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Cultivating Disaster Donors Using Data Analytics

Author

Listed:
  • Ilya O. Ryzhov

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Bin Han

    (Applied Mathematics, Statistics, and Scientific Computation, University of Maryland, College Park, Maryland 20742)

  • Jelena Bradić

    (Department of Mathematics, University of California, San Diego, La Jolla, California 92093)

Abstract

Nonprofit organizations use direct-mail marketing to cultivate one-time donors and convert them into recurring contributors. Cultivated donors generate much more revenue than new donors, but also lapse with time, making it important to steadily draw in new cultivations. The direct-mail budget is limited, but better-designed mailings can improve success rates without increasing costs. We propose an empirical model to analyze the effectiveness of several design approaches used in practice, based on a massive data set covering 8.6 million direct-mail communications with donors to the American Red Cross during 2009–2011. We find evidence that mailed appeals are more effective when they emphasize disaster preparedness and training efforts over post-disaster cleanup. Including small cards that affirm donors’ identity as Red Cross supporters is an effective strategy, whereas including gift items such as address labels is not. Finally, very recent acquisitions are more likely to respond to appeals that ask them to contribute an amount similar to their most recent donation, but this approach has an adverse effect on donors with a longer history. We show via simulation that a simple design strategy based on these insights has potential to improve success rates from 5.4% to 8.1%. This paper was accepted by Serguei Netessine, operations management.

Suggested Citation

  • Ilya O. Ryzhov & Bin Han & Jelena Bradić, 2016. "Cultivating Disaster Donors Using Data Analytics," Management Science, INFORMS, vol. 62(3), pages 849-866, March.
  • Handle: RePEc:inm:ormnsc:v:62:y:2016:i:3:p:849-866
    DOI: 10.1287/mnsc.2015.2149
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    2. Bin Han & Ilya O. Ryzhov & Boris Defourny, 2016. "Optimal Learning in Linear Regression with Combinatorial Feature Selection," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 721-735, November.
    3. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.

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