Predicting the Future: Driving Fundraising Outcomes with Models | npENGAGE

Predicting the Future: Driving Fundraising Outcomes with Models

By on Jul 10, 2018


nonprofit fundraising models

My first experience with modeling happened at the University of Washington development office in the fall of 1992. I arrived as the freshly-minted director of prospect research.  Life was primitive back then. Real people did not have cell phones, and the cell phones that existed were the size of bricks. The Internet barely existed; we navigated it with tools like Gopher and TelNet. This was the first job I had that came with an email account.  I was soon to learn all the lessons about what not to do by email the hard way.

Prior to my arrival, the development office had purchased a screening of the entire constituent database that provided two scores. One was a likelihood to give score, and the other was a wealth score. We also got some Claritas Prizm scores thrown in.  Prizm is still around—it’s a lifestyle indicator that classifies people into groups with common characteristics. The groups had entertaining names like “Gold Coast,” “Gray Power,” and “Big Fish—Small Pond.”  My favorite was “Shotguns & Pickups,” because it was so evocative of a specific lifestyle that I had some familiarity with.

My job, as the director of research, was to use the modeling results for prospecting—but also to sell them to the directors of development in the schools and colleges. I had a simple plan of attack:

  1. Cut the entire database into lists by school or college
  2. Take those lists to the director of development in each department
  3. Show them the top prospects identified by the screening
  4. Wait for the outpouring of gratitude

In fact, I typically got one or more of these reactions:

  1. “I already know all these people; tell me something I don’t know.”
  2. “I already know these people and they will never make a gift because they hate us.”
  3. “I already know these people and they aren’t wealthy enough to make a major gift.”
  4. (And this is the one that really drove me crazy) “I’ve never heard of these people. Why should I reach out to someone I don’t know?”

Gratitude was never once evidenced. In fact, most reactions resembled pity: “Poor naïve researcher, trying to mess up our fundraising expertise with science.”

I was too green and inexperienced to push back on the last reaction—the “who are these people” reaction. Today, it’s widely understood that this is why we use models: to find the unknown prospect—the whale swimming below the surface. We can use what we know about the people who do make big gifts to create models that identify those we never thought of as major gift prospects, but who match our model.

I will confess that this early experience with models made me a non-believer for quite a while. I became a champion of public data wealth screening. This takes what the prospect researcher does every day and soups it up to append public data to more records faster than ever before. It shifts the emphasis from reactive research to proactive by sorting your wealth screening results to reveal unexpected wealth. It’s an alternative way to spot the whales, and it’s a good one.

Wealth screening has its own set of problems, though.  Just because someone is wealthy, does not mean they are going to give you a gift.  And then there’s that annoying problem of privacy and respectful donor stewardship. Most wealth is private. I’ve handed profiles to major gift officers who threw them down in disgust, telling me that they know for a fact that this prospect has way more wealth than the profile reflects. Well, their inherited wealth, their private investments, their valuable collections—all private information. No researcher can put a precise value on any of that.

Over the years, I’ve learned that neither models nor wealth screening will save fundraising for nonprofits. In fact, I believe that they work very well hand in hand when there’s money in the budget and you have the staff and the expertise to make use of both. Sometimes you must make choices. And when you have to make choices, I’ve come full circle.  A well-designed modeling project can do more to provide structure and direction to a fundraising program than can wealth screening alone.

What does it mean to have a well-designed project? To preface, I am not a statistician. I don’t make models. But after 14 years of delivering and selling them, I’ve learned a few things about what it takes to make good models—things that will make any statistical analysis better. Without these fundamentals, you could end up completely off the rails.

