We describe a novel approach for predicting political donations and performing psychographic segmentation based on social data linked to election donation records. The role of microblogs in enterprise informatics, specifically in relation to customer relationship systems is highlighted. Algorithms trained on social data can be used to interpret and detect prospects' psychographic information. Contrasted with past approaches which focused exclusively on a single source of social data, the method being presented allows us to use an objective gold standard by linking Twitter and election records. Two experiments were conducted using data collected from 438 Twitter users, half of which are linked with donation event records collected from the United States Federal Election Commission. Probabilistic, entropy and kernel approaches were tested for predictive accuracy, while the CNG technique is explored as an alternative. The CNG algorithm was found to predict political affiliation 17 percentage points above the majority classifier, exceeding benchmarks suggested by the literature. A Naive Bayes word n-gram approach was found to outperform CNG at predicting donations by predicting political donations. Insufficient performance and poor reliability of standard word n-gram techniques in opinion detection reveal skepticism about past work on political affiliation analysis from social data alone. This suggests that prospecting systems may benefit from constructing algorithms using data linked to external sources.