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Using Lexical Semantic Analysis to Derive Online Brand Positions: An Application to Retail Marketing Research

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  • Aggarwal, Praveen
  • Vaidyanathan, Rajiv
  • Venkatesh, Alladi

Abstract

This paper provides an innovative approach to brand tracking in the context of online retail shopping by deriving meaning from the vast amount of information stored in online search engine databases. The method draws upon research in lexical text analysis and computational linguistics to gain insights into the structural schema of online brand positions. The paper proposes a simple-to-use method that managers can utilize to assess their brand's positioning relative to that of their competitors’ in the online environment.

Suggested Citation

  • Aggarwal, Praveen & Vaidyanathan, Rajiv & Venkatesh, Alladi, 2009. "Using Lexical Semantic Analysis to Derive Online Brand Positions: An Application to Retail Marketing Research," Journal of Retailing, Elsevier, vol. 85(2), pages 145-158.
  • Handle: RePEc:eee:jouret:v:85:y:2009:i:2:p:145-158
    DOI: 10.1016/j.jretai.2009.03.001
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    References listed on IDEAS

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    Cited by:

    1. A. Fronzetti Colladon & F. Grippa & R. Innarella, 2021. "Studying the association of online brand importance with museum visitors: An application of the semantic brand score," Papers 2105.07749, arXiv.org.
    2. Fronzetti Colladon, Andrea, 2018. "The Semantic Brand Score," Journal of Business Research, Elsevier, vol. 88(C), pages 150-160.
    3. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.
    4. Benedicktus, Ray L. & Brady, Michael K. & Darke, Peter R. & Voorhees, Clay M., 2010. "Conveying Trustworthiness to Online Consumers: Reactions to Consensus, Physical Store Presence, Brand Familiarity, and Generalized Suspicion," Journal of Retailing, Elsevier, vol. 86(4), pages 322-335.
    5. Brown, James R. & Dant, Rajiv P., 2009. "The Theoretical Domains of Retailing Research: A Retrospective," Journal of Retailing, Elsevier, vol. 85(2), pages 113-128.
    6. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    7. Moon, Sangkil & Kamakura, Wagner A., 2017. "A picture is worth a thousand words: Translating product reviews into a product positioning map," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 265-285.
    8. Jaeger, Lena-Christin & Höhler, Julia, 2021. "Using word of mouth data from social media to identify asymmetric competition in food retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    9. Moon, Sangkil & Song, Reo, 2015. "The Roles of Cultural Elements in International Retailing of Cultural Products: An Application to the Motion Picture Industry," Journal of Retailing, Elsevier, vol. 91(1), pages 154-170.
    10. Ravula, Prashanth & Bhatnagar, Amit & Gauri, Dinesh K, 2023. "Role of gender in the creation and persuasiveness of online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    11. Grewal, Dhruv & Herhausen, Dennis & Ludwig, Stephan & Villarroel Ordenes, Francisco, 2022. "The Future of Digital Communication Research: Considering Dynamics and Multimodality," Journal of Retailing, Elsevier, vol. 98(2), pages 224-240.
    12. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
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