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Prediction in Marketing Using the Support Vector Machine

Citations

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Cited by:

  1. Julio López & Sebastián Maldonado & Ricardo Montoya, 2017. "Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1323-1334, November.
  2. Ana Suárez-Vázquez & José Quevedo, 2015. "Analyzing superstars’ power using support vector machines," Empirical Economics, Springer, vol. 49(4), pages 1521-1542, December.
  3. Maldonado, Sebastián & Montoya, Ricardo & Weber, Richard, 2015. "Advanced conjoint analysis using feature selection via support vector machines," European Journal of Operational Research, Elsevier, vol. 241(2), pages 564-574.
  4. Yupeng Chen & Raghuram Iyengar & Garud Iyengar, 2017. "Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis—A Sparse Learning Approach," Marketing Science, INFORMS, vol. 36(1), pages 140-156, January.
  5. Eugene J. S. Won, 2007. "—A Theoretical Investigation of the Effects of Similarity on Brand Choice Using the Elimination-by-Tree Model," Marketing Science, INFORMS, vol. 26(6), pages 868-875, 11-12.
  6. Lan Luo & P. K. Kannan & Brian T. Ratchford, 2007. "New Product Development Under Channel Acceptance," Marketing Science, INFORMS, vol. 26(2), pages 149-163, 03-04.
  7. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
  8. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2009. "Identifying winners of competitive events: A SVM-based classification model for horserace prediction," European Journal of Operational Research, Elsevier, vol. 196(2), pages 569-577, July.
  9. Ramasubramanian Sundararajan & Tarun Bhaskar & Abhinanda Sarkar & Sridhar Dasaratha & Debasis Bal & Jayanth K. Marasanapalle & Beata Zmudzka & Karolina Bak, 2011. "Marketing Optimization in Retail Banking," Interfaces, INFORMS, vol. 41(5), pages 485-505, October.
  10. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
  11. Mizuno, Makoto & Saji, Akira & Sumita, Ushio & Suzuki, Hideo, 2008. "Optimal threshold analysis of segmentation methods for identifying target customers," European Journal of Operational Research, Elsevier, vol. 186(1), pages 358-379, April.
  12. Kübler, Raoul V. & Colicev, Anatoli & Pauwels, Koen H., 2020. "Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 136-155.
  13. van Giffen, Benjamin & Herhausen, Dennis & Fahse, Tobias, 2022. "Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods," Journal of Business Research, Elsevier, vol. 144(C), pages 93-106.
  14. Olivier Toubia & John R. Hauser, 2007. "—On Managerially Efficient Experimental Designs," Marketing Science, INFORMS, vol. 26(6), pages 851-858, 11-12.
  15. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
  16. Julian Schuir & Frank Teuteberg, 2021. "Understanding augmented reality adoption trade-offs in production environments from the perspective of future employees: A choice-based conjoint study," Information Systems and e-Business Management, Springer, vol. 19(3), pages 1039-1085, September.
  17. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
  18. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
  19. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
  20. Jing Chen & Jose Humberto Ablanedo-Rosas & Gary L. Frankwick & Fernando R. Jiménez Arévalo, 2021. "The State of Artificial Intelligence in Marketing With Directions for Future Research," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 12(2), pages 1-26, July.
  21. Timothy J. Gilbride & Greg M. Allenby, 2006. "Estimating Heterogeneous EBA and Economic Screening Rule Choice Models," Marketing Science, INFORMS, vol. 25(5), pages 494-509, September.
  22. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
  23. Steven M. Shugan, 2009. "—Relevancy Is Robust Prediction, Not Alleged Realism," Marketing Science, INFORMS, vol. 28(5), pages 991-998, 09-10.
  24. 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.
  25. Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
  26. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.
  27. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2014. "Ensemble Learning for Cross-Selling Using Multitype Multiway Data," Working Papers 0155mss, College of Business, University of Texas at San Antonio.
  28. Gianni Di Pillo & Vittorio Latorre & Stefano Lucidi & Enrico Procacci, 2013. "An application of learning machines to sales forecasting under promotions," DIAG Technical Reports 2013-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
  29. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
  30. Stich, Lucas & Spann, Martin & Schmidt, Klaus M., 2022. "Paying for open access," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 273-286.
  31. Na Zhang & Karthik Kannan & George Shanthikumar, 2021. "Nudging a Slow‐Moving High‐Margin Product in a Supply Chain with Constrained Capacity," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 11-27, January.
  32. Díaz, Verónica & Montoya, Ricardo & Maldonado, Sebastián, 2023. "Preference estimation under bounded rationality: Identification of attribute non-attendance in stated-choice data using a support vector machines approach," European Journal of Operational Research, Elsevier, vol. 304(2), pages 797-812.
  33. Simone Guercini, 2022. "Scope of heuristics and digitalization: the case of marketing automation," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 21(2), pages 151-164, November.
  34. Ahmad, Shimi Naurin & Richard, Marie-Odile, 2014. "Understanding consumer's brand categorization across three countries: Application of fuzzy rule-based classification," Journal of Business Research, Elsevier, vol. 67(3), pages 278-287.
  35. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
  36. Hirche, Martin & Farris, Paul W. & Greenacre, Luke & Quan, Yiran & Wei, Susan, 2021. "Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship," Journal of Retailing, Elsevier, vol. 97(4), pages 697-714.
  37. 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.
  38. 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.
  39. Dhaoui, Abderrazak & Audi, Mohamed & Ouled Ahmed Ben Ali, Raja, 2015. "Revising empirical linkages between direction of Canadian stock price index movement and Oil supply and demand shocks: Artificial neural network and support vector machines approaches," MPRA Paper 66029, University Library of Munich, Germany.
  40. G. Di Pillo & V. Latorre & S. Lucidi & E. Procacci, 2016. "An application of support vector machines to sales forecasting under promotions," 4OR, Springer, vol. 14(3), pages 309-325, September.
  41. Shimi Naurin Ahmad & Michel Laroche, 2023. "Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 662-676, December.
  42. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
  43. Ana Suarez-Vazquez & Elena Montañés-Roces, 2017. "Superstars Power, Mining the Paths to Stars’ Persuasion," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 67-81, January.
  44. Jose Ramon Saura & Daniel Palacios-Marqués & Domingo Ribeiro-Soriano, 2023. "Leveraging SMEs technologies adoption in the Covid-19 pandemic: a case study on Twitter-based user-generated content," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1696-1722, October.
  45. Olivier Toubia & Laurent Florès, 2007. "Adaptive Idea Screening Using Consumers," Marketing Science, INFORMS, vol. 26(3), pages 342-360, 05-06.
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