Want to (re-)discover your Donor Data? Let’s take a deep dive!

If you're not thinking segments, you're not thinking, Theodore Levitt once said. Fundraisers have traditionally put immense efforts in developing and tracking segments - so we are definitely not talking about something completely new for fundraisers. However, in times of Predictive AI and Data Science, advanced algorithms enable us to shed new light on the data of our supporters. In this blog post, we explore how advanced algorithms can generate new insights from donor data, improving targeted fundraising strategies. This deep dive into donor data segmentation reveals the importance of understanding and leveraging different segmentation techniques for optimal results.

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:

• Behavioral: Payment behavior, loyalty, lifetime value, etc.  - this is the "classic" and common denominator for many.
• Psychographic: Lifestyle, interests, attitudes, etc.
• Geographical: Country, region, topography, etc.
• Demographic: Age, income, gender, occupational group, etc.

These dimensions provide a framework for understanding donors' motivations and behaviors, which is essential for creating targeted and effective fundraising campaigns.

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:
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​​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:

• Recency: When did a donor last donate?
• Frequency: How often does a donor donate over a certain period?
• Monetary Value: How much do donors contribute per donation or a set time period such as a year?

This classic approach is easy to understand and implement, making it a popular choice for many organizations.

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:

1. Randomly select reference points in the data (e.g. a donor with her / his values for R, F and M)
2. Assign adjacent data to the closest reference point.
3. Move reference points to the data center.
4. Reassign data based on new affiliations.
5. Repeat until reference points are centered within correctly associated data.

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Comparing RFM vs. Unsupervised Learning
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Both RFM and unsupervised learning have their advantages and limitations:

• RFM: Simple, easy to implement, but may miss nuanced behavioral patterns.
• Unsupervised Learning: Flexible and data-driven, but can be challenging to interpret without additional effort.

Despite their differences, these approaches can coexist, providing a comprehensive understanding of donor behavior. By leveraging both methods, organizations can gain deeper insights and develop more effective fundraising strategies.

So What? A straightforward conclusion
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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!