Why should we segment Donor Data? Segmentation is the process of dividing a market of individuals or organizations into subgroups based on common characteristics. This process allows organizations to align their products, services, and communication strategies with the specific needs of different segments. In the case of fundraising, by understanding what drives donor groups and their available choices, organizations can tailor their approaches for more effective engagement. Key Dimensions for Donor Segmentation Donor segmentation can be approached from various dimensions, including psychographic, geographical, behavioral, and demographic factors. Each dimension offers unique insights:
Of course, not all data necessary for the above-mentioned dimensions is available right away. Some of it is hard to obtain, only indirectly available, or not obtainable at all. In a stylized, way, the possibilites can be summarized as follows: Behavior-Based Segmentation with RFM One of the most effective methods for behavior-based segmentation is the RFM model, which evaluates donors based on Recency, Frequency, and Monetary value:
Unsupervised Learning for Donor Segmentation In addition, not necessarily as a replacement, unsupervised learning offers a more advanced technique for donor segmentation. Unlike RFM, which relies on predefined categories, unsupervised learning models detect patterns or groups within the data without prior labels. This method is highly flexible and can uncover hidden (sub-)segments that traditional methods might overlook. A simplified "cooking recipe" for a clustering approach like k-means looks as follows:
Comparing RFM vs. Unsupervised Learning
Both RFM and unsupervised learning have their advantages and limitations:
So What? A straightforward conclusion Unsupervised methods of donor segmentation are designed to incorporate various data types unbiasedly, offering a data-driven approach to understanding donor behavior. While these methods provide valuable insights, they also come with limitations, particularly regarding traceability and group stability over time. Ultimately, a combination of RFM and unsupervised learning techniques can yield the most comprehensive and actionable insights for donor-centered fundraising. Inspired? Interested? In need for a chat? Or are there experiences you can share? Please go ahead and do so and do not hesitate to reach out. All the best and have a great summer!
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September 2024
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