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Journal of the Academy of Marketing Science

, Volume 46, Issue 4, pp 557–590 | Cite as

Unstructured data in marketing

  • Bitty Balducci
  • Detelina MarinovaEmail author
Review Paper

Abstract

The rise of unstructured data (UD), propelled by novel technologies, is reshaping markets and the management of marketing activities. Yet these increased data remain mostly untapped by many firms, suggesting the potential for further research developments. The integrative framework proposed in this study addresses the nature of UD and pursues theoretical richness and computational advancements by integrating insights from other disciplines. This article makes three main contributions to the literature by (1) offering a unifying definition and conceptualization of UD in marketing; (2) bridging disjoint literature with an organizing framework that synthesizes various subsets of UD relevant for marketing management through an integrative review; and (3) identifying substantive, computational, and theoretical gaps in extant literature and ways to leverage interdisciplinary knowledge to advance marketing research by applying UD analyses to underdeveloped areas.

Keywords

Unstructured data Machine learning Deep learning Artificial intelligence Nonverbal Image Video Voice Text Linguistics Acoustic Big data Text mining 

References

  1. Aaker, J. L. (1997). Dimensions of brand personality. Journal of Marketing Research, 34(3), 347–356.Google Scholar
  2. Aggarwal, P., Vaidyanathan, R., & Venkatesh, A. (2009). Using lexical semantic analysis to derive online brand positions: An application to retail marketing research. Journal of Retailing, 85(2), 145–158.Google Scholar
  3. Al-nasheri, A., Muhammad, G., Alsulaiman, M., & Ali, Z. (2017). Investigation of voice pathology detection and classification on different frequency regions using correlation functions. Journal of Voice, 31(1), 3–15.Google Scholar
  4. Apkinar, E., & Berger, J. (2017). Valuable Virality. Journal of Marketing Research, 54(2), 318–330.Google Scholar
  5. Appold, K. (2017). Turn data into insight: How predictive analytics can capture revenue. Managed Healthcare Executive, 27(7), 16–21.Google Scholar
  6. Aribarg, A., Pieters, R., & Wedel, M. (2010). Raising the BAR: Bias adjustment of recognition tests in advertising. Journal of Marketing Research, 47(3), 387–400.Google Scholar
  7. Arnheim, R. (1954). Art and visual perception: A psychology of the creative eye. Berkley: University of California Press.Google Scholar
  8. Backhaus, K., Meyer, M., & Stockert, A. (1985). Using voice analysis for analyzing bargaining processes in industrial marketing. Journal of Business Research, 13(5), 435–446.Google Scholar
  9. Bänziger, T., Patel, S., & Scherer, K. R. (2014). The role of perceived voice and speech characteristics in vocal emotion communication. Journal of Nonverbal Behavior, 38(1), 31–52.Google Scholar
  10. Barasch, A., & Berger, J. (2014). Broadcasting and narrowcasting: How audience size affects what people share. Journal of Marketing Research, 51(3), 286–299.Google Scholar
  11. Bashir, N. Y., & Rule, N. O. (2014). Shopping under the influence: Nonverbal appearance-based communicator cues affect consumer judgments. Psychology & Marketing, 31(7), 539–548.Google Scholar
  12. Batra, R., & Keller, K. L. (2016). Integrating marketing communications: New findings, new lessons, and new ideas. Journal of Marketing, 80(6), 122–145.Google Scholar
  13. Baumann, O., & Belin, P. (2010). Perceptual scaling of voice identity: Common dimensions for different vowels and speakers. Psychological Research, 74(1), 110–120.Google Scholar
  14. Beckers, S. F. M., van Doorn, J., & Verhoef, P. C. (2018). Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value. Journal of the Academy of Marketing Science, In-Press.  https://doi.org/10.1007/s11747-017-0539-4.
  15. Bellman, S., Nenycz-Thiel, M., Kennedy, R., McColl, B., Larguinat, L., & Varan, D. (2016). What makes a television commercial sell? Using biometrics to identify successful ads: Demonstrating neuromeasures’ potential on 100 Mars brand ads with single-source data. Journal of Advertising Research, 57(1), 53–66.Google Scholar
  16. Berger, J., Sorensen, A. T., & Rasmussen, S. J. (2010). Positive effects of negative publicity: When negative reviews increase sales. Marketing Science, 29(5), 815–827.Google Scholar
  17. Berger, J., & Schwartz, E. M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48(5), 869–880.Google Scholar
  18. Berger, J., & Milkman, K. L. (2012). What makes online content viral?? Journal of Marketing Research, 49(2), 192–205.Google Scholar
  19. Bernard, J. (2017). Local and location-based: Combining strategies for mobile marketing maturity. In Forbes. Retrieved on January 24, 2018 from https://www.forbes.com/sites/forbesagencycouncil/2017/09/25/local-and-location-based-combining-strategies-for-mobile-marketing-maturity/#24356c2aae90.
