Beginners Introduction to Data-Driven Fundraising
Part 1 of 6 in Our Data-Driven Fundraiser’s Reference Guide
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Data-driven fundraising’s definition
What is data-driven fundraising? Before we can even begin to think about sharing six weeks worth of content on the subject, we need to know what our key phrase means.
The term “data-driven” is thrown around a lot. “Data-driven” very well could be the buzzword of the year, but what does it stand for? Is there a universally accepted definition for data-driven fundraising?
It turns out there is. And our team didn’t come up with it. Data-driven fundraising is simply the application of evidence-based practice (EBP) in the fundraising field. Evidence-based practice is characterized by three core components:
- Having a question about something (Will thank-you cards boost retention?)
- Testing that something (Send out thank-you cards.)
- Recording and measuring the results of that test (Did retention go up as a result of the thank-you cards?)
Building data-driven culture
With our definition in mind, it becomes clear why company or organizational culture is such a crucial component of successful data-driven fundraising. Organizations, no matter their size, mission or vision, are steered by their culture.
“I recently completed development audits for two nonprofits, neither of which had delved deeply into numbers beyond the overall amount raised in a given year or the amount raised for a specific event or mailing. In each case we looked back over five years and were able to identify peaks and valleys in philanthropic gifts and other trends.
In both cases we found that the nonprofits had lost significant numbers of new donors at some point, which for one of the two could result in significant issues with fundraising going forward given the overall age of the donor base. The findings led to conversations about donor retention and stewardship and resulted in one of the nonprofits sending a brief survey to lapsed donors. The other nonprofit is considering next steps to reach out to lapsed donors to determine whether to keep them on the mailing list.”
In order for data-driven practices to take place, you need an organizational culture that not only accepts questioning the status quo, but endorses it . Steve MacLaughlin, author of Data Driven Nonprofits, goes into much more depth on the importance of culture (there is an entire chapter dedicated to the topic in his book), but I took away one key passage from his writing.
Steve suggests that there are, “ABC’s of data-driven culture adoption.” They are:
- Acknowledge the current culture
- Baby step behaviors
- Culture aligns with strategy
Culture isn’t set in stone, but it also doesn’t change instantly.
If you are serious about adopting a more data-driven culture, then acknowledging your organization’s existing culture and focusing on its strengths is a good place to start. Next, be prepared to make small, incremental progress in developing a new culture. Not everyone will be on board with adopting a data-driven mindset. Find champions within your team that are early adopters and engage them. Finally, make sure your data-driven mindset aligns with the strategy of the organization. Without alignment, it’s like running into a meeting proclaiming the immediate need for Facebook advertising to boost acquisition when everyone is focused on tightening up your leaky bucket (donor retention).
Acknowledgement, incremental change and alignment, focus on these three components first.
Our culture conversation leads us to one of the most challenging problems nonprofits face when trying to shift towards being data-driven: the lack of infrastructure to efficiently and effectively carry out tests, record data and validate hypotheses.
It is very easy to say in a meeting, “I think we can improve online giving by 15% if we post more on Facebook,” but if you don’t have the tools in place to measure online giving, and you don’t have the resources to actually post more on Facebook, then nothing good will happen. You will be shot down the moment someone says, “How are we going to measure that 15% increase?”
It’s an unfortunate reality, but a lot of the technology and tools that nonprofits use are simply “hand-me-downs” from the for-profit world. (Free Salesforce for example…) A lot of them just aren’t that great. But, that doesn’t mean it’s impossible to find people, software and other resources that can come together to create the infrastructure you need to be more evidence-based.
This ties into another subject, overhead costs, which would be an even longer discussion, but the idea here is simple. It’s a chicken and egg problem. You need an organizational culture that endorses questioning, testing and measuring. And, at the same time you need the people, tools and resources to actually be able to do that effectively. Try your best to bring together both at the same time.
Leverage online resources like LinkedIn groups and community forums to learn what your peers are using and how they are building the infrastructure necessary to be more evidence-based.
