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

Abstract

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

Keywords

• Online donor segmentation
• donation marketing strategies
• socio-demographic factors
• RFM