’s been seeking ways to better recognize these elements for more than a decade.
“I’m much more interested in measuring impact than outcomes. How does the institution change people’s lives through scholarships?” Peterson asks. “A gift of a million pounds can be measured, but the number doesn’t matter. What matters is what the money does.”
She also ties a sector-wide focus on qualitative data to her work with CASE’s Alumni Engagement Metrics Task Force and Survey, which seek to define a wider view of alumni involvement.
A couple of years ago, the National University of Singapore implemented the alumni metrics as a more straightforward way to understand alumni. Bernard Toh, director of alumni relations, says he is presently concentrating on building quantitative data for NUS alumni. Although he understands the value of qualitative data (and there are qualitative elements in the university’s post-event/activity surveys), the quantitative elements of NUS’s alumni database are where the priority is currently.
“Our focus now is to get the collection of our quantitative data right,” he says before referencing the 17 faculties and schools within NUS. “Our goal now is to persuade every faculty and school to standardize the way we collect data and also to collect data diligently. This will take some time to achieve.”
NUS’s quantitative aims aren’t an outlier among advancement shops. Toh’s colleagues at independent schools, two-year colleges, and many other institutions around the world may be at an earlier spot on their data timeline—something those at universities with strong quantitative data practices can forget.
Phil Higginson, associate head of school for philanthropy at Ravenscroft School in Raleigh, North Carolina, U.S., agrees with the principle of increasing efforts to collect and assess qualitative data. But he cautions that, for his school and others, this would require greater emphasis on hiring and training advancement services staff.
“Advancement services is not greatly appreciated at the independent school level as much as it is at the university level,” Higginson says. “We’re behind in that area. The majority of the independent schools just don’t have the staff to enter all the data.”
That’s not to say he and Ravenscroft can’t make strides with qualitative data. Higginson says the school is planning to create focus groups with loyal donors to facilitate more “two-way conversations” between constituents and the school. He thinks it’s a better way of hearing what donors want and engaging them in Ravenscroft’s needs and vision.
Like Higginson, MaryAnn Cicala thinks it’s important to capture how the most connected constituents feel about their institution. As the alumni relations director for Austin Community College District in Texas, she is a team of one, focused on building her database. But Cicala has looked at alumni affinity groups (including ACC’s alumni/employee group) to serve as informal focus groups from which she can acquire valuable qualitative information and bring it to the Alumni Network Advisory Council.
“Managing qualitative data is like anything else. If you’re listening, you’re hearing and really taking it in. But we all hear something differently,” she says, highlighting one of the challenges for this type of data. “So, it is important to look to who is at the table and what has been shared. You are going to see something very different than me based on your experience.”
Organize Your Organization
For this, and many other reasons, John H. Taylor recommends an institution-wide, strategic approach—regardless of what type of data an advancement team is attempting to capture. The principal of John H. Taylor Consulting, LLC, which specializes in working with advancement programs and advancement services, says this is even more important with qualitative data, which can be “voluminous” in terms of reports, recordings, videos, and more.
“The challenge and the opportunity is that if we don’t proactively go out there looking for those stories, the qualitative data, and put them in a digestible format, in a mineable database, it becomes very difficult to do anything with the data,” Taylor says, adding that he’s unsure if many institutions have the best systems or technology in place to manage qualitative data. “I can count on one hand the number of institutions I’ve consulted for in the last five years that have even begun to approach this topic.”
As Taylor points out, advancement professionals cannot mine qualitative data if there’s not a central place to put it. Others agree that most institutions’ customer relationship management technology isn’t geared toward handling both quantitative and qualitative data in a centralized manner.
That’s what Oliver Davey hopes to improve as the information and data manager at the University of London. Based on his background working with qualitative data in the nonprofit world, he discusses creating a centralized data lake in partnership with the university’s IT teams, where many staff members have access to both quantitative and qualitative constituent information.
“You have to solve the problem of structured and unstructured data residing in a data lake, with fluid access and the correct tooling and skilling of staff. Then you can collect both types,” Davey says. “It’s about getting the building blocks first, and then moving forward. There usually is a skills issue where you need to have the right people to write algorithms and mine this data, so it’s always important to do a skills audit and see if further training or coaching is needed.”
Others talk about how, for this to work, advancement and institutional leaders need to encourage best practices for reporting about interactions with donors and alumni. Technology can make this more accessible, but good data habits are still crucial, according to UC Davis’ LaBorde.
“Our development officers are excellent at listening to and understanding their donors. The question is, how do you standardize that?” she asks, then suggests adding checkboxes for key elements of an interaction. “That requires, I think, losing a little bit of individuality, and being able to put the standards in place.”
Where Do We Go?
