Relationship mapping analytics for fundraising and sales prospect research

第三部门出版公司(TSP)已成功地向加拿大各地的筹款组织出售慈善活动订阅。这是融合了来自不同来源的大量数据的结果，前景研究人员在试图为他们的组织确定潜在捐赠者时发现这些数据是有用的。例如，TSP从Blackbaud，Inc.获得许可的加拿大数据包括超过730万条捐赠记录-个人、基金会和公司向哪些组织捐赠的记录。除了获得许可的数据外，还有大量公开可用的数据，这些数据将对慈善机构的订阅者有用。TSP将能够通过自动提取过程从网站提取这些数据。例如，加拿大的大多数律师事务所都会制作和发布免费的律师传记。TSP将能够 将这些传记添加到CharityCan平台上已经可以找到的越来越多的有用数据中。 CharityCAN面临的挑战是连接越来越多的数据点。关系映射是指识别个体之间的关系。当关系映射可以预测任何关系的强弱时，它就变得特别有用。CharityCan需要复杂的机器学习算法和数据挖掘工具来识别个人、私营部门公司和非营利性机构之间的关系，然后这些算法应该能够预测这些关系的强度(或缺乏)。其结果是，可以形成各种(复杂的)网络，并且在掌握这一点的情况下，可以使用一些混合聚类法来提取订阅机构或公司可能感兴趣的用户组(例如，用于个性化和有针对性的请求)。

Third Sector Publishing (TSP) has been successful selling CharityCAN subscriptions to fundraising organizations across Canada. This has been the result of incorporating a large volume of data from different sources that prospect researchers find useful as they attempt to identify potential donors for their organizations. For example, the Canadian data that TSP licenses from Blackbaud, Inc. includes over 7.3 million donation records - records of donations that individuals, foundations, and companies made to which organizations. As well as licensed data, there is an abundance of publicly available data that will be useful to CharityCAN subscribers. TSP will be able to extract this data from websites through automated extraction processes. For example, most law firms in Canada create and post for free biographies of their lawyers. TSP will be able to add these biographies to the growing volume of useful data that already can be found on the CharityCAN platform. The challenge for CharityCAN is connecting this growing number of data points. Relationship mapping refers to the identification of relationships among individuals. Relationship mapping becomes particularly useful when it can predict the strength or weakness of any relationship. CharityCAN requires sophisticated machine learning algorithms and data mining tools that will identify relationships among individuals, private-sector companies, and non-profit institutions, and then these algorithms should be able to predict the strength (or lack thereof) of these relationships. As a result, various (complex) networks could be formed and, with this in hand, some hybrid clustering methods could be used to extract groups of users that are potentially of interest to the subscribing institution or company (for example, for personalized and targeted solicitation).