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基于信任、信心和捐赠行为对消费者进行聚类:澳大利亚非营利部门慈善参与的数据驱动型模型构建。

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

作者信息

de Vries Natalie Jane, Reis Rodrigo, Moscato Pablo

机构信息

Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia.

Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia; Faculdade de Medicina de Ribeirao Preto, Universidade de São Paulo, São Paulo, Brazil.

出版信息

PLoS One. 2015 Apr 7;10(4):e0122133. doi: 10.1371/journal.pone.0122133. eCollection 2015.

Abstract

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.

摘要

非营利组织和慈善部门面临着越来越激烈的竞争,要从共同的捐赠者群体中赢得时间、资金和精力。因此,这些组织需要比以往任何时候都更加积极主动。如今个人与组织之间沟通水平的提高,加剧了对慈善捐赠驱动因素进行调查并了解人群中不同消费者群体(即捐赠者细分群体)的需求。有人认为,“信任”是非营利部门生存的基石,这使其成为这一背景下不可避免的研究课题。慈善机构和非营利组织采用营利性组织的研究、营销和目标定位策略已变得势在必行。本研究为非营利部门提供了一种易于解释的细分方法,该方法基于一种新颖的无监督聚类技术(MST-kNN),随后是一种特征显著性方法(CM1分数)。对澳大利亚慈善与非营利委员会进行的一项调查中的1562名受访者样本进行分析,以揭示捐赠者细分群体。使用CM1分数确定每个聚类的最显著特征。此外,采用符号回归建模来找到特定聚类的模型,以预测聚类中的“低”或“高”参与度。MST-kNN方法发现了七个聚类。根据其显著特征,它们被标记为:“非制度主义慈善支持者”、“资源分配批评者”、“寻求信息的金融怀疑者”、“不质疑的慈善支持者”、“不信任的怀疑者”、“慈善管理信徒”和“制度主义慈善信徒”。每个聚类都展现出自身的特征以及不同的“参与”驱动因素。本研究中的方法为非营利部门提供了一个聚类、细分、理解并可能更好地定位其捐赠者群体的指导方针。如果慈善机构和非营利组织采用这些策略,它们将在当今竞争激烈的环境中更成功。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0f0/4388642/de4c060c769f/pone.0122133.g001.jpg

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