  1. Garbage in, garbage out. You need accurate demographic data about your prospects in order to make valid models. Get the deceased people out of the database. Clean up those addresses, make them as current as possible. As much as possible, identify which of the several addresses you might have for the constituent is the home address. That is the most relevant address to use in your modeling. The presence of other addresses might turn out to be useful, but nothing beats the home address for what it tells you about the life and situation of your prospect.
  2. Don’t assume you know what will turn out to be predictive. You may know in your gut that alumni who were part of the Greek system tend to be good donors, but it may turn out to be something else, or a cluster of other somethings, that becomes the key to predicting giving. Let go of your pet variables and let the statistics speak for themselves.
  3. Watch out for endogenous variables – the variables that are in some way the cause or the result of the thing you’re trying to predict. Endogenous variables always have a strong correlation to your dependent variable, but they are not necessarily predictive. You’re making a model for a women’s college and it turns out that gender is predictive? Imagine that! You might find that knowing someone’s email address is correlated with giving. Is it also possible that people who give are more likely to share their email with you?
  4. Correlation is not always causation. If you find that most of your donors were born before 1965, that might be predictive, but not if most of your constituent population is of a similar vintage. Your model can’t just show what your best donors have in common. It must show what makes them different from those who were given the opportunity to give but didn’t.
  5. Models require a good historical record. If you only have one year of giving data, you can’t predict who is likely to give next year. What separates the one-and-done donor from the truly committed supporter? Without a few years of consistent solicitations and donations, you just don’t have the basis for a strong model.
  6. As much as possible, append data to your file that comes from the outside. Demographic data, wealth data, philanthropic data, consumer marketing data. These additional data points broaden your view beyond what you have in your own database and may reveal correlations and predictive variables that make your models stronger than they would be if you remain limited to the data you have collected internally.
  7. Have a plan to use the results. You can create the most perfect model ever devised, but if it doesn’t change something about the way fundraising happens at your organization, it’s just a waste of time and resources. Too many modeling projects have foundered on the shoals of fundraiser indifference. Get buy-in from key stakeholders before you begin and get a commitment to use the results. It really does take a leap of faith for some development officers to reach out to people they don’t know. But the proof of the pudding is in the tasting. Tasting must happen.

Ultimately, a good model is not a crystal ball foretelling the future with precision. It isn’t a guarantee that constituents will behave one way or the other. But a good model is a way to reduce uncertainty. If you pick any constituent at random, you will be highly uncertain that the person will perform any particular action, such as making a gift. But a good predictive model will provide a better filter to highlight those most likely to give or upgrade their gift. You reduce your uncertainty about the outcome of your fundraising by giving your best attention to the highest scoring people. It’s true that not every high scoring person will ultimately do the thing you’re asking for, and some with low scores will do it, even with very little encouragement. But we know that most people give because they are asked to do so. By giving your best effort to those identified by the model, you improve your chances of getting to “yes.”


David Lamb joined Blackbaud in 2004 following three years as an independent consultant for prospect research. David has more than 20 years of experience in the prospect research field. His Prospect Research Page( is a trusted and popular resource among prospect researchers. David is a frequent speaker at professional conferences, including those sponsored by the Council for Advancement and Support of Education (CASE), The Association of Fundraising Professionals (AFP), and The Association of Professional Researchers for advancement (APRA). His areas of expertise include prospect research, prospect management, fundraising, and database systems. In 1997, he received APRA’s Service Award for outstanding service to the profession, and in 2001, he was awarded the CASE Steuben Apple Award for excellence in teaching. He holds a bachelor’s degree in sociology from Sterling College (Sterling, Kansas), a master’s degree in sociology from Wichita State University, and a Master’s in Divinity from San Francisco Theological Seminary.

Comments (51)

  • Lyne Labrecque says:

    Very nice article that I will share with the director of development at my office. We are just in the decision making phase to see if we will spend for aN evaluation of our database or not.

  • Karen says:

    Fantastic view David! You are right in the modeling and wealth ratings. I look at correlations and analyze the relationship to identify opportunities.

  • Brinkley Cox says:

    Great article! We use the “Garbage in, garbage out” phrase ALL THE TIME.

  • Ann Nischke says:

    Loved this gem: using “models: to find the unknown prospect—the whale swimming below the surface. “ Thank you for the validation!

  • rachel says:

    “You can create the most perfect model ever devised, but if it doesn’t change something about the way fundraising happens at your organization, it’s just a waste of time and resources.” Sometimes preliminary buy-in isn’t enough when the model isn’t what they wanted to hear. In the future, I’m going to use this phrase to remind my groups why we do this: “a good model is a way to reduce uncertainty”. Great insights!

  • Jennifer Lange says:

    “Gratitude was never once evidenced.” Wow. I feel ya!

  • Donna Woods says:

    Great article! I’ll be sharing with our IA staff! Thanks!

  • Becky says:

    Great article! I’ll be sharing this with my team to emphasize again the importance of good, clean data. The larger organization which we support is working on some very powerful modelling and the results have quickly shown us where to throw additional resources.

  • Tammi Burkhardt says:

    We at engaged in modeling our mid-level and top prospects to determine affinity and to predict further engagement.

  • B Melloh says:

    Great Article David!

  • Jayme says:

    Thank you David! The database and its results are only as good as the quality of the data that is added to it. I agree that you have clean up the data with appends and general upkeep before you can expect results. However, I do feel that a working partnership with fundraisers (which I am privileged to have) makes a difference in the willingness to take chances on cold prospects.

  • Heather says:

    Interesting read! Our donors seem to be outside normal modeling parameters on occasion, but we still find it useful.

