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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

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  • Natalie Jane de Vries
  • Rodrigo Reis
  • Pablo Moscato

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.

Suggested Citation

  • Natalie Jane de Vries & Rodrigo Reis & Pablo Moscato, 2015. "Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-28, April.
    • Handle: RePEc:plo:pone00:0122133
    DOI: 10.1371/journal.pone.0122133
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    References listed on IDEAS

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    1. Andrew Robson & David Hart, 2019. "The post-Brexit donor: segmenting the UK charitable marketplace using political attitudes and national identity," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 16(2), pages 313-334, December.
    2. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    3. Eric Kolhede & J. Tomas Gomez-Arias, 2022. "Segmentation of individual donors to charitable organizations," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 19(2), pages 333-365, June.
    4. Leily Farrokhvar & Azadeh Ansari & Behrooz Kamali, 2018. "Predictive models for charitable giving using machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    5. Artur Wolak & Kamil Fijorek & Grzegorz Zając, 2020. "Professional Car Drivers’ Attitudes toward Technical, Marketing and Environmental Characteristics of Engine Oils: A Survey Study," Energies, MDPI, vol. 13(8), pages 1-14, April.
    6. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.

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