  20. Blei, D. M., Ng, A. Y., Jordan, M. I., & Lafferty, J. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4/5), 993–1022.Google Scholar
  21. Bodell, T. (2014). How big data is becoming a bigger deal for the power sector. In Electric Light & Power. Retrieved on September 8, 2017 from http://www.elp.com/articles/print/volume-92/issue-3/columns/economic-inquiry/how-big-data-is-becoming-a-bigger-deal-for-the-power-sector.html.
  22. Borah, A., & Tellis, G. J. (2016). Halo (spillover) effects in social media: Do product recalls of one brand hurt or help rival brands? Journal of Marketing Research, 53(2), 143–160.Google Scholar
  23. Brasel, A. S., & Gips, J. (2008). Breaking through fast-forwarding: Brand information and visual attention. Journal of Marketing, 72(6), 31–48.Google Scholar
  24. Brickman, G. A. (1976). Voice analysis. Journal of Advertising Research, 16(3), 43–48.Google Scholar
  25. Brickman, G. A. (1980). Uses of voice-pitch analysis. Journal of Advertising Research, 20(2), 69–73.Google Scholar
  26. Briggs, B., & Hodgetts, C. (2017). Tech trends 2017: An overview. In Wall Street Journal. Retrieved on September 5, 2017 from http://deloitte.wsj.com/cio/2017/02/08/tech-trends-2017-an-overview/.
  27. Burgoon, M., Jones, S. B., & Stewart, D. (1975). Toward a message-centered theory of persuasion: Three empirical investigations of language intensity. Human Communication Research, 1(3), 240–256.Google Scholar
  28. Burgoon, J. K., Guerrero, L. K., & Floyd, K. (2016). Vocalics. In J. K. Burgoon, L. K. Guerrero, & K. Floyd (Eds.), Nonverbal Communication (p.132–144). New York: Routledge.Google Scholar
  29. Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science, 35(6), 953–975.Google Scholar
  30. CallMiner. (2017). Health Insurance Innovations selects CallMiner Interaction Analytics to build consumer and regulatory confidence. In CallMiner Eureka. Retrieved on September 11, 2017 from https://callminer.com/company/news/health-insurance-innovations-selects-callminer-interaction-analytics-build-consumer-regulatory-confidence/.
  31. Cavanaugh, L. A., Nunes, J. C., & Han, Y. J. (2018). Please process the signal, but don’t praise it: How compliments on identity signals result in embarrassment. Los Angeles: Working Paper, University of Southern California.Google Scholar
  32. Chapple, E. D., & Donald Jr., G. (1947). An evaluation of department store salespeople by the interaction chronograph. Journal of Marketing, 12(2), 173–185.Google Scholar
  33. Chattopadhyay, A., Dahl, D. W., Ritchie, R. J. B., & Shahin, K. N. (2003). Hearing voices: The impact of announcer speech characteristics on consumer response to broadcast advertising. Journal of Consumer Psychology, 13(3), 198–204.Google Scholar
  34. Chen, Z., & Lurie, N. H. (2013). Temporal contiguity and negativity bias in the impact of online word of mouth. Journal of Marketing Research, 50(4), 463–476.Google Scholar
  35. Chen, B. X. (2017). The smartphone’s future: It’s all about the camera. In New York Times. Retrieved on October 15, 2017 from https://www.nytimes.com/2017/08/30/technology/personaltech/future-smartphone-camera-augmented-reality.html.
  36. Chico’s FAS Inc. (2017). Interactive Analyst Center. In Chico’s FAS Inc. Retrieved on September 10, 2017 from http://apps.indigotools.com/IR/IAC/?Ticker=CHS &Exchange=NYSE#.
  37. Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87.Google Scholar
  38. Colicev, A., Malshe, A., Pauwels, K., & O’Connor, P. (2018). Improving customer mindset metrics and shareholder value through social media: The different roles of owned and earned media. Journal of Marketing, 82(1), 37–56.Google Scholar
  39. Collins, S. (2016). Harnessing the transformative power of big data. Milliman Inc., 1–6.Google Scholar
  40. Coughlin, T. (2017). Analysis of dark data provides market advantages. In Forbes. Retrieved on September 5, 2017 from https://www.forbes.com/sites/tomcoughlin/2017/07/24/analysis-of-dark-data-provides-market-advantages/#4fe9af29872b.
  41. Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343–362.Google Scholar
  42. Danaher, P. J., Smith, M. S., Ranasinghe, K., & Danaher, T. S. (2015). Where, when and how long: Factors that influence the redemption of mobile phone coupons. Journal of Marketing Research, 52(5), 710–725.Google Scholar
  43. Davies, A. (2015). Why unstructured data holds the key to understanding the customer. In My Customer. Retrieved February 27, 2017 from http://www.mycustomer.com/. marketing/data/why-unstructured-data-holds-the-key-to-understanding-the-customer.
