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Creating a product to segment donors and predict donor churn for the non-profit sector - data-driven decision making in NGO-s : improving data collection to refine donor segmentation
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Resumo(s)
The present work explores the application of Machine Learning in a Non-Governmental Organization(NGO) -as proof of the benefits of extending Artificial Intelligence to non-profits -to support fund raising actions, by identifying donors' similarities and churn probability. For an appropriate implementation, NGOs must guarantee abundance and quality of data. Data collection on contributors should be adequated to organizational needs, therefore the significance of socio-economic features is evaluated in this project. While their relevance to understand the donors' base is assured, their impact on performance can be obstructed by the quality of the data gathered-future improvements are suggested.
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Machine learning Business analytics Business and data analytics Segmentation Data collection Non-profit organizations