Data Analytics - Optimizing Donor Segmentation Using Clustering Algorithms for Donor Retention
Author(s): Sriram Jasti, Deepthi Ravi
Publication #: 2602025
Date of Publication: 15.03.2025
Country: United States
Pages: 1-7
Published In: Volume 11 Issue 2 March-2025
DOI: https://doi.org/10.62970/IJIRCT.v11.i2.2602025
Abstract
Donor retention is the primary issue of non-profit sectors. Although individual and business donations play an important role in charity sustainability globally, retention rates are significantly lower than acquisition rates. Donor disengagement and loss are usually caused by the absence of personalization and the use of generic communication strategies. Data analytics in this sense gives a very effective framework on how to re-conceptualize fundraising, especially by using clustering algorithms to categorize donors. The key features like recency, frequency, monetary value (RFM) and demographic variables help organizations to identify meaningful donor segments including loyal supporters, occasional contributors, high-value donors, and at-risk individuals. The results demonstrate the way segmentation facilitates more focused fundraising appeals, resulting in enhanced donor engagement and retention. The methodology includes review of literature, analysis of clustering algorithms, and a case study of the application of these methods to a synthetic dataset. Findings have shown that segmentation can provide more actionable information compared to conventional broad-based segmentation.
Keywords: Donor segmentation, clustering algorithms, data analytics, donor retention, non-profit fundraising, k-means, DBSCAN, hierarchical clustering, recency frequency monetary (RFM), donor behavior.
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