  44. Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293–307.Google Scholar
  45. Derbaix, C. M. (1995). The impact of affective reactions on attitudes toward the advertisement and the brand: A step toward ecological validity. Journal of Marketing Research, 32(4), 470–479.Google Scholar
  46. Ding, M., Hauser, J. R., Dong, S., Dzyabura, D., Yang, Z., Chenting, S. U., & Gaskin, S. P. (2011). Unstructured direct elicitation of decision rules. Journal of Marketing Research, 48(1), 116–127.Google Scholar
  47. Dondis, D. A. (1974). A primer of visual literacy. Cambridge: The MIT Press.Google Scholar
  48. Duke, K., & Amir, O. (2018). Guilt accounting theory: Consequences of temporally separating decisions and their enactment. San Diego: Working Paper: University of California.Google Scholar
  49. Dzyabura, D., & Hauser, J. R. (2018). Recommending products when customers learn their preferences. Marketing Science, forthcoming.Google Scholar
  50. Edelman, D., & Singer, M. (2015). The new consumer journey. In McKinsey & Company. Retrieved on September 26, 2017 from http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-new-consumer-decision-journey.
  51. Ekman, P., & Friesen, W. V. (1976). Measuring facial movement. Journal of Nonverbal Behavior, 1(1), 56–75.Google Scholar
  52. Elliott, S. (2014). Targeting customers on mobile during the holiday shopping season. In New York Times. Retrieved January 24, 2018 from https://www.nytimes.com/2014/12/03/business/media/targeting-customers-on-mobile-during-holiday-shopping-season.html .
  53. Elpers, J., Wedel, M., & Pieters, R. (2003). Why do consumers stop viewing television commercials? Two experiments on the influence of moment-to-moment entertainment and information value. Journal of Marketing Research, 40(4), 437–453.Google Scholar
  54. Fong, N. M., Fang, Z., & Luo, X. (2015). Geo-conquesting: Competitive locational targeting of mobile promotions. Journal of Marketing Research, 52(5), 726–735.Google Scholar
  55. Fossen, B. L., & Schweidel, D. A. (2017). Television advertising and online word-of-mouth: An empirical investigation of social TV activity. Marketing Science, 36(1), 105–123.Google Scholar
  56. Fujiwara, K., & Daibo, I. (2014). The extraction of nonverbal behaviors: Using video images and speech-signal analysis in dyadic conversation. Journal of Nonverbal Behavior, 38(3), 377–388.Google Scholar
  57. Gebeloff, R., & Russell, K. (2017). How the growth of E-commerce is shifting retail jobs. In The New York Times. Retrieved on October 2, 2017 from https://www.nytimes.com/interactive/2017/07/06/business/ecommerce-retail-jobs.html?mcubz=1 .
  58. Ghose, A., Ipeirotis, P. G., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.Google Scholar
  59. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth conversations. Marketing Science, 23(4), 1–44.Google Scholar
  60. Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth communication: Evidence from a field test. Marketing Science, 28(4), 721–739.Google Scholar
  61. Goodrich, K. (2011). Anarchy of effects? Exploring attention to online advertising and multiple outcomes. Psychology & Marketing, 28(4), 417–440.Google Scholar
  62. Guitart, I. A., & Hervet, G. (2017). The impact of contextual television ads on online conversations: An application in the insurance industry. International Journal of Research in Marketing, 34, 480–498.Google Scholar
  63. Hall, L. O., Bensaid, A. M., Clarke, L. P., Velthuizen, R. P., Silbiger, M. S., & Bezdek, J. C. (1992). A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks, 3(5), 672–682.Google Scholar
  64. Hall, J. A., Verghis, P., Stockton, W., & Goh, J. X. (2014). It takes just 120 seconds: Predicting satisfaction in technical support calls. Psychology & Marketing, 31(7), 500–508.Google Scholar
  65. Hamilton, R. W., Schlosser, A., & Chen, Y. J. (2017). Who's driving this conversation? Systematic biases in the content of online consumer discussions. Journal of Marketing Research, 54(4),1–16.Google Scholar
  66. Hashimoto, A., & Clayton, M. (2009). Visual design fundamentals: A digital approach (third ed.). Boston: Course Technology CENGAGE Learning.Google Scholar
  67. Hautamäki, R. G., Kinnunen, T., Hautamäki, T., & Laukkanen, A. (2015). Automatic versus human speaker verification: The case of voice mimicry. Speech Communication, 72, 13–31.Google Scholar
  68. He, T., Huang, W., Qiao, Y., & Yao, J. (2016). Text-attentional convolutional neural network for scene text detection. IEEE Transactions on Image Processing, 25(6), 2529–2541.Google Scholar
  69. Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does twitter matter? The impact of microblogging word of mouth on consumers' adoption of new movies. Journal of the Academy of Marketing Science, 43(3), 375–394.Google Scholar
  70. Hessman, T. (2013). Putting big data to work. Industry Week, 262(4), 14–18.Google Scholar
  71. Hewett, K., Rand, W., Rust, R. T., & van Heerde, H. J. (2016). Brand buzz in the echoverse. Journal of Marketing, 80(3), 1–24.Google Scholar
  72. Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884–905.Google Scholar
  73. Hoberg, G., & Phillips, G. (2018). Conglomerate industry choice and product language. Management Science-In Press.  https://doi.org/10.1287/mnsc.2016.2693.
