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Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data

Lisa Schetgen (UGent) , Matthias Bogaert (UGent) and Dirk Van den Poel (UGent)
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Abstract
The purpose of this study is to demonstrate the value of Facebook data in predicting first-time donation behavior. More specifically, we provide evidence that Facebook data can be used as a valuable data source for nonprofit organizations in acquiring new donors. To do so, we evaluate three different dimensionality reduction techniques (i.e., singular value decomposition, non-negative matrix factorization, and latent Dirichlet allocation) over seven classification techniques (i.e., logistic regression, k-nearest neighbors, bagged trees, random forest, adaboost, extreme gradient boosting, and artificial neural networks) using five times twofold cross-validation. Next, we assess what type of Facebook data and which predictors are most important. The results indicate that we can predict first-time donation behavior based on Facebook data with high predictive performance. Our benchmark indicates that the combination of singular value decomposition and logistic regression outperforms all other analytical methodologies with an area under the receiver operating characteristic of 0.72 and a top decile lift of 3.33. The results show that Facebook pages and categories of Facebook pages are the most important data types. The most important predictors are dimensions related to age, education, residence, materialism, responsible consumption, and interest in nonprofits. The presented acquisition models can be used by nonprofit organizations to implement a one-to-one targeted marketing campaign towards Facebook fans. To the best of our knowledge, our study is the first to determine the predictive value of Facebook data for nonprofits in a real-life acquisition context.
Keywords
Facebook, CRM, Customer acquisition, Predictive analytics, Social media

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Citation

Please use this url to cite or link to this publication:

MLA
Schetgen, Lisa, et al. “Predicting Donation Behavior : Acquisition Modeling in the Nonprofit Sector Using Facebook Data.” DECISION SUPPORT SYSTEMS, 2020, doi:10.1016/j.dss.2020.113446.
APA
Schetgen, L., Bogaert, M., & Van den Poel, D. (2020). Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data. DECISION SUPPORT SYSTEMS. https://doi.org/10.1016/j.dss.2020.113446
Chicago author-date
Schetgen, Lisa, Matthias Bogaert, and Dirk Van den Poel. 2020. “Predicting Donation Behavior : Acquisition Modeling in the Nonprofit Sector Using Facebook Data.” DECISION SUPPORT SYSTEMS. https://doi.org/10.1016/j.dss.2020.113446.
Chicago author-date (all authors)
Schetgen, Lisa, Matthias Bogaert, and Dirk Van den Poel. 2020. “Predicting Donation Behavior : Acquisition Modeling in the Nonprofit Sector Using Facebook Data.” DECISION SUPPORT SYSTEMS. doi:10.1016/j.dss.2020.113446.
Vancouver
1.
Schetgen L, Bogaert M, Van den Poel D. Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data. DECISION SUPPORT SYSTEMS. 2020;
IEEE
[1]
L. Schetgen, M. Bogaert, and D. Van den Poel, “Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data,” DECISION SUPPORT SYSTEMS, 2020.
@article{8681585,
  abstract     = {The purpose of this study is to demonstrate the value of Facebook data in predicting first-time donation behavior. More specifically, we provide evidence that Facebook data can be used as a valuable data source for nonprofit organizations in acquiring new donors. To do so, we evaluate three different dimensionality reduction techniques (i.e., singular value decomposition, non-negative matrix factorization, and latent Dirichlet allocation) over seven classification techniques (i.e., logistic regression, k-nearest neighbors, bagged trees, random forest, adaboost, extreme gradient boosting, and artificial neural networks) using five times twofold cross-validation. Next, we assess what type of Facebook data and which predictors are most important. The results indicate that we can predict first-time donation behavior based on Facebook data with high predictive performance. Our benchmark indicates that the combination of singular value decomposition and logistic regression outperforms all other analytical methodologies with an area under the receiver operating characteristic of 0.72 and a top decile lift of 3.33. The results show that Facebook pages and categories of Facebook pages are the most important data types. The most important predictors are dimensions related to age, education, residence, materialism, responsible consumption, and interest in nonprofits. The presented acquisition models can be used by nonprofit organizations to implement a one-to-one targeted marketing campaign towards Facebook fans. To the best of our knowledge, our study is the first to determine the predictive value of Facebook data for nonprofits in a real-life acquisition context.},
  articleno    = {113446},
  author       = {Schetgen, Lisa and Bogaert, Matthias and Van den Poel, Dirk},
  issn         = {0167-9236},
  journal      = {DECISION SUPPORT SYSTEMS},
  keywords     = {Facebook,CRM,Customer acquisition,Predictive analytics,Social media},
  language     = {eng},
  title        = {Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data},
  url          = {http://dx.doi.org/10.1016/j.dss.2020.113446},
  year         = {2020},
}

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