A Fundraiser’s Secret Weapon: Data Analytics | npENGAGE

A Fundraiser’s Secret Weapon: Data Analytics

By on Dec 5, 2017


For 5 years the Blackbaud Institute has brought together experts in the nonprofit sector to speak on a wide variety of topics. From donor retention to nonprofit marketing to organizational leadership, every ebook is an invaluable resource.

In our npEXPERTS Spotlight series we’ll be sharing some of our favorite articles from the past 5 years. Our first installment covered the must-haves for your nonprofit marketing plan.

This post is all about analytics and the vital role it should play in your organization:

  1. Let Go of Your Fear – Anyone Can Be Data-Driven

  2. Predictive Analytics: Lighting the Way

Looking for a deeper dive into a particular analytics topic? You might find our Analytics Toolkit helpful!

Let Go of Your Fear – Anyone Can Be Data-Driven

by Jann Schultz

Being results-driven is an intrinsic quality we share as nonprofit professionals.

We all use data and analytics in some measure every day to guide our strategic planning and tactical decision making. And we all have likely asked these same questions:

  • How much do I really know about my donor’s relationship with my charity?
  • Am I using the data I have to its fullest advantage?
  • Should I be doing more?

Data—the capture, analysis, and use—can be overwhelming. And while I completely understand why you may be overwhelmed—maybe even frightened—by the concept of “big data”, the health and future of your organization depends on your willingness to embrace data head on. While this may sound hyperbolic, take a moment to think about how our business landscape has changed over just the last five years. The emergence of new technology alone has led to increases in donor expectations on several fronts—from mission transparency and program reporting to organization access and donor responsiveness.

The truth of the matter is that I failed third-grade long division. Data and the understanding and interpreting of metrics is something I have to work hard at, because it simply does not come naturally to me. The reality is that I collaborate with strong partners on data and the associated analytics then spend the time needed to understand and evaluate our fundraising programs at Project HOPE.

When I joined Project HOPE, the first thing I did was dig into the data with my partners. For me, I needed data to help focus on four things:

  1. Data to measure and monitor the performance of the individual giving fundraising program
  2. Data to create context, how our fundraising stacks up to peer programs
  3. Data to understand and adjust the donor experience to improve loyalty and retention
  4. Data to help tell the story of the mission—through outcomes and impact

The analysis of our fundraising programs’ performance helped set the foundation for embracing big data and creating the cultural shift needed at Project HOPE. I began to educate and report on a new series of metrics that added depth and reason behind our results. The analysis moved beyond short-term metrics such as annual net revenue and took a macro view of our program. The focus shifted to a new set of key performance indicators (KPIs), including:

  • File size and donor file coverage ratios
  • Donor retention rates across lifecycles
  • Percent of donors giving 5+ years consecutively
  • Lifetime value measurements

The introduction of these KPIs naturally led to discussions of how Project HOPE compared to other organizations. Through participation in multiple benchmarking opportunities with Blackbaud, I had the necessary context from which I could evaluate my fundraising program across a range of very specific metrics. Benchmarking has provided an apples-to-apples comparison of data and helped clearly identify areas of growth and opportunity as well as highlight how past decisions had affected the current program. Regular benchmarking meetings also provide opportunities to discuss what our peers are doing to influence those numbers and define how to grow our program in meaningful and measurable ways.

My next focus was identifying and understanding the key metrics connected to donor loyalty and then determining the process improvement and infrastructure enhancements needed to move the needle on performance. We launched a donor commitment study though a partnership with DonorVoice that utilized a data-driven approach to determine the quality of our interactions, identify gaps, and help prioritize areas of improvement. The study provided insights, such as low customer service ratings from donors who called our 800 number—especially donors who called outside of East Cost business hours. I was able to use the data to build a case for investment in a new donor services team that was significantly more responsive to all donors across the US and established new protocols for call management.

We also collect feedback across multiple donor touchpoints including website visits, online donation transactions, email, direct mail, telemarketing, and call center interactions. This feedback collection, which is standard in the commercial industry, is not something you see often in the nonprofit sector—and for us is a highly effective way of using data to build brand loyalty and preference. Through this ongoing feedback collection, we receive twice-weekly reports where we close the loop with donors—responding to both good and bad experiences. Data is used for trend analysis or point-in-time analysis—identifying and resolving issues. For example, we were able to identify opportunities to improve both our online donation form and gift acknowledgments that have increased donation conversions and customer satisfaction.