  74. Ho-Dac, N. N., Carson, S. J., & Moore, W. L. (2013). The effects of positive and negative online customer reviews: Do brand strength and category maturity matter? Journal of Marketing, 77(6), 37–53.Google Scholar
  75. Hodgson, K. (2015). What’s the big deal about big data? SDM: Security Distributing & Marketing, 69–75.Google Scholar
  76. Homburg, C., Ehm, L., & Artz, M. (2015). Measuring and managing consumer sentiment in an online community environment. Journal of Marketing Research, 52(5), 629–641.Google Scholar
  77. Howatson, A. (2016). How to unlock the power of unstructured data. In Marketing Tech News. Retrieved February 27, 2017 from https://www.marketingtechnews.net/news/2016/dec/13/how-unlock-power-unstructured-data/ .
  78. Hsu, L., & Lawrence, B. (2016). The role of social media and brand equity during a product recall crisis: A shareholder value perspective. International Journal of Research in Marketing, 33(1), 59–77.Google Scholar
  79. Huang, M., & Rust, R. T. (2017). Technology-driven service strategy. Journal of the Academy of Marketing Science, 45(6), 906–924.Google Scholar
  80. Humphreys, A. (2010). Megamarketing: The creation of markets as a social process. Journal of Marketing, 74(2), –1, 19.Google Scholar
  81. Humphreys, A., & Wang, R. J. H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, In-Press.  https://doi.org/10.1093/jcr/ucx104.
  82. IBM. (2017). IBM Watson Speech to Text. Retrieved on July 30, 2017 from https://www.ibm.com/watson/services/speech-to-text/.
  83. Joo, M., Wilbur, K. C., & Zhu, Y. (2016). Effects of TV advertising on keyword search. International Journal of Research in Marketing, 33(3), 508–523.Google Scholar
  84. Juslin, P. N., & Scherer, K. R. (2005). Vocal expression of affect. In J. A. Harrigan, R. Rosenthal, & K. R. Scherer (Eds.), The New Handbook of Methods in Nonverbal Behavior Research (pp. 65–135). New York: Oxford University Press.Google Scholar
  85. Karolefski, J. (2015). Accepting the BIG DATA challenge. Progressive Grocer, 94(7), 142–146.Google Scholar
  86. Kashmiri, S., & Mahajan, V. (2017). Values that shape marketing decisions: Influence of chief executive officers’ political ideology on innovation propensity, shareholder value, and risk. Journal of Marketing Research, 54(2), 260–278.Google Scholar
  87. Kashmiri, S., Nicol, C. D., & Arora, S. (2017). Me, myself, and I: Influence of CEO narcissism on firms’ innovation strategy and the likelihood of product-harm crises. Journal of the Academy of Marketing Science, In-Press.  https://doi.org/10.1007/s11747-017-0535-8.
  88. Kim, H. S. (2015). Attracting views and going viral: How message features and news-sharing channels affect health news diffusion. Journal of Communication, 65(3), 512–534.Google Scholar
  89. Klie, L. (2013). Getting closer to customers tops big data agenda. CRM Magazine, 17(1), 15–15.Google Scholar
  90. Köhler, C. F., Rohm, A. J., de Ruyter, K., & Wetzels, M. (2011). Return on interactivity: The impact of online agents on newcomer adjustment. Journal of Marketing, 75(2), 93–108.Google Scholar
  91. Költringer, C., & Dickinger, A. (2015). Analyzing destination branding and image from online sources: A web content mining approach. Journal of Business Research, 68(9), 1836–1843.Google Scholar
  92. Kostyra, D. S., Reiner, J., Natter, M., & Klapper, D. (2016). Decomposing the effects of online customer reviews on brand, price, and product attributes. International Journal of Research in Marketing, 33(1), 11–26.Google Scholar
  93. Kotler, P., & Keller, K. L. (2015). Marketing Management. Upper Saddle River: Prentice Hall.Google Scholar
  94. Kozinets, R. V., de Valck, K., Wojnicki, A.C., & Wilner, S. J. S. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(2), 71–89.Google Scholar
  95. Kulesza, W., Szypowska, Z., Jarman, M. S., & Dolinski, D. (2014). Attractive chameleons sell: The mimicry-attractiveness link. Psychology & Marketing, 31(7), 549–561.Google Scholar
  96. Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36–68.Google Scholar
  97. Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80(6), 146–172.Google Scholar
  98. Landwehr, J. R., Labroo, A. A., & Herrmann, A. (2011). Gut liking for the ordinary: Incorporating design fluency improves automobile sales forecasts. Marketing Science, 30(3), 416–429.Google Scholar
  99. Landwehr, J. R., Wentzel, D., & Herrmann, A. (2013). Product design for the long run: Consumer responses to typical and atypical designs at different stages of exposure. Journal of Marketing, 77(5), 92–107.Google Scholar
  100. Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881–894.Google Scholar
  101. Leigh, T. W., & Summers, J. O. (2002). An initial evaluation of industrial buyer’s impressions of salespersons’ nonverbal cues. Journal of Personal Selling and Sales Management, 22(1), 41–53.Google Scholar
