If you are following along (and who wouldn’t be), we have reached the second step of my people-based analysis process. We have already determined how much our donor will contribute over the next few years. Now it is time to better understand who they are and how they engage.
Most organizations start and finish marketing segmentation with a recency, frequency, and monetary (RFM) assessment to analyze customer value. While it can effectively target your direct mail program, RFM does little to describe donors and considers only their financial engagement. If our goal is donor centricity, then we must move away from strictly RFM-based segmentation.
More advanced organizations are already using donor segments that go beyond RFM. Unfortunately, most of these segments are either bought off the shelf or focus only on demographics and wealth. While demographics and wealth are helpful in picturing the donor and buying traditional media (e.g., TV, print), there are two problems with basing your segmentation on them.
- Most donor files are demographically similar (mostly older, mostly women). Thus, your segments tell you little you did not already know and provide limited opportunity to build segment specific strategy.
- Traditional wealth data is often flawed and provides only minor benefit. Flaws aside, wealth scoring tells you who has money, but not who is willing to give it to you.
A better approach to analyzing customer value is multi-dimensional segmentation. Essentially, multi-dimensional segmentation combines donor actions (both financial and non-financial) with third-party data like demographics, wealth, consumer behavior, donations to other organizations, etc. Combining these multiple data sources via a cluster or decision tree analysis can provide crisp, actionable segments. The following example shows how this type of analysis can shine a light on new segments within a donor population.
Example: Nationally Recognized Health Charity
Through multi-dimensional segmentation, this national health charity identified four base segments within the donor population. Two of those segments were a surprise. By using this type of analysis, the organization discovered a group of low income donors who are highly responsive, who give consistently over years, and whose frequent, small gifts add up to significant revenue. They also discovered a high-income group in whom the organization invests heavily but whose members rarely respond. When its members do respond, they give small gifts. This added visibility into the characteristics and behavior of the donors allowed the organization to better understand and target the different segments of its donor population.
Now that you have developed actionable segments and can tier those segments by their predicted future value, you are ready to ask an often-ignored question: Why do they give to our organization? which I explore in my next post. Also join me for my upcoming webinar, Path to Donor Centricity: The Analytic First Steps.
In the meantime, please connect with me to discuss your answer to these crucial questions: What segmentation method do you use today? What would it take to move from that approach to a multi-dimensional one? Reach out at [email protected]