The fourth way that I regularly use data is to help tell the story of our mission—through outcomes and impact. As donors continue to make hard choices about where their dollars are going, they are taking a deeper look at the organizations they support. They demand proof of concept and access to information when and where they want it. So, it has become increasingly important to demonstrate quantifiable impact, but not at the expense of the emotional connection to our cause. Through storytelling and distribution across multiple channels, we make the impact of our lifesaving work in global health tangible and understandable.

So, how do YOU get started with data and analytics?

  • Start by identifying how you are going to use data
  • Work with your strategic partners to analyze and determine areas of opportunity
  • Build your case through use of insights and analysis
  • Measure and report on your success

From your friend who flunked third-grade long division—do not let all this talk of “big data” overwhelm you. Use these tips to leverage data and analytics to achieve fundraising success for your mission.

Predictive Analytics: Lighting the Way

By Gilman Sullivan
Technology drives change. In my experience, nonprofits adopt change a little more slowly than the for-profits, and the real need is to understand the benefit of the change. Lance Slaughter, Chief Chapter Relations and Development Officer at ALSA, spoke recently saying, as he held up his cell phone, that ALS could not have raised $115 million with the Ice Bucket Challenge. If it had not been for cell phones, it could not have been possible even five years earlier.

Technology allowed that to happen! As nonprofits continue to search for ways to get a bigger share of the donor and the constituent pies in an era of greater competition, it is those who really leverage technology who will be more successful. One key example is using technology to take donor information and use it to predict their future behavior. That is what happens with the use of predictive analytics. Predictive analytics will allow those nonprofits who use technology well to be more successful than those who don’t.

Before we go too deeply into Predictive Analytics, let’s first understand the three major types of business analytics: descriptive, predictive, and prescriptive:



Descriptive analytics in our nonprofit world use the attributes of donors to describe behavior or to classify them into groups. Descriptives describe what has happened.


Predictive analytics analyze historical data about donors to predict their future behavior. Predictives describe what will happen.


With prescriptive analytics, you are combining and analyzing data to make a prediction, then creating options to take advantage of that prediction. Prescriptives use the prediction to prescribe what should be done.

Imagine using your donor data not to just report what has happened, but to predict what is likely to happen. That is the opportunity offered by predictive analytics—revealing the likelihood of a major gift or a planned gift. Predictive analytics can light the way. What does the journey down that path look like?

  1. Let’s begin with your data. Is it good?

The common concern is that the data may not be good enough to be used for an analytic effort. Whether you are using an analytic partner or using analytic software, the hygiene of your data is critical. The statistical models work from your data, and technology in use merely enables that process. The accuracy of the data determines the accuracy of the result. Cleaning that data does not have to be painful, and a vendor may be a viable option for both cleaning the data and helping you build good data practices.

  1. What questions do I want to ask?

Regardless of whether regression analysis, classic statistical models you saw in college, or highly proprietary statistical models are used by your scientists using software or your analytics partner, you do not have to understand the entirety of the math involved. You do need to understand what question is being answered. You are no longer reporting who gave a gift over $1k or $100k, but rather who can give a major gift. That means that access to some data sources outside of your database may be necessary to determine both affinity and capacity. The model needs to know: to what other organizations did the donor give, and how much wealth does she possess?

  1. What will you do with the information? Can your organization react to and act upon the predictions?

If you use a partner experienced in nonprofits, and you understood the questions you were asking, the predictions will make sense. The process of using predictive analytics is only a benefit if you use the results. Using the predictions requires a cohesive organization willing to change. As with any major initiative you must consider and plan for successful adoption. The culture of your organization trumps everything else!

You need to:

  • Build a coalition across leadership that understands the benefits and is committed to supporting the process.
  • Determine who needs to be involved in the process.
  • Create a sense of urgency around the change. > Paint a verbal picture of the journey and the benefit to walking down this path.
  • Find an interested and dedicated person to own the effort.

Technology drives change. Those who use the technology and are capable of change will lead.

If you’re interested in learning even more about analytics, check out Blackbaud Institute’s Analytics Toolkit!


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