  102. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96.Google Scholar
  103. Li, X., Shi, M., & Wang, X. (2018). Video Mining: Measuring Visual Information Using Automatic Methods. Working paper, Ivey Business School.Google Scholar
  104. Liaukonyte, J., Teixeira, T., & Wilbur, K. C. (2015). Television advertising and online shopping. Marketing Science, 34(3), 311–330.Google Scholar
  105. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.Google Scholar
  106. Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388.Google Scholar
  107. Liu, X., Burns, A. C., & Hou, Y. (2017a). An investigation of brand-related user-generated content on Twitter. Journal of Advertising, 46(2), 236–247.Google Scholar
  108. Liu, Y., Li, K. J., Chen, H., & Balachander, S. (2017b). The effects of products’ aesthetic design on demand and marketing-mix effectiveness: the role of segment prototypicality and brand consistency. Journal of Marketing, 81(1), 83–102.Google Scholar
  109. Liu, H., Lu, J., Feng, J., & Zhou, J. (2017c). Learning deep shareable and structural detectors face alignment. IEEE Transactions on Image Processing, 26(4), 1666–1678.Google Scholar
  110. Lobschat, L., Osinga, E. C., & Reinartz, W. J. (2017). What happens online stays online? – Segment-specific online and offline effects of banner advertisements. Journal of Marketing Research, 54(6), 901–913.Google Scholar
  111. Lohr, S. (2012). The age of big data. In New York Times. Retrieved on September 6, 2017 from http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?mcubz=1.
  112. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35–65.Google Scholar
  113. Lowe, M. L., & Haws, K., L. (2017). Sounds big: The effects of acoustic pitch on product perceptions. Journal of Marketing Research, 54(2), 331–346.Google Scholar
  114. Lowe’s (2017). Lowe’s introduces in-store navigation using augmented reality. In Lowe’s. Retrieved on September 10, 2017 from https://newsroom.lowes.com/news-releases/lowesintroducesin-storenavigationusingaugmentedreality/.
  115. Lu, S., Xiao, L., & Ding, M. (2016). A video-based automated recommender (VAR) system for garments. Marketing Science, 35(3), 484–510.Google Scholar
  116. Ludwig, S., de Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., & Pfann, G. (2013). More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. Journal of Marketing, 77(1), 87–103.Google Scholar
  117. Luo, X., Andrews, M., Fang, Z., & Phang, C. W. (2014). Mobile Targeting. Marketing Science, 60(7), 1738–1756.Google Scholar
  118. Ma, Z., & Dubé, L. (2011). Process and outcome interdependency in frontline service encounters. Journal of Marketing, 75(3), 83–98.Google Scholar
  119. Ma, L., Sun, B., & Kekre, S. (2015). The squeaky wheel gets the grease--An empirical analysis of customer voice and firm intervention on Twitter. Marketing Science, 34(5), 627–645.Google Scholar
  120. MacInnis, D. (2011). A framework for conceptual contributions in marketing. Journal of Marketing, 75(4), 136–154.Google Scholar
  121. Mandler, G. (1982). The structure of value: Accounting for taste. In H. Margaret, S. Clarke &. S.T. Fiske (Eds). Affect and Cognition: The 17th Annual Carnegie Symposium on Cognition, pp. 3–36. Hillsdale: Lawrence Erlbaum.Google Scholar
  122. Marchand, A., Hennig-Thurau, T., & Wiertz, C. (2017). Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success. International Journal of Research in Marketing, 34(2), 336–354.Google Scholar
  123. Marinova, D., de Ruyter, K., Huang, M. H., Mueter, M. L., & Challagalla, G. (2017). Getting smart: Learning from technology-empowered frontline interactions. Journal of Service Research, 20(1), 29–42.Google Scholar
  124. Marinova, D., Singh, S., & Singh, J. (2018). Frontline problem-solving interactions: A dynamic analysis of verbal and nonverbal cues. Journal of Marketing Research, 55:178–192.  https://doi.org/10.1509/jmr.15.0243.
  125. Marr, B. (2017). The complete beginner’s guide to big data in 2017. In Forbes. Retrieved on. September 5, 2017 from https://www.forbes.com/sites/bernardmarr/2017/03/14/the-complete-beginners-guide-to-big-data-in-2017/#1ce04b97365a.
  126. Mattila, A. S., & Enz, C. A. (2002). The role of emotions in service encounters. Journal of Service Research, 4(4), 268.Google Scholar
  127. McAlister, L., Sonnier, G., & Shively, T. (2012). The relationship between online chatter and firm value. Marketing Letters, 23(1), 1–12.Google Scholar
  128. McCurry, J. (2017). Japanese company replaces office workers with artificial intelligence. In The Guardian. Retrieved on October 22, 2017 from https://www.theguardian.com/technology/2017/jan/05/japanese-company-replaces-office-workers-artificial-intelligence-ai-fukoku-mutual-life-insurance.
