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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2bq67muntvahlgzwn7lfoncyt4" style="color: black;">Business & Information Systems Engineering</a>
Classification analysis contributes to the support of several corporate decision making tasks. In particular, the domain of customer relationship management comprises a variety of respective applications, which involve estimating some aspects of customer behavior. Whereas classical statistical techniques as well as decision tree models are routinely employed to approach such tasks, the use of modern techniques is still in its infancy. A major obstacle prohibiting a wider adoption of novel<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12599-010-0094-8">doi:10.1007/s12599-010-0094-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ku6yftrmmveuhm6urngnqnmfji">fatcat:ku6yftrmmveuhm6urngnqnmfji</a> </span>
more »... s may be seen in the fact that their potential to improve decision quality in customer-centric settings has not yet been investigated. Therefore, this paper contributes to the literature by conducting an empirical study that compares the performance of modern to that of established classifiers as to their predictive accuracy and economic consequences. The observed results provide strong evidence for the value of modern techniques and identify one approach which appears to be particularly well suited for solving customer-centric classification problems.
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