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A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries

Citations

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Cited by:

  1. Savannah Wei Shi & Michael Trusov, 2021. "The Path to Click: Are You on It?," Marketing Science, INFORMS, vol. 40(2), pages 344-365, March.
  2. Carl F. Mela & Jason M. T. Roos & Tulio Sousa, 2023. "Advertiser Learning in Direct Advertising Markets," Papers 2307.07015, arXiv.org, revised Jul 2025.
  3. Jia Liu & Olivier Toubia, 2020. "Search query formation by strategic consumers," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 155-194, June.
  4. Ruomeng Cui & Meng Li & Qiang Li, 2020. "Value of High-Quality Logistics: Evidence from a Clash Between SF Express and Alibaba," Management Science, INFORMS, vol. 66(9), pages 3879-3902, September.
  5. Hyowon Kim & Greg M. Allenby, 2022. "Integrating Textual Information into Models of Choice and Scaled Response Data," Marketing Science, INFORMS, vol. 41(4), pages 815-830, July.
  6. Yuping Liu-Thompkins & Shintaro Okazaki & Hairong Li, 2022. "Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1198-1218, November.
  7. Peiyao Li & Noah Castelo & Zsolt Katona & Miklos Sarvary, 2024. "Frontiers: Determining the Validity of Large Language Models for Automated Perceptual Analysis," Marketing Science, INFORMS, vol. 43(2), pages 254-266, March.
  8. Kaatz, Christopher & Brock, Christian & Figura, Lilli, 2019. "Are you still online or are you already mobile? – Predicting the path to successful conversions across different devices," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 10-21.
  9. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
  10. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.
  11. Daria Dzyabura & Renana Peres & Irina Linevich, 2025. "Color Analytics for Data-Driven Brand Communications," Working Papers w0292, New Economic School (NES).
  12. Yang Liu, 2024. "Analyzing the effect of user‐generated content on studio performance: A combined approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(4), pages 2228-2248, June.
  13. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.
  14. Peter Landry, 2021. "Keywords, limited consideration, and organic product listings," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 505-566, December.
  15. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
  16. Nicolas Padilla & Eva Ascarza & Oded Netzer, 2025. "The customer journey as a source of information," Quantitative Marketing and Economics (QME), Springer, vol. 23(3), pages 379-418, September.
  17. Xinyu Liu & Yezheng Liu & Yang Qian & Yuanchun Jiang & Haifeng Ling, 2025. "Learning consumer preferences through textual and visual data: a multi-modal approach," Electronic Commerce Research, Springer, vol. 25(4), pages 2955-2984, August.
  18. 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.
  19. Martin Reisenbichler & Thomas Reutterer & David A. Schweidel & Daniel Dan, 2022. "Frontiers: Supporting Content Marketing with Natural Language Generation," Marketing Science, INFORMS, vol. 41(3), pages 441-452, May.
  20. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
  21. Herhausen, Dennis & Ludwig, Stephan & Abedin, Ehsan & Haque, Nasim Ul & de Jong, David, 2025. "From words to insights: Text analysis in business research," Journal of Business Research, Elsevier, vol. 198(C).
  22. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
  23. Jia Liu & Olivier Toubia & Shawndra Hill, 2021. "Content-Based Model of Web Search Behavior: An Application to TV Show Search," Management Science, INFORMS, vol. 67(10), pages 6378-6398, October.
  24. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
  25. Hongshuang (Alice) Li, 2022. "Converting free users to paid subscribers in the SaaS context: The impact of marketing touchpoints, message content, and usage," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2185-2203, May.
  26. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
  27. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
  28. Jiang, Zhe & Liu, Ning & Zhang, Xiaobing & Chu, Yanlai & Zhang, Lin, 2025. "Proactive social learning and green product consumption: Evidence from new energy vehicle sales in China," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
  29. Shah Jahan Miah & Huy Quan Vu & Damminda Alahakoon, 2022. "A social media analytics perspective for human‐oriented smart city planning and management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 119-135, January.
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