Revenue Prediction and Donor Segmentation are vital to ensuring that any NGO has the right tools to promote itself in this digital era so that they can bring in more donations and have a better notion of what they might get, allowing them to serve more people. According to historical statistics, this data provides numerous insights on the kind of people who should be addressed, the target audience, and the predicted donations that may be expected in the coming months. These insights enable NGOs to improve their attempts to attract new donors. Because it expressly allows univariate time series data with a seasonal component, revenue prediction will be performed by utilizing the SARIMA (Seasonal Auto Regressive Integrated Moving Averages) Model on previous monthly arriving donations to estimate future month wise donations of the NGO. On the donor dataset, donor segmentation is accomplished by combining RFM (Recency, Frequency, and Monetary Value) Analysis with K-Means because RFM Analysis is a data-driven segmentation technique that allows the NGO to make tactical decisions, and the K-means clustering algorithm is used to find groups that have not been explicitly labelled in the data. Any additional data may be readily allocated to the correct group once the algorithm has been run and the groups have been formed. This document provides an overview of revenue forecasting and donor segmentation for non-profits, as well as a technique for doing so. It will assist NGOs in making well-informed decisions.

NGOs Machine Learning SARIMA RFM Forecasting