  • Ursula says:

    Great and informative article!

  • Amy says:

    Absolutely #2! I once ran stats on age range for alumnae because we had always assumed older alumnae were the most loyal. Surprise! Nearly 25% of the support over the last 10 years was from recent grads.

  • Jill says:

    YES! Especially on #5! Historical data is so important, implementing a new strategy and the timing is everything!

  • Kerry Ayres-Smith says:

    As the DBA for my organization who is in charge of creating the prospect cards each year, I love this article. Reading the science behind how to study their past gifts to gauge their future involvement is so fascinating!

  • Barb says:

    Some excellent points, thank you!

  • Andy Schroeder says:

    I have always felt that Fundraising is a science and your article helped to further that idea. I appreciated the comments about what development directors were telling you, as someone who has dabbled in prospect research I have heard much of the same negative comments.

  • Karina says:

    Great article. It is also frustrating when you have all these people with the same name and trying to figure out what belongs to who. I understand about doing all this work that takes a lot of your time to get not recognition or kudos in return.

  • Jenny Stephens says:

    Great article, very helpful and pleased to see we are headed in the right direction.

  • Karen Stuhlfeier says:

    Really good advice and something I’ll read again. Thanks!

  • Angie Stumpo says:

    Great info. Makes me want to dig into our database right now!

  • Magdalena Sarnas says:

    Great Article. This is similar to the type of research you need to in market research.

  • Sunshine Watson says:

    “Garbage in, garbage out!”

    Words to live by!

  • aps says:

    Excellent information! Thanks for sharing. . .

  • Joanne Felci says:

    Great article! And thanks for the blast from the past – Prizm – I remember that from probably 10 years ago.

  • Claudia says:

    Garbage in, garbage out– my database mantra.

  • Maggi says:

    Great article and again reflects the importance of keeping a database maintained and clean. I wish that management would buy into that thought and not think of it as a once a year task.

  • Gillian Armstrong says:

    I can’t imagine working in development during the beginnings of the internet, or even before. I appreciate the point on determining why a particular group gave (what makes them different as a whole from the rest).

  • Sage says:

    Garbage in – garbage out


  • Lindsay D. says:

    We can definitely use these tips! I’ve heard “Who is that? I’ve never heard of them.” so often when people’s names are brought up… We can’t keep going after only people we know!

    Do you think it’s alright to not have age in your demographic stats? We have hardly any birth dates in our RE, because people are often hesitant to give that information.

  • Alicia says:


  • Alice Black says:

    Thank you for this article! Love the garbage in/garbage out. We say this all the time!

  • Julie Ann says:

    Thank you for posting, a really interesting read!

  • Meagan Shaw says:

    Great read! Data is golden is used correctly.

  • Sasha Russell says:

    We are trying to move from “garbage in garbage” out to “good in good” out! Accurate data is so helpful when prospecting!

  • Dawn Stockton says:

    When I started as the Donor Relations Coordinator, I soon discovered that our database was a MESS!! Data was not entered consistently at all. I have worked very hard to get it cleaned up. Thank you for the great information!

  • krista says:

    i have so much to learn! thanks for starting out my new education in creating that crystal ball we all need!

  • Chris says:

    Number 7 is my personal favorite. Too often people are anxious to “do” something that they don’t plan ahead.

  • LaDonna says:

    Thank you for the great information!

  • Lisa Rizzo says:

    Very relatable!

    I really like the “Have a plan to use the results”, because so much effort goes into researching and many times if there is not a solid action plan many times it is not used or not to the extent that it could be.

    I think these are great tips not only for prospect research, but also for list segmentation for marketing, memberships, new projects, etc

  • Jennifer says:

    Data is a powerful tool and an excellent partner in philanthropy. This was a great read.

  • Lisa says:

    Data works wonders when it’s usable and organized well. It seems that the struggle to keep the database clean is the biggest hurdle, at least for us. Good read and something to think about. Thank you.

  • Tracey Sirles says:

    Research is life! Good article.

  • Julio Cesar says:

    Interesting read! Our donors seem to be outside normal modeling parameters on occasion, but we still find it useful.

  • George Buss says:

    I’m fascinated by modeling, and targeting your ask before making it. Thanks for focusing my thinking here.

  • MK says:

    Garbage in, garbage out, YES!!

  • Stephanie Boyce says:

    This was a great read! Thanks for sharing!!!

  • Mary Sommer says:

    Excellent points. Will be a great article for strategy sessions.

  • Kellie DeMers says:

    David Lamb does it again. Great article.

  • Maya says:

    A good model is an excellent tool. Thanks for a great article.

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