  129. Mims, C. (2016). Why are there more consumer goods than ever before? In Wall Street Journal. Retrieved August 27, 2017 from https://www.wsj.com/articles/why-there-are-more-consumer-goods-than-ever-1461556860.
  130. Mochon, D., Johnson, K., Schwartz, J., & Ariely, D. (2017). What are likes worth? A Facebook page field study. Journal of Marketing Research, 54(2), 306–317.Google Scholar
  131. Moon, S., & Kamakura, W. A. (2017). A picture is worth a thousand words: Translating product reviews into a product positioning map. International Journal of Research in Marketing, 34(1), 265–285.Google Scholar
  132. Moorman, C., & Day, G. S. (2016). Organization for marketing excellence. Journal of Marketing, 80(6), 6–35.Google Scholar
  133. Muir, K., Joinson, A., Cotterill, R., & Dewdney, N. (2016). Characterizing the linguistic chameleon: Personal and social correlates of linguistic style accommodation. Human Communication Research, 42(3), 462–484.Google Scholar
  134. Nair, R., & Narayanan, A. (2012). Benefitting from big data: Leveraging unstructured data capabilities for competitive advantage. In Booz & Company. Retrieved April 19, 2017 from https://www.strategyand.pwc.com/media/file/Stategyand_Benefiting-from-BigData.pdf.
  135. Nam, H., & Kannan, P. K. (2014). The informational value of social tagging networks. Journal of Marketing, 78(4), 21–40.Google Scholar
  136. Nam, H., Joshi, Y. V., & Kannan, P. K. (2017). Harvesting brand information from social tags. Journal of Marketing, 81(4), 88–108.Google Scholar
  137. Naylor, R. W., Lamberton, C. P., & West, P. M. (2012). Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. Journal of Marketing, 76(6), 105–120.Google Scholar
  138. Nelson, M. (2013). The big picture on big data. Intermedia, 41(1), 12–16.Google Scholar
  139. Nelson, R. G., & Schwartz, D. (1979). Voice-pitch analysis. Journal of Advertising Research, 19(5), 55–59.Google Scholar
  140. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market- structure surveillance through text mining. Marketing Science, 31(3), 521–543.Google Scholar
  141. Netzer, O., Lemaire, A., & Herzenstein, M. (2018). When words sweat: Identifying signals for loan default in the text of loan applications. Working Paper, Columbia Business School, Columbia University.Google Scholar
  142. Niederhoffer, K. G., & Pennebaker, J. W. (2002). Linguistic style matching in social interaction. Journal of Language and Social Psychology, 21(4), 337–360.Google Scholar
  143. Olenski, S. (2016). How inbound marketing killed cold calling. In Forbes. Retrieved on July 2, 2017 from https://www.forbes.com/sites/steveolenski/2016/06/30/how-inbound-marketing-killed-cold-calling/#4979d86da71f.
  144. Onishi, H., & Manchanda, P. (2012). Marketing activity, blogging and sales. International Journal of Research in Marketing, 29(3), 221–234.Google Scholar
  145. Ordenes, F. V., Theodoulidis, B., Burton, J., Gruber, T., & Zaki, M. (2014). Analyzing customer experience feedback using text mining: A linguistics-based approach. Journal of Service Research, 17(3), 278–295.Google Scholar
  146. Ordenes, F. V., Ludwig, S., de Ruyter, K., Grewal, D., & Wetzels, M. (2017). Unveiling what is written in the stars: Analyzing explicit, implicit, and discourse patterns of sentiment in social media. Journal of Consumer Research, 43(6), 875–894.Google Scholar
  147. Ordenes, F. V., Grewal, D., Ludwig, S., de Ruyter, K., Mahr, D., & Wetzels, M. (2018). Cutting through content clutter: How speech and image acts drive consumer sharing of social media brand messages. Journal of Consumer Research.  https://doi.org/10.1093/jcr/ucy032
  148. Packard, G., & Berger, J. (2017). How language shapes word of mouth’s impact. Journal of Marketing Research, 54(4), 572–588.Google Scholar
  149. Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic Inquiry and Word Count: LIWC 2001. [computer software]. Mahwah: Lawrence Erlbaum Associates.Google Scholar
  150. Pennington, A. L. (1968). Customer-salesman bargaining behavior in retail transactions. Journal of Marketing Research, 5(3), 255–262.Google Scholar
  151. Peterson, R. A., Cannito, M. P., & Brown, S. P. (1995). An exploratory investigation of voice characteristics and selling effectiveness. Journal of Personal Selling & Sales Management, 15(1), 1–15.Google Scholar
  152. Pham, M. T., Geuens, M., & De Pelsmacker, P. (2013). The influence of ad-evoked feelings on brand evaluations: Empirical generalizations from consumer responses to more than 1000 TV commercials. International Journal of Research in Marketing, 30(4), 383–394.Google Scholar
  153. Pieters, R., & Wedel, M. (2004). Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. Journal of Marketing, 68(2), 36–50.Google Scholar
  154. Pieters, R., Wedel, M., & Batra, R. (2010). The stopping power of advertising: Measures and effects of visual complexity. Journal of Marketing, 74(5), 48–60.Google Scholar
  155. Pieters, R., & Wedel, M. (2012). Ad gist: Ad communication in a single eye fixation. Marketing Science, 31(1), 59–73.Google Scholar
  156. Rafaeli, A., Ziklik, L., & Doucet, L. (2008). The impact of call center employees’ customer orientation behaviors on service quality. Journal of Service Research, 10(3), 239–255.Google Scholar
  157. Reuzel, E., Embregts, P. J. C. M., Bosman, A. T. M., Cox, R., van Nieuwenhuijzen, M., & Jahoda, A. (2013). Conversational synchronization in naturally occurring settings: A recurrence-based analysis of gaze directions and speech rhythms of staff and clients with intellectual disability. Journal of Nonverbal Behavior, 37(4), 281–305.Google Scholar
  158. Rizkallah, J. (2017). The big (unstructured) data problem. In Forbes. Retrieved on September 5, 2017 from https://www.forbes.com/sites/forbestechcouncil/2017/06/05/the-big-unstructured-data-problem/#274fefa9493a.
