Data-Driven Precision and Selectiveness in Political Campaign Fundraising

The sophistication of political campaigns has dramatically increased over recent election cycles through the embracing of big data analytics. Collecting and storing information on actual and potential voters, volunteers, and donors has produced extensive databases that are then used to guide microtargeting efforts related to advertising, organizing, and fundraising efforts. But building, maintaining, and analyzing this data are costly in itself. This paper looks at two aspects of data-driven political fundraising: precision and selectiveness, with respect to the alignment of the ideology of potential donors and the ideology of the candidate. Specifically, the proposed model is used to determine the optimal precision in estimating a donor's location on the political ideology spectrum and the optimal targeting decision for fundraising solicitations given that estimate. Analysis of the model produces guidance for changes in solicitation cost and donation size. The results produced by the model are considered in light of the 2016 U.S. Presidential election. The analysis suggests that the Clinton campaign's larger donation size likely played a greater role than did the campaign's higher solicitation cost in terms of targeting. The model is also consistent with the Clinton campaign's higher expenditures on analytics, given their larger donation size and solicitation cost.