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Machine Learning and Fundraising: Applications of Artificial Neural Networks

Author

Listed:
  • Diana Barro

    (Department of Economics, Ca' Foscari University of Venice)

  • Luca Barzanti

    (Department of Mathematics, University of Bologna)

  • Marco Corazza

    (Department of Economics, Ca' Foscari University of Venice)

  • Martina Nardon

    (Department of Economics, Ca' Foscari University of Venice)

Abstract

In fundraising management, the assessment of the expected gift is a key point. The availability of accurate estimates of the number of donations, their amounts, and the gift probability is relevant in order to evaluate the results of a fundraising campaign. The accuracy of the expected gift estimation depends on the appropriate use of the information about Donors. In this contribution, we propose a non-parametric methodology for the prediction of Donors' behavior based on Artificial Neural Networks. In particular, Multi-Layer Perceptron is applied. In the numerical experiments, the expected gift is then estimated based on a simulated dataset of Donors' individual characteristics and information on donations history.

Suggested Citation

  • Diana Barro & Luca Barzanti & Marco Corazza & Martina Nardon, 2023. "Machine Learning and Fundraising: Applications of Artificial Neural Networks," Working Papers 2023: 33, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2023:33
    as

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    References listed on IDEAS

    as
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    2. Duffy, John & Ochs, Jack & Vesterlund, Lise, 2007. "Giving little by little: Dynamic voluntary contribution games," Journal of Public Economics, Elsevier, vol. 91(9), pages 1708-1730, September.
    3. Cappellari, Lorenzo & Ghinetti, Paolo & Turati, Gilberto, 2011. "On time and money donations," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(6), pages 853-867.
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    5. Christian Gourieroux & Joann Jasiak, 2007. "Introduction to The Econometrics of Individual Risk: Credit, Insurance, and Marketing," Introductory Chapters, in: The Econometrics of Individual Risk: Credit, Insurance, and Marketing, Princeton University Press.
    6. Duncan, Brian, 1999. "Modeling charitable contributions of time and money," Journal of Public Economics, Elsevier, vol. 72(2), pages 213-242, May.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Fundraising Management; Donor's Profile; Gift Expectation; Artificial Neural Networks;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers

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