Mining Consumer Mindset Metrics With User-Generated Content

Raoul V. Kübler (), Susanne J. Adler, Lina Welke, Marko Sarstedt and Koen Pauwels
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Raoul V. Kübler: ESSEC Business School
Susanne J. Adler: Ludwig Maximilian University of Munich
Lina Welke: ESSEC Business School
Marko Sarstedt: Ludwig Maximilian University of Munich
Koen Pauwels: Northeastern University Boston

Schmalenbach Journal of Business Research, 2025, vol. 77, issue 3, 497-525

Abstract: Abstract In the wake of digital transformation, marketers gained access to large amounts of user-generated content and data in which consumers specifically mention and discuss brands, products, and services. This data offers rich information potential and may ultimately provide marketers with the ability to use this data pool to approximate survey-based consumer mindset metrics that mirror consumer attitudes alongside the different levels of the decision-making process. We argue that leveraging this potential may ultimately help marketers overcome common limitations of survey-based metrics and enable companies to observe and track mindset metrics that have been so far inaccessible due to financial and other constraints. To this end, we propose a four-step process that first identifies the key aspects of a mindset metric based on the existing body of developed constructs, then pinpoints potential data sources, and subsequently chooses an adequate data transformation tool.

Keywords: Mindset metrics; User-generated content; Construct validity; Machine learning; Customer decision journey (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s41471-025-00219-4

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