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<p>This paper demonstrates that automated segmentation of consumers based on Self-Determination Theory (SDT) is feasible using relatively simple text classification methods. By analyzing 290 Swiss German consumer interviews across seven industries, we apply computational linguistics to identify and segment consumers according to their motivational profiles derived from SDT and its derivative, Basic Psychological Needs Theory (BPNT). The study compares seven machine learning algorithms, including one deep learning classifier, from five different conceptual bases. This reveals that simpler and more interpretable classifiers, such as those based on logistic regression, distance measures, or decision trees, outperform more complex neural network classifiers. Although the interview data were designed to manually identify motivation, the algorithms used were not pre-trained on SDT or BPNT concepts. The findings indicate that with a well-prepared data foundation, simpler algorithms can achieve high performance, making advanced algorithm design expertise unnecessary. This approach provides a practical blueprint for marketing managers to leverage the growing volume of text and voice data for consumer segmentation.</p>

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Automated psychographic consumer segmentation: a text classification approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月11日

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  • Simone E. Griesser,
  • Patricia Feubli,
  • Douglas MacKevett

摘要

This paper demonstrates that automated segmentation of consumers based on Self-Determination Theory (SDT) is feasible using relatively simple text classification methods. By analyzing 290 Swiss German consumer interviews across seven industries, we apply computational linguistics to identify and segment consumers according to their motivational profiles derived from SDT and its derivative, Basic Psychological Needs Theory (BPNT). The study compares seven machine learning algorithms, including one deep learning classifier, from five different conceptual bases. This reveals that simpler and more interpretable classifiers, such as those based on logistic regression, distance measures, or decision trees, outperform more complex neural network classifiers. Although the interview data were designed to manually identify motivation, the algorithms used were not pre-trained on SDT or BPNT concepts. The findings indicate that with a well-prepared data foundation, simpler algorithms can achieve high performance, making advanced algorithm design expertise unnecessary. This approach provides a practical blueprint for marketing managers to leverage the growing volume of text and voice data for consumer segmentation.