donor modeling Archives - Helen Brown Group

September 19, 2013 By Helen Brown

Fundraising Analytics ABCs – Visualizing Data

What are the best representations of information that you’ve ever seen? For most people, they’re the ones that are the simplest to understand. The ones that display information clearly and move people to action. Or inaction:

Even in a different language, if we understand the context, the message is still clear.

That’s what good data visualization does. It takes information and lays it out in the simplest of terms to get across what needs to be communicated.

KISS

There’s no getting around the fact that data analytics is intimidating for most of us, and that it produces a lot of really interesting factoids that can move people to action. But when you’re trying to get across complex information, there are still ways to keep messaging simple. In fact, the more complex the information, the more imperative it is to KISS (Keep It Simple, Stupid). Or, in the words of Joey Cherdarchuk, director of operations at DarkHorse Analytics, Data Looks Better Naked.

THE MASTER AT WORK

If you haven’t seen Hans Rosling’s fascinating TED Talks using his free software GapMinder or watched his fascinating holographic data visualization on YouTube, you really should take the time to be impressed. And inspired.

Even if you’re not a data analytics person, watching Dr. Rosling will help you think about presenting information better, whether you’re a frontline fundraiser, researcher, social media guru, consultant or …just about anyone. Okay, go watch those two videos and then come back and we’ll talk more about available resources for visualizing data.

RESOURCES

Now that you’re inspired to do something different, let’s talk about what’s available for manipulating and showing off the great conclusions (or continuing questions) that your data is begging you to share.

Enter the graph, the moving chart, the GIF, the mini movie, the podcast, and the interactive illustration, to name a few, all made possible by recent entries into the market from companies including (in alpha order) Advizor Solutions, a flexible visualization tool that is especially good for looking at annual giving results and gift officer portfolio performance; Factary, whose product Atom allows you to visualize relationships between people, organizations and entities; Rapid Insight, a modeling and illustration package; and Tableau Software, an interactive visualization tool that allows for on-the-fly graphic shifting and drill-down-into-the-data capability.

What’s free?

NodeXL, which adheres to Excel like an elbow-length glove to allow you to manipulate and visualize relationships and social media interactions. Hans Rosling’s Big Data manipulator, GapMinder, which he uses in the aforementioned videos, is also completely free to download. And there are lots of others.

Where are some examples of visualizations?

Here is a terrific collection of resources from Andy Kirk, a UK-based freelance data visualization design consultant. And Mike Bostock of The New York Times has pulled together a great compilation of compelling visualization tools.

Finally, we’d like to leave you with a quote by David Schmitt, head of Performance Strategy and Planning Analytics in the finance division at the Intercontinental Hotels Group (IHG). Schmitt’s team is responsible for communicating information about IHG’s financial performance in a way that is entertaining, compelling and clear. You wouldn’t think that corporate financials would lend themselves to music videos, but that’s one of the tools Schmitt’s group has used to visualized data. For him, “data isn’t the point; numbers aren’t the point—it’s about the idea.”

It looks like our Excel pie chart days are probably over, thank goodness. We’d love to hear about any tools, resources or sites that you’ve found to help you visualize data, too! Please share them here in Comments, so that all of us can check them out.

September 12, 2013 By Helen Brown

Fundraising Analytics ABCs – Donor Modeling

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:

Annual giving prospects who are most likely to renew
People who are likely to be good board/volunteer candidates
Planned giving prospects
People who would be great prospects for a specific project or campaign (like a library fund, or for endowment)
People who would be most likely to accept a request for a visit
Top level annual giving prospects
Prospects best suited for a particular gift officer or volunteer

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.

September 5, 2013 By Helen Brown

Fundraising Analytics ABCs – Data Mining

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 5^th and 6^th 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:

Determining the best solicitation methods for donor acquisition, renewal, or upgrade
Measuring the characteristics of event attendees who later become donors
Understanding the clusters of members/grateful patients/families/alumni/docents who respond better to e-mail, direct mail, phone calls, or social media
Finding the best pattern for the cultivation/giving ladder
Adding or dropping solicitation methods, or venues
Assessing timing, including how long it takes to successfully solicit gifts at different levels

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:

clustering like-minded, or like-attributed prospects for a cultivation dinner
breaking long-held “truths” about your donor base, such as “our best athletics prospects are male football alumni.” What if that’s not actually true?
looking at what statistics calls “interactions” – the combination of characteristics that make good prospects, members, volunteers, trustees, etc. For example: Married prospects and/or prospects living in rural areas show a mild relationship to loyal giving. However, prospects who are married AND live in rural areas show a strong relationship to loyal giving.
determining which group responds best to email and which to social media.

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.

September 2, 2013 By Helen Brown

What if fundraisers reported to the Director of Strategic Information?

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.

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