Focus On: Database Analysis: A Closer Look at Models
The past few years have proven to be challenging for direct marketing fundraising. When you consider the 2001 terror attacks and the ensuing questions about dispersement of funds contributed as a result, Anthrax scares, the war on terrorism, corporate distrust, the recent Catholic Church opprobrium and the troubled economy, it’s no surprise that fundraisers have felt left out in the cold.
A closer look within the fundraising sector finds that the size of the American donor population is shrinking at an alarming rate, as well, with traditional donor segments being impacted the most. Donors cite “trust” (or lack thereof) as the primary reason for their change of heart, and confidence in nonprofits has fallen to an alarming low. As a result, many donors have scaled back the number of charities that they support.
As outside forces make it increasingly difficult to balance success benchmarks (i.e., response rate, average gift, etc.) with volume goals, fundraisers are recognizing that achieving gross revenue objectives at the expense of profitability is a losing battle. Past success has conditioned many of us to increase mail volume to reach our targeted goals, but today’s fundraising environment no longer supports such a practice.
What we need is something to help us better align marketing investments with potential donor value to maximize profitability. As Ginny Renehan, associate development director for the Zoological Society of San Diego, has experienced, “Adding analytic modeling … strategies has proven to be very effective both in cost savings and results.”
Letting go of the past
Nonprofits have begun to recognize that sophisticated analytics provide much of the intelligence necessary to drive targeted, donor-centric management. However, the vast majority of nonprofits continue to embrace the more familiar RFA (recency, frequency, gift amount) approach to donor segmentation.
RFA is borrowed from the catalog industry, which was credited with pioneering the use of RFM (recency, frequency, monetary value) prior to the introduction of the computer. Generally speaking, RFA is used to segment donors based on a few key components of past behavior (often direct mail behavior) to predict future activity. Although simple and often subjective, RFA is an effective segmentation model that combines several pieces of information to form relatively homogeneous groups of donors that can be ranked from best to worst.