  159. Rutz, O. J., Trusov, M., & Bucklin, R. E. (2011). Modeling indirect effects of paid search advertising: Which keywords lead to more future visits? Marketing Science, 30(4), 646–665.Google Scholar
  160. Rutz, O. J., Sonnier, G. P., & Trusov, M. (2017). A new method to aid copy testing of paid search text advertisements. Journal of Marketing Research, 54(6), 885–900.Google Scholar
  161. Satomura, T., Wedel, M., & Pieters, R. (2014). Copy alert: A method and metric to detect visual copycat brands. Journal of Marketing Research, 51(1), 1–13.Google Scholar
  162. Schramm, W. (1954). How communication works. In W. Schramm (Ed.), The process and effects of mass communication. Urbana: The University of Illinois Press.Google Scholar
  163. Schweidel, D. A., & Moe, W. W. (2014). Listening in on social media: A joint model of sentiment and venue format choice. Journal of Marketing Research, 51(4), 387–402.Google Scholar
  164. Sennaar, K. (2017). How America’s top 4 insurance companies are using machine learning. In Tech Emergence. Retrieved on September 11, 2017 from https://www.techemergence.com/machine-learning-at-insurance-companies/.
  165. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana: The University of Illinois Press.Google Scholar
  166. Shoemaker, P. J., Tankard, J. W., & Lasorsa, D. L. (2004). How to build social science theories. Thousand Oaks: Sage Publications, Inc..Google Scholar
  167. Singh, J., Marinova, D., & Singh, S. (2018). Customer query handling in sales interactions. Journal of the Academy of Marketing Science, forthcoming.Google Scholar
  168. Smith, K. (2015). Big data discoveries. Best’s Review, 7, 53–56.Google Scholar
  169. Song, R., Moon, S., Chen, H. A., & Houston, M. B. (2018). When marketing strategy meets culture: The role of culture in product evaluations. Journal of the Academy of Marketing Science, In-Press.  https://doi.org/10.1007/s11747-017-0525-x.
  170. Sonnier, G. P., McAlister, L., & Rutz, O. J. (2011). A dynamic model of the effect of online communications on firm sales. Marketing Science, 30(4), 702–716.Google Scholar
  171. Sridhar, S., & Srinivasan, R. (2012). Social influence effects in online product ratings. Journal of Marketing, 76(5), 70–88.Google Scholar
  172. Stringer, B. (2013). Big data in the chemical industry. ICIS Chemical Business, 284(20), 2–2.Google Scholar
  173. Székely, E., Ahmed, Z., Hennig, S., Cabral, J. P., & Carson-Berndsen, J. (2014). Predicting synthetic voice style from facial expressions. An application for augmented conversations. Speech Communication, 57, 63–75.Google Scholar
  174. Tang, T., Fang, E., & Feng, W. (2014). Is neutral really neutral? The effects of neutral user- generated content on product sales. Journal of Marketing, 78(4), 41–58.Google Scholar
  175. Tang, C., & Guo, L. (2015). Digging for gold with a simple tool: Validating text mining in studying electronic word-of-mouth (eWOM) communication. Marketing Letters, 26(1), 67–80.Google Scholar
  176. Teixeira, T., Wedel, M., & Pieters, R. (2012). Emotion-induced engagement in internet video advertisements. Journal of Marketing Research, 49(2), 144–159.Google Scholar
  177. Teixeira, T. S., & Stipp, H. (2013). Optimizing the amount of entertainment in advertising: What’s so funny about tracking reactions to humor? Journal of Advertising Research, 53(3), 286–296.Google Scholar
  178. Teixeira, T., Picard, R., & el Kaliouby, R. (2014). Why, when, and how much to entertain consumers in advertisements? A web-based facial tracking field study. Marketing Science, 33(6), 809–827.Google Scholar
  179. Timoshenko, A., & Hauser, J. R. (2018). Identifying customer needs from user-generated content. Marketing Science, forthcoming.Google Scholar
  180. Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198–215.Google Scholar
  181. Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. Journal of Marketing Research, 51(4), 463–479.Google Scholar
  182. Toubia, O., & Stephen, A. T. (2013). Intrinsic vs. image-related utility in social media: Why do people contribute content to Twitter? Marketing Science, 32(3), 369–392.Google Scholar
  183. Toubia, O., & Netzer, O. (2017). Idea generation, creativity, and prototypicality. Marketing Science, 36(1), 1–20.Google Scholar
  184. Townsend, L. (2014). How much has the ice bucket challenge achieved? In BBC. Retrieved on July 1, 2017 from http://www.bbc.com/news/magazine-29013707.
  185. Treistman, J., & Gregg, J. P. (1979). Visual, verbal, and sales responses to print ads. Journal of Advertising Research, 19(4), 41.Google Scholar
  186. Trusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Science, 35(3), 405–426.Google Scholar
  187. van Heerde, H. J., Gijsbrechts, E., & Pauwels, K. (2015). Fanning the flames? Flow media coverage of a price war affects retailers, consumers, and investors. Journal of Marketing Research, 52(5), 674–693.Google Scholar
  188. Wang, W. C., Chien, C. S., & Moutinho, L. (2015). Do you really feel happy? Some implications of voice emotion response in Mandarin Chinese. Marketing Letters, 26(3), 391–409.Google Scholar
  189. Wang, X., Li, X., Goldenberg, J., Muchnik, L. (2018). One picture is worth 253 characters: Using photo mining to understand the role of a camera in online word of mouth. Working paper, Ivey Business School.Google Scholar
  190. Wedel, M., & Pieters, R. (2000). Eye fixations on advertisements and memory for brands: A model and findings. Marketing Science, 19(4), 297–312.Google Scholar
  191. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.Google Scholar
  192. Welsh, A. (2017). Unstructured content: An untapped fuel source for AI and machine learning. In Developer. Retrieved on September 10, 2017 from http://www.developer.com/db/unstructured-content-an-untapped-fuel-source-for-ai-and-machine-learning.html.
  193. Wikipedia. (2017). Unstructured data. Retrieved October 29, 2017 from https://en.wikipedia.org/wiki/Unstructured_data.
  194. Wilson, A., Giebelhausen, M., & Brady, M. (2017). Negative word of mouth can be a positive for consumers connected to the brand. Journal of the Academy of Marketing Science, 45(4), 534–547.Google Scholar
  195. Wyner, G. (2013). Data, data everywhere. Marketing News, 47(3), 18–19.Google Scholar
  196. Xiao, L., & Ding, M. (2014). Just the faces: Exploring the effects of facial features in print advertising. Marketing Science, 33(3), 338–352.Google Scholar
  197. Xiong, G., & Bharadwaj, S. (2013). Asymmetric roles of advertising and marketing capability in financial returns to news: Turning bad into good and good into great. Journal of Marketing Research, 50(6), 706–724.Google Scholar
  198. Xiong, G., & Bharadwaj, S. (2014). Prerelease buzz evolution patterns and new product performance. Marketing Science, 33(3), 401–421.Google Scholar
  199. Xiong, H., Yu, W., Yang, X., Swamy, M. N. S., & Yu, Q. (2017). Learning the conformal transformation kernel for image recognition. IEEE Transactions on Neural Networks and Learning Systems, 28(1), 149–163.Google Scholar
  200. Xu, Z., Zhang, Y., & Cao, L. (2014). Social image analysis from a non-IID perspective. IEEE Transactions on Multimedia, 16(7), 1986–1998.Google Scholar
  201. Yadav, M. S., Prabhu, J. C., & Chandy, R. K. (2007). Managing the future: CEO attention and innovation outcomes. Journal of Marketing, 71(4), 84–101.Google Scholar
  202. Yin, D., Bond, S. D., & Zhang, H. A. N. (2017). Keep your cool or let it out: Nonlinear effects of expressed arousal on perceptions of consumer reviews. Journal of Marketing Research, 54(3), 447–463.Google Scholar
  203. Yokoyama, H., & Daibo, I. (2012). Effects of gaze and speech rate on receivers’ evaluations of persuasive speech. Psychological Reports, 110(2), 663–676.Google Scholar
  204. Zachary, M. A., McKenny, A. F., Short, J. C., Davis, K. M., & Wu, D. (2011). Franchise branding: An organizational identity perspective. Journal of the Academy of Marketing Science, 39(4), 629–s645.Google Scholar
  205. Zhang, X., Li, S., Burke, R. R., & Leykin, A. (2014). An examination of social influence on shopper behavior using video tