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A picture is worth a thousand words: Translating product reviews into a product positioning map

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  • Moon, Sangkil
  • Kamakura, Wagner A.

Abstract

Product reviews are becoming ubiquitous on the Web, representing a rich source of consumer information on a wide range of product categories (e.g., wines and hotels). Importantly, a product review reflects not only the perception and preference for a product, but also the acuity, bias, and writing style of the reviewer. This reviewer aspect has been overlooked in past studies that have drawn inferences about brands from online product reviews. Our framework combines ontology learning-based text mining and psychometric techniques to translate online product reviews into a product positioning map, while accounting for the idiosyncratic responses and writing styles of individual reviewers or a manageable number of reviewer groups (i.e., meta-reviewers). Our empirical illustrations using wine and hotel reviews demonstrate that a product review reveals information about the reviewer (for the wine example with a small number of expert reviewers) or the meta-reviewer (for the hotel example with an enormous number of reviewers reduced to a manageable number of meta-reviewers), as well as about the product under review. From a managerial perspective, product managers can use our framework focusing on meta-reviewers (e.g., traveler types and hotel reservation websites in our hotel example) as a way to obtain insights into their consumer segmentation strategy.

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  • 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.
    • Handle: RePEc:eee:ijrema:v:34:y:2017:i:1:p:265-285
    DOI: 10.1016/j.ijresmar.2016.05.007
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    References listed on IDEAS

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    Cited by:

    1. Francesca Negri, 2018. "A (Social Media) picture is worth a thousand words," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2018(4), pages 47-64.
    2. Vermeer, Susan A.M. & Araujo, Theo & Bernritter, Stefan F. & van Noort, Guda, 2019. "Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media," International Journal of Research in Marketing, Elsevier, vol. 36(3), pages 492-508.
    3. Tammo H.A. Bijmolt & Michel Wedel & Wayne S. DeSarbo, 2021. "Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(1), pages 1-15, June.
    4. Mike McGuirk, 2021. "Performing social media analytics with Brandwatch for Classrooms: a platform review," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 363-378, December.
    5. Klostermann, Jan & Plumeyer, Anja & Böger, Daniel & Decker, Reinhold, 2018. "Extracting brand information from social networks: Integrating image, text, and social tagging data," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 538-556.
    6. Roelen-Blasberg, Tobias & Habel, Johannes & Klarmann, Martin, 2023. "Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 164-188.
    7. Moon, Sangkil & Kim, Moon-Yong & Iacobucci, Dawn, 2021. "Content analysis of fake consumer reviews by survey-based text categorization," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 343-364.
    8. Moon, Sangkil & Kim, Moon-Yong & Bergey, Paul K., 2019. "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms," Journal of Business Research, Elsevier, vol. 102(C), pages 83-96.
    9. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    10. Moon, Sangkil & Jalali, Nima & Erevelles, Sunil, 2021. "Segmentation of both reviewers and businesses on social media," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    11. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    12. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    13. Mitra, Satanik & Jenamani, Mamata, 2020. "OBIM: A computational model to estimate brand image from online consumer review," Journal of Business Research, Elsevier, vol. 114(C), pages 213-226.
    14. Nima Jalali & Sangkil Moon & Moon-Yong Kim, 2023. "Profiling diverse reviewer segments using online reviews of service industries," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 130-148, June.

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