If qualitative data is organized properly, it’s much more efficient for a machine (rather than a human) to find patterns or commonalities. Many in advancement think that the interest in a systematic approach to qualitative data will increase alongside the growing implementation of machine learning, predictive modeling, and natural language processing.
NLP, which is a type of AI, can be programmed to analyze huge amounts of human language and seek patterns, keywords, and specific insights. The application is obvious for NLP as it relates to, say, hundreds of thousands of contact reports packed with qualitative data.
“What you really should be doing is asking [constituents] very broad questions…but there hasn’t been a way to quantify it,” says Lawson, whose NewSci offers an NLP service. “That’s what NLP brings to the table: a way to quantify qualitative data.”
Lawson and the Georgia Tech Foundation’s Dozier see opportunities for predictive modeling to dig into the middle of the donor pyramid, drawing on qualitative data from those at the top to locate “undiscovered” donors who might be primed to give.
“With modeling, you can see where the significant donors started,” Dozier says. “If I can take that model and look at others in the mid-tier, I can model from the more exhaustive qualitative approaches [with major gifts] to the mid-tier in the pipeline.”
LaBorde is also using modeling and thinks there will be greater applications regarding qualitative data in the future, if responsible data methods are combined with widespread access to technology.
“While we’re not quite there yet, I see it coming and I’m excited about it,” she says. “As the marketplace demands it, the tools will become cheaper and more accessible to all industries.”
In principle, Taylor agrees about qualitative data’s value and ability to offset quantitative data. But given what he’s seen with his clients and education at large, he thinks the focus will remain on properly collecting, storing, and analyzing quantitative data.
“Right now, you’re looking at organizations that are barely able to obtain a quantitative data process,” he says. “Our institutional efforts are geared toward the quantitative side of the house. We can’t abandon that. Many institutions can’t even begin to think about building a strategy for qualitative data until they have gotten their quantitative house in order.”
Higginson, who is hopeful for a greater focus on both types of data at Ravenscroft School, does think that there’s an overemphasis on quantitative data.
“So many of us rely on data, data, data. It’s mostly quantitative data around prospect research information and giving history,” he says. “Values are not driven by quantitative data. They are driven by qualitative data. A younger generation of donors has enlightened institutions about how all donors, regardless of generation, are interested in prioritizing their giving to charities that can demonstrate the impact of their gifts which align with their passions and interests. I wonder if the national decline in alumni participation is because we’re not doing a good enough job with articulating the alignment of our values with their values. We’re just expecting them to be like previous generations of donors: loyal because they attended.”
Lawson says he’s optimistic about advancement’s potential to assess qualitative data in the long term and thinks the relatively recent adoption of CRM technology will be transformational for many in this regard. Dozier thinks that any advancement shop that considers itself donor-centric has to be listening carefully to its constituents. That means a greater emphasis on qualitative data.
In his conference session, Pisors described the immense potential he sees in qualitative data to answer questions uncovered by quantitative data and build better relationships with an institution’s community.
“I believe this has the potential to change how we think,” he says. “We have the skill set. We’re experienced at qualitative work on the individual level. We’re gathering a fair amount of qualitative data in contact reports and more, but we need to identify this as qualitative data. Where we haven’t gone yet is developing and using qualitative research as an advancement skill set that can inform how we address and work with constituent groups. We need to harness the power of qualitative data that we’re already extracting from donor conversations and other such interactions and build on this.”
Data at the Crossroads of Social Media
Tracking how constituents interact with institutions’ social media accounts involves both quantitative data (likes, follows, retweets) and qualitative data (which programs do they value? what conversations are they engaging in?).
“It’s not just about how many likes a person has made on your institution’s social media posts; that’s a number. But what did they say in the comments?” asks David Lawson of NewSci. “That’s qualitative data, and it is pure gold for fundraisers seeking a deeper understanding of their constituents’ current interests, feelings, and passions.”
UC Davis’ Jessica LaBorde discusses this in the context of her alma mater. LaBorde says that her undergraduate university does a great job with noticing her presence on athletics pages. The institution asks her to give and she does.
“I have degrees in English and communications. I’m not necessarily following the English department, but I’m following the football team,” she says. “My passion is athletics. They know that.”
Ravenscroft School’s Phil Higginson says that a shop with strong qualitative and quantitative data practices wouldn’t just notice how he interacts with his alma mater’s social media accounts, but would follow up based on those interactions.
“This person may not have ever given a penny … but for some reason, on Instagram, he is liking every single photo of lacrosse. Should we consider engaging them in the lacrosse program?” he suggests. “If someone likes everything the school is posting about diversity, equity, and inclusion, maybe this is a donor to put on a DE&I focus group.”