Year: 2025
Type: article
Abstract: 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.
Source: Journal of Marketing Analytics
Institution: Lucerne University of Applied Sciences and Arts
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Award: G5037006239
OpenAlex ID: W4409366122