Fundraising: The Science

Now that we’ve discussed the art of fundraising, I think it’s only right that we focus a little bit on the science. After all, knowing which prospects are statistically most likely to give makes a gift officer’s contribution to the art of fundraising that much more successful. As I’ve mentioned before, my function in the fundraising spectrum is as an analyst, helping customers build models identifying which prospects are most likely to donate.

One of the most important things we do during the predictive modeling process is data preparation, which often means creating new variables from the data our customers have on-hand. I’d like to discuss some of these variables, as well as how and why to include them in a fundraising or advancement model. For the purposes of this blog entry, I’ll use a higher education example. Typically a higher education institution might have some extra variables, but these can be tailored to fit other institutions or excluded when not relevant.

Demographic Information          

It’s always smart to have an idea of what each donor looks like at a demographic level. Variables to include here are things like age, gender, marital status, and any occupational data you might have available to you. In a higher education context, this would also include things like the constituent’s class year, major, whether or not their spouse is an alumni, and whether they are a legacy alumni (meaning a parent or grandparent also attended the institution). Additionally, we often create a “reunion year flag” indicating if the analysis year is a reunion year for that person, as donors are often more likely to give (and give larger gifts) during a reunion year.

Location Information

General information about each donor’s location like ZIP code, city, and county can be useful as categorical variables (treating people that live in each one as a group). Once we have a ZIP code, we always calculate a “distance from institution” variable using one of Veera’s pre-programmed functions. This new variable, which is measured in miles, gives you a solid idea of the relationship between location and giving. If you have access to census data, we recommend appending variables relating to neighborhood or housing type. Creating flag variables for wealthy neighborhood ZIP codes can also be useful; constituents coming from these areas may be more likely to give. Although this can be created at a more local level, we often start with Forbes’ list of the top 500 wealthiest ZIP codes in the US, which is available online at http://www.forbes.com/lists/2011/7/zip-codes-11_rank.html.

Contact History

The ways in which a donor engages with you can tell you a lot about their likelihood of giving. For starters, include variables pertaining to their event history. How many events have they attended? Which types of events are they attending? How many days since their last event? Answers to questions like these can sometimes turn out to be predictive of giving. This is also where your social media variables come into play; create flags for whether a constituent is following you on LinkedIn, Facebook, Twitter, Pinterest, etc. A donor following you on one or more of these sites is an indication that they want to be connected, and therefore they may be more likely to give. Conversely, if a constituent has indicated that they do not want to be contacted, you’ll want to include this information as well, as it can be very predictive.

Gift History

This brings us to our last and most predictive set of variables: giving history. These variables should answer all kinds of questions about what a giver looks like historically, like:

• How many gifts have they given in their lifetime?
• What was their last gift?
• How many days since their first gift? How many days since their last gift?
• Have they given in the past 12 months? If so, how much?
• What is the velocity of the gifts - are the increasing, decreasing, or staying the same?

One thing to note here is that gift dates themselves aren’t useful in a predictive model, but their translations – like the number of days since an event – allow us to use the insight they provide.

In building a predictive model, some of these variables may be predictive, while others might turn out not to be. It’s a good idea to include some combination of these variables, plus anything you have on-hand that you think could possibly be predictive.

-Caitlin Garrett, Statistical Analyst at Rapid Insight