This week I’m in England talking with our colleagues at The Factary in Bristol. Their offices are open-plan and I’m impressed at how collaborative they are, with each other and with their clients. I met with nearly everyone on the team to learn more about the kinds of projects they’re working on, what they each do to meet their clients’ needs and how they work together as a team. It’s really interesting to see the ways we’re the same and the ways we do things differently.
Back before I started my career in prospect research in 1987 at The University of North Carolina at Chapel Hill, I was the assistant to the director of finance for the development office at the university. My responsibility was to help my boss Jean track all of the money the university received; she had to make sure it went into the right bucket and was distributed correctly.
As a fundraiser constantly on the go, sometimes it might seem like it takes a long time to get a research profile back from an in-house prospect researcher or consultant. When you’ve got a meeting coming up or you need to prioritize your prospect pool in a hurry, I know that you really haven’t got a lot of extra time to wait. Here are four secrets to getting information back faster from your research partner:
1. Take care of your gold. Chances are that your nonprofit received a larger-than-normal number of gifts last month, and many of them came from new donors. The money that came in will help you do your important work, but the gold I’m talking about is the information that came with each gift. You’ve just started a relationship with someone new that you hope will last a lifetime, right? Here are a few things that your organization should pay attention to:
Chances are good if you are in the fundraising field that you have heard the term “fundraising analytics.” You’ve probably also heard the terms “data mining,” “donor modeling,” “reporting” and “prospect identification,” too. Do these terms mean the same thing? What are the differences among them?
I asked Marianne Pelletier, who leads the HBG Analytics team, to help me put together a series of short articles designed to make sense of these terms. In each, she will describe the method and give examples of how they can be used. To continue our series, we describe the questions that Donor Modeling can answer.
Let’s begin with a case study:
A museum is in the planning stage to launch a major fundraising campaign. Their last campaign was over 5 years ago, and while they had a number of significant gifts, the coming campaign will require many more major gifts in order to be successful. After developing a table of gifts for the campaign, it quickly becomes apparent that there are huge gaps that need to be filled with prospects at every level. Significant prospect identification needs to happen.
To score the museum’s database and identify the top prospects, the museum decides to use a technique called predictive modeling, also referred to commonly as donor modeling.
What is donor modeling?
Donor modeling uses statistics tools to score a group of records using a variety of methods, including regression analysis, clustering, decision trees, neural networks and support vector machines (SVMs) amongst lots of others. Let’s take a look at just one of them, regression analysis.
Regression analysis uses calculus to find the slope of a line, which helps us visualize trends in the data. For example, we could see (based on a number of factors) which groups of people in the museum’s database have the most capacity to give as well as affinity, or connection to, the museum.
Here’s a standard matrix that is often built for major gifts programs. After downloading records and using regression analysis to score the group studied, prospects would be shown along the slope of the red line based on their relative affinity with the museum and their capacity to make a major gift.
Affinity, or “how much they love the museum” might be measured by the number of times someone attended events, or donated in consecutive years, or bought tickets to special exhibits, amongst other things. Capacity, or “how much they can give” might be found through primary or secondary research, such as a visit, prospect research or an electronic screening.
A graphic describing the relative level of a group of prospects’ affinity using a number of hearts (ranked on a scale of 1 to 3) and the relative level of their gift capacity (ranked 1 to 3) by dollar signs might look something like this:
In this example, the top-right box represents those with greatest capacity and affinity for the organization, and the bottom-left box shows those with the least.
If you were the chief development officer at the museum, whom would you want to approach first? Your answer is likely to be those in the top right group. Unfortunately most of the time that group is also the smallest population among the scored groups, and are usually the donors you know fairly well.
Whom to select next, then? Often, two of the largest groups, represented by the larger boxes, are the $$$ ♥♥ and the $$♥♥♥ groups. And of those, it might be hard to decide which to choose.
So, your next donor modeling study might be to look at the museum’s past track record with each of these two groups. What is your level of success in cultivating each group? What motivates them to become major gift donors?
Donor modeling helps answer those questions. The characteristics of top level donors are compared to various segments of the pool, and their scores help bubble up the best future prospects.
What else can you use donor modeling for?
Although it’s most often used to identify major gifts prospects, donor modeling can also rank groups like these:
Donor modeling can even help determine the best ways to acquire new members for a member recruitment campaign. It’s a powerful tool to help your organization identify new donors, whether you’re in a campaign, thinking about a campaign, or just looking for new donor prospects.
What do you need to know?
Our series on the ABCs of fundraising analytics continues next Thursday, September 19 with a look at data visualization.
Do you have questions about donor modeling or would you like to see it at work at your organization? Contact us at info [at] helenbrowngroup [dot] com for more information.
Chances are good if you are in the fundraising field that you have heard the term “fundraising analytics.” You’ve probably also heard the terms “data mining,” “donor modeling,” “reporting” and “prospect identification,” too. Do these terms mean the same thing? What are the differences among them?
I asked Marianne Pelletier, who leads the HBG Analytics team, to help me put together a series of short articles designed to make sense of all of this. In each, we will describe the method and give examples of how they can be used. To begin our series, we discuss data mining.
Let’s begin with a case study:
The fundraising team at a university is having a problem with donor retention. Every year the university must acquire a significant number of new donors to offset the nearly 50% of donors they lose from the previous year. They need to find an answer to the question “why are we losing so many donors, and what can we do to keep them from leaving?”
They decide to use a technique called data mining to find out.
What is data mining?
Sometimes we don’t know what we don’t know. In those situations, it may be best to explore a little to see if we can find answers (and perhaps even see the questions we need to ask). Think of it as discovering what’s in a new mall by walking the length of it: data mining is like shopping in the data mall. What will we find when we look in each store?
Data mining sifts data back and forth until it finds natural breaking points, puts together associated characteristics and then lays out what it finds. For example:
“The donors who give the highest gift amounts are married male alumni. They’re in their 50s. They live along the east or west coast. They have a job and children that we know about.”
Data mining delves deeper to find relationships between many characteristics
The good news is that data mining also names the characteristics for the second best segment, and the third best, and so on, so a university could use it to find out various solutions to their attrition problem. The characteristics they see might look like these:
“The donors who stop donating tend to leave between their 5th and 6th years of consistently donating to the annual fund” So we can work harder to retain them before year 5!
“The donors who stop donating tend to be married alumni males in their thirties” What special incentives can we offer to that group since we know that married men in their 50s tend to be our largest donors later on?
Data mining can also be used for:
What else can data mining do?
Have you ever walked into a store that has section after section of fun things for purchase? A department each of clever t-shirts, gifts that your best friend would love, beautiful hand-crafts, and more – things that are so perfect that you’ve picked up an armful of things and you need to find a basket to dump them all in.
Data mining is like that. It can also be for:
What do you want to know?
Our series continues next Thursday, September 12 when we will discuss Donor Modeling.
Do you have questions about data mining or would you like to see how it can work for your organization? Email us for more information at info [at] helenbrowngroup [dot] com.
Are you hearing a lot of talk lately that fundraising is becoming more data-driven? That we need to show the return on investment of donor dollars? We’re all hearing this train coming down the track and it’s fueled by top volunteers who are used to seeing that kind of business intelligence in their company board meetings. In the past few years they’ve come to expect to see it when they sit at the nonprofit board table as well.
It’s good business, and it makes sense
We all want to cultivate prospects that have the highest likelihood to become donors. And we need to be careful with our most valuable resources – time and money. So it makes sense that business intelligence methods are edging into fundraising work as well. Ten years ago, we didn’t hear much about analytics in fundraising, but in the past five years more and more shops are hiring fundraising analysts – internally or outsourcing the activity to companies like mine.
Earlier this month, Forbes magazine published an article called “Does Your Organization Need a Chief Analytics Officer?” (link here). The article describes two very interesting case studies highlighting how Caesars Entertainment and KeyBank are using analytics to create stronger relationships with their customers and creating more revenue.
Ruben Sigala, the new CAO of Caesars:
“The data we collect is a treasure trove that enables us to treat every guest well, but treat every guest differently,” says Sigala. “And centralizing the function has enabled us to get a lot more creative about how to reward customers across properties and functions, and how to drive more revenue for Caesars.”
By substituting a few words, it’s easy to see how that could translate to fundraising, isn’t it?
At Cleveland-based KeyBank, one of the top 20 largest banks in the country, they’ve done something really interesting: the marketing team and the analytics team both report to the Chief Analytics Officer. What would that look like translated to fundraising? Major gifts officers would report to the Director of Strategic Information.
Will that happen in your shop? I’m sure the marketing folks at KeyBank wouldn’t have thought it possible 10 years ago.
Regardless, the big takeaway here is that fundraising analytics is here to stay. These tools are incredibly nimble and powerful and are having a tremendous impact on the organizations – large and small, for-profit and nonprofit – that are using it. And if you are unfamiliar with the terms or how data analytics can help you, stay tuned to this space.
Over the next few blog posts, HBG’s chief analytics consultant Marianne Pelletier and I will go over, in English, what analytics are and what these tools can do for you and your organization. If you have any questions at all, or are interested to see how we can help, don’t hesitate to be in touch.
First in the series, this Thursday we’ll discuss Data Mining.
I think it’s easy to get frustrated with assumptions that some front-line fundraisers have about prospect research. On the one hand, we researchers want people to see us as a resource. Indispensable. That we have (or can get) all the answers. Fast.
As a personality type, we tend to be diligent and dogged sorts of people – we generally can’t rest until we find the answer. We learn Boolean logic. We use databases that give us reliable answers. We get faster at it and we’re proud of our agility and reliability.
But that can foster an assumption that research profiles just appear (poof!) with the push of a button. Or that full profiles only take a couple of hours to do.
As a colleague at the APRA conference said two weeks ago, “Research profiles take two hours to do just like a major gift takes 18 months to get.”
They have no idea what we do all day…
A few months ago I did a training session – an introduction to prospect research – for a development team at a mid-sized nonprofit that had no researcher. I asked them to give me the name of one of their donors that they’d recently researched using the big search engine. They were feeling pretty confident that it had turned up everything there was to find about their Mr. Smith.
So first I used that search engine (and taught them about a couple of others) and we went through the results. Then I pulled out my research magic wand.
I admit, I just love the ooohs and the aaahs that always generates. Then I showed them a few other fee-based resources we use. Deep web, pay-wall, give-me-the-serious-411 kinds of resources.
“Wow, that’s a lot of information. It must take you forever to visit all these sites and pull together a profile on someone” said the director of major gifts.
…and we have no idea what they do.
But before we researchers start feeling too smug here, let me just say that we make a lot of assumptions, too, about what fundraisers do all day. The good ones make it look easy – but it’s a lot of hard work and it takes longer than we think.
So here’s my proposal: The next time your development office does a brown bag lunch together, show each other what you do. Just a half-hour each. Talk about how much time each thing that you do takes and what your greatest joys and frustrations are.
Honestly, it’ll just be…magic!
You need new donors in major gifts, annual giving, planned giving, principal giving…okay, I understand: you need new donors in ALL areas of your fundraising operation. No worries. Here are just a few (of the many possible) remedies to help you identify and involve new donors.
Remedy #1: Have you taken care of donor attrition?
For many organizations, attrition numbers are scary-high right now. Do you know what percentage of your donors leave every year? It’s a lot easier (and cheaper) to keep a donor than it is to acquire a new one, so work at understanding how many are drifting away and why they leave. Then devise strategies to keep them.
Remedy #2: Do you know who your best prospects are?
It doesn’t matter if you work at an organization with less than 500 donors or one with a million. You need to get to know your donors better so you can find others like them. Data analytics – even basic queries – can provide characteristics of your best prospects to help you identify more people just like them. Slicing and dicing your data – even sparse data – will give you great answers. If you don’t have capacity to do it in-house, it’s very easy to find talented analytics experts to help you.
Remedy #3: Do you know what it is about your organization that donors love?
You may be surprised to learn that it’s not always a priority you’re pushing, but some other X factor that gets them jazzed. Ask them! Surveys are a great way to find out donor interests and opportunities you could capitalize on. (And don’t give me the old “but we’re not an alumni-based organization!” argument!) Alumni organization or not, don’t you have gorgeous t-shirts to give away as an incentive? Or what about a “Free ice cream cone in the splash park for donor survey responders appreciation day”? What do you have that prospects would value? Be creative and piggyback activities!
Remedy #4: Are current donors giving you what they’re giving other nonprofits?
An electronic screening can help you answer this question, and will help you elevate both annual fund and major gift numbers – probably significantly. Many of the vendors, in addition to providing asset information, also match the individuals in your database to donor honor roll lists of nonprofits across the US and United Kingdom. Someone who is regularly making gifts across town that are 10x what they give your organization needs to be asked for more.
Remedy #5: Maybe they don’t love you yet, but what about the ones who ‘Like’ you?
It’s a good bet that your nonprofit has some kind of social media presence at this point (and if not, it’s time to get a move-on). What have you done to convert those people who just Like you into future donors who love you? What can you offer them – of value – in exchange for their contact information? A study or white paper? Access to an invitation-only lecture? A free hour in the swimming pool? A ‘behind the scenes’ tour with the performers?
These are just a few of the many ways strategic prospect research can help you identify prospects. Thanks for reading – What ideas do you have for how you identify new donors?
Look at what arrived by special delivery today!
It’s an advance copy, meaning that for all of you who pre-ordered (and thank you for that, by the way!), yours will be arriving very soon.
If you haven’t already ordered it, now’s the time to get your very own copy hot off the presses! Just click that little book cover over there on the right to buy it at a discount (!). It will be on your doorstep in no time. This book has got everything anyone working in fundraising needs to know about prospect research. You’re going to love it.
Thank you to everyone who was involved: those who agreed to be interviewed, who were the subjects of case studies, who provided quotes and who read (and re-read!) drafts and offered sage advice and suggestions. And the biggest thank you to my co-author, the awesome Jen Filla.