With culture and infrastructure in mind, it’s a good time to address a few basic data-driven principles. What are some basic questions that you should have answered? And, what are some simple ways to start getting organized, measured and analytical? I’m glad you asked!
Our team came up with three questions and metrics that are good places to start:
- What is our annual rate of growth?
Download the PDF cheat sheet to the left (or above if you’re on mobile) for more information on how to calculate, analyze and understand those three questions/metrics. Hopefully you are starting to think to yourself about how having answers to these three questions would help guide your organization’s strategy.
If average gift size has been on the decline for the past three years, maybe it is time to invest some money in upgrading donors. Or, if your donor lifetime value metric is trending upward, it may be the right time to increase budget and allocate resources towards acquiring new donors at your perfect price point.
These questions are simple, but their implications are significant. Part of becoming a data-driven fundraiser is taking a step back from the data. You’ll want to spend your time strategizing and analyzing, not crunching numbers and making excel spreadsheets. Begin with these basic metrics and you should be off to a good start.
Applying this to your shop
“At the Philadelphia Museum of Art Suzanne Harris, a Research Analyst was using the Raiser’s Edge donor database. Raiser’s Edge provided summary financial data, which was exactly what she needed to calculate a RFM (Recency, Frequency, Monetary) score.
Suzanne struggled with how to make it come together for the Museum. She began having conversations internally with database/IT folks. She emphasized how the RFM data would be used and why that was important.
She attended an APRA conference where she heard Joshua Birkholz talk about the value of fundraising analytics. Upon returning to the office she read her notes out loud, verbatim, to persuade people of the importance of a score like RFM.
Then, finally, it all came together in one meeting. Suzanne sat down for about an hour and half with an internal database guru and they worked out how the RFM could be automatically calculated using an intermediary Access database. They cherry-picked the data points most relevant to the Museum and created the scores based on them.
Suzanne’s “I can do anything” generalist attitude, combined with her ability to boldly persuade others of the importance of an internal score had resulted in success!”
Data-driven, or maybe we should call it evidence-based, fundraising is a pretty simple concept. Have a question. Test it. Measure what happened. Writing it out takes a mere 8 words to do. But implementing the concept is undoubtedly more difficult.
So, like we discussed above, start with culture. Share this article with a colleague in the office. Start to have the discussion about metrics at your next meeting. Engage early adopters internally and get their support. Culture is slow to adapt and change (which can be a good and bad thing), but share these ideas and eventually something will stick.
Next, do some research on the infrastructure. You don’t want to spend days or weeks measuring and calculating metrics, you want to spend that time thinking strategically about the results of your test. Look into tools that can help you do this more efficiently and easily. (P.S. that’s what we’ve spent the past year building here for you, for free.)
And finally, start small. Growth rates, gift amounts and lifetime value are a great place to start. Look at those metrics, answer those questions and slowly but surely, compel your colleagues and staff to take a look too. Dive even slightly deeper and calculate your donor retention rate, share that metric with your Executive Director. Discuss the potential need for employing steps to retain donors such as developing a welcome series of email or letters sent to first-time donors. Reference Sophie Penney’s story from above, and calculate your lapsed donor metric. If that number is growing it might be a good time to suggest implementing a lapsed donor mailing such as a survey.
Remember, this is part 1 of 6 in our Data-Driven Fundraiser’s Reference Guide! If you’re interested in receiving email updates as new parts of the series are released, please fill out this short form:
- Data-driven fundraising is simply fundraising that is compelled by data, not intuition or gut-feel. The definition isn’t overly complex, it’s actually pretty simple.
- You need an organizational culture that endorses questioning, testing and measuring. And, at the same time you need the people, tools and resources to actually be able to do that effectively. Do your best to bring culture and infrastructure together at the same time.
- Start small. When following the evidence based practices outlined above, question practical, easy to grasp things. Answer questions that your co-workers will comprehend. Present things in simple, visual ways.
NOTE: Thank you Sophie Penney, Jennifer Filla and T.J. McGovern for sharing your insights and feedback on this section of the guide. Thank you Jennifer Willett, Cheryl Papsch and Lizzie Weiland for editing this section of the guide.
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