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Clustering consumers based on trust, confidence and giving behaviour: data-driven model building for charitable involvement in the Australian not-for-profit sector

机译:基于信任,信心和奉献行为将消费者聚集在一起:建立数据驱动的模型以促进澳大利亚非营利部门的慈善参与

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摘要

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment.
机译:非营利和慈善部门的组织面临越来越多的竞争,以争取时间,金钱和共同捐助者的努力。因此,这些组织需要比以往更加主动。如今,个人与组织之间的沟通水平越来越高,这就需要调查慈善捐赠的驱动力并了解人群中各个消费者群体或捐赠者群体。有人认为,“信任”是非营利部门生存的基石,这使其成为在这种情况下进行研究的必然主题。慈善机构和非营利组织必须采用营利性的研究,营销和针对性策略。这项研究为非营利部门提供了一种基于新的无监督聚类技术(MST-kNN)和特征显着性方法(CM1评分)的易于解释的细分方法。对澳大利亚慈善与非营利委员会进行的一项调查中的1,562名受访者进行了抽样分析,以揭示捐赠者群体。每个群集的最显着特征是使用CM1分数来识别的。此外,使用符号回归建模来找到特定于群集的模型,以预测群集中“低”或“高”的参与度。 MST-kNN方法发现了七个聚类。根据他们的显着特征,他们被标记为:“非制度主义慈善支持者”,“资源分配批评家”,“寻求信息的金融怀疑论者”,“不质疑慈善组织的支持者”,“不信任怀疑论者” ”,“慈善管理信徒”和“制度主义慈善信徒”。每个集群都展现出自己的特征以及“参与”的不同驱动力。本研究中的方法为非营利部门提供了更好地聚集,细分,理解并有可能更好地针对其捐助者基础的指南。如果慈善机构和非营利组织采用这些策略,那么它们将在当今竞争激烈的环境中取得更大的成功。

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