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Integrating LLMs and Text Mining for Cost-Effective Marketing Intelligence: A Hospitality Industry Perspective

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  • Shawanluck Kunathikornkit

    (Srinakharinwirot University, Thailand)

  • Intaka Piriyakul

    (Srinakharinwirot University, Thailand)

  • Rapepun Piriyakul

    (Srinakharinwirot University, Thailand)

Abstract

As the volume of unstructured data on social media continues to grow, it is becoming increasingly important to have a proactive marketing strategy that can extract knowledge from this data. This study explores the use of large language models (LLMs) for detecting causal relations and analyzing significant themes in order to build models for marketing analysis. Four hundred sample reviews and contemporary techniques were used to create and test a causal graph, which showed good model fit. All paths in the causal network were found to be significant except for the one from customer experience to customer advocacy. The system identified three serial mediators: Exceptional Hospitality → Quality Lodging → Customer Experience → Enjoyable Time → Customer Advocacy, with an effect size of .0106. This research highlights the potential of linguistic data for developing mathematical models in marketing research and expands the scope of scientific inquiry in this field.

Suggested Citation

  • Shawanluck Kunathikornkit & Intaka Piriyakul & Rapepun Piriyakul, 2025. "Integrating LLMs and Text Mining for Cost-Effective Marketing Intelligence: A Hospitality Industry Perspective," International Journal of Knowledge Management (IJKM), IGI Global Scientific Publishing, vol. 21(1), pages 1-25, January.
    • Handle: RePEc:igg:jkm000:v:21:y:2025:i:1:p:1-25
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