Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

The reduction in government support and the rapid growth number of nonprofit organizations have made them face fierce competition for charitable donations. Identifying valuable donors developing effective marketing strategies can contribute to online donation platforms. This study explored donors' characteristics Taiwan through identification different donor segments using a refined clustering algorithm. Furthermore, based on salient features each segment are offered retain maximize their monetary A real dataset derived from 14,029 records contributed by 7,432 during years 2016-2018 an platform were collected. cluster analysis improved particle swarm optimization algorithm was applied according RFM (Recency, Frequency, Monetary) values socio-demographic variables (e.g., Sex, Age, Education). results four Taiwan. “Passive donors” found be largest (38%), followed “female active (24%), “potential (21%), “male loyal (17%). Most female, highly educated, aged between 30 40. men's single amount higher than women's; however, women's total donations men's. We segmentation process with technique, which combines as criteria compensate shortcomings previous studies that only focused RFM. Longitudinal data instead questionnaire survey used analyze profiles