Turning Data Into Dollars
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For me, 2020 was a year of contemplation and juxtaposition. I spent a lot of time reflecting on how lessons learned from philanthropy of the past could help us understand how to adjust to current events while also acknowledging that we are in unprecedented times.
Fifteen years ago, I offered a presentation at a CASE conference titled "Turning Data into Dollars." Yet here I sit in 2021, still thinking about how data can help nonprofits raise more money. Since then, we have weathered a stock market crash, an economic recession, three presidential elections and a pandemic, among countless other hurdles. We have also seen profound changes in how technology intersects with our daily lives, but has the path to successful philanthropic strategy not evolved? If so, what does that say about our industry?
Intrigued, I revisited my 2006 presentation, which was focused exclusively on the power of electronic wealth screenings and how to best apply them to major gift programs. While the title might still be relevant today — and buzzwords like "donor-centered fundraising," "data-driven strategies and "big data" might be the same — the application has (thankfully) evolved significantly. In that presentation, I quoted from Joni Podolksy's 2003 book, "Wired for Good: Strategic Technology Planning for Nonprofits": "Technology is, in many ways, a necessary evil for nonprofits doing business today. You need it just to stay competitive." Still true.
But competitive in 2021 looks different than competitive did back then. Frankly, the stakes are higher. Strategic decision-making is more nuanced. Successful philanthropy is more urgent. And luckily, the technology and analytic applications that support philanthropy are significantly more sophisticated. Sure, electronic wealth screenings still have a place in any successful major gift program. But instead of leveraging technology for data aggregation and simple capacity formulas, the most successful fundraising operations require more.
Advanced statistical techniques and data analysis, such as predictive modeling, has taken hold in our industry. Rather than merely focusing on aggregating public data, predictive modeling leverages past giving, demographic, behavioral, wealth, asset and other data to apply regression analysis and predict who is likely to do something in the future based on others who have done it in the past. Amazon and Netflix are two examples from our consumer lives, in which items or movies we might like are suggested with uncanny accuracy. Predictive modeling for nonprofits is similar, using the behavior of donors, activists or volunteers to help better understand who is more likely to do something in the future — like become a monthly donor, sign a petition, give online or make a major gift.
The goal now is to segment your entire database, better placing your efforts where they are likely to yield the highest return on investment. Conversely, it can also help you understand which segments are least likely to respond so that you can conserve scarce organizational resources. But unlike Amazon or Netflix, this is not as easy as the click of a button. Data itself does not raise dollars — your organization will still need to take action.
Whether focusing on wealth screening or predictive modeling, data analysis techniques have historically concentrated on simple segmentation. Simple segmentation looks for similarities between things or people. In our industry, it typically helps an organization identify "who" is most likely to do "what." Now, the most advanced statistical techniques allow the nonprofit industry to up the ante. Cluster segmentation goes more in depth, looking for similarities that help define what is different between groups, allowing a significantly more nuanced approach to identifying not only "who" and "what," but also "how." For example, once "who" is more likely to respond to a fundraising appeal has been identified, we can now also identify subsegments that are more likely to respond to a direct mail appeal versus an email and with what message.
With so much noise in marketing, social media and inbound messaging channels, nonprofits must be more effective in cutting through than ever before. Now we must also understand what messages will resonate best. You can raise funds for the same campaign by using quantitative, logical, emotional or passionate messaging. You can use language that focuses on building community versus building impact. These subtle changes in language, wording and images can affect response and dollars raised.
Cluster segmentation can finally allow us to do this. For instance, some donors might respond more favorably to a message that states, "To date, we have reached 42% of families in our area and are aiming to double that by next year," accompanied by a chart. In contrast, others might resonate with, "Join other families in your community to help double our impact," with an image of a family. It doesn't seem like a big difference, but it can be crucial to success. With cluster segmentation, the analysis can group like-minded people and create messages that resonate.
So, I can now say with confidence that we have evolved over the past 15 years. Our industry is using data, analytics and technology more strategically to remain relevant and competitive. And all three can help an organization be more strategic on where and how to spend its resources. Yet, at the same time, we can and should rely on lessons learned from our past to determine how to interpret data, apply analytics and leverage technology so that we can raise more dollars with greater efficiency. Here is to a smarter 2021!