A NEW SERIES FROM JESSICA NENO CLOUD
This is the first in a series on donor acquisition by Jessica Neno Cloud, CFRE, the author of Successful Fundraising Calls: A Phonathon Scripting Workshop. Cloud is the assistant director for fundraising initiatives and planned giving at the University of Southern Mississippi Foundation. She practices evidence-based fundraising with a focus on return on investment, and has a variety of innovative and effective techniques to share.
by Jessica Neno Cloud (The University of Southern Mississippi Foundation)
Higher education fundraisers have the benefit of enjoying a natural constituency. We don’t have to go out like a small non-profit, buying the list for a niche magazine and hoping to build up a database mailing by mailing. Our industry graduates more constituents each semester and to one degree or another, they all have some built-in affinity for our institutions.
This can be a bit of a double-edged sword though. While we don’t have to hunt for prospects, it is often true that we often have more people in our database than we can possibly reach.
This is where data-mining comes in.
It’s crucial to understand that blanket mailings to every prospect in our database can quickly become a money-losing strategy. We need to be smart and selective about how we undertake our acquisition work in direct mail. Data mining is a tool for doing that.
You can reach the best prospects within your database with the most attractive ask ladder by testing different ways to prioritize your phone calls and target your mailings. This can result not only in more dollars raised from more donors but also save program operation costs.
THE DIFFERENCE BETWEEN DATA MINING AND SEGMENTATION
Many people use the term “data mining” interchangeably with “segmentation.” While they are related, segmentation and data mining are not the same thing.
Segmentation involves breaking your entire pool of prospects into smaller groups, based on demographic or giving similarities, in order to utilize targeted messaging or for testing and statistical analysis purposes. For example, breaking your fall mailers into groups based on donor type (lybunt, sybunt, etc.) or by college in order to use different letter versions or so that you can report results easily to on-campus clients (or both).
Data mining involves using data points in your database to limit who is solicited, thereby saving costs and improving effectiveness. This can also include using information in your database to go beyond segmentation to create customized ask ladders for prospects within segments in order to maximize returns.
Data Mining for Direct Mail
The strategy for direct mail acquisition is to mail to right people: those most likely to respond. Don’t waste budgetary dollars (printing costs, postage, processing, etc.) on those who won’t even bother opening the envelope. The question is: how can we possibly know that, since we don’t have the benefit of giving history on a future donor record?
If your organization screens the database to get likelihood scores, wealth scores or does any kind of predictive modeling, you can use those data points to find the best acquisition prospects for your mail program. It could be as simple as saying, “With my budget I can mail to 7,000 acquisition prospects, so I want a list of the 7,000 most highly rated non-donors in the database.”
Again, you can take that strategy to the next level if you have any scores that indicate wealth or capacity and use those scores to guide customized ask amounts on your reply cards.
For institutions that have not done any specialized screening, there are still ways to pinpoint the most likely prospects for an acquisition mailing. Some colleges and universities come up with their own formulas for “engagement scores” by taking a combination of markers for affinity and involvement and boiling them down into a quantitative score.
Even if you cannot append a specially created score to records, as in the example above, you can still test various engagement criteria and see which ones have an impact on the result. Just be clear with your data team as to the priority of those groups when they are pulling them and be clear with your codes for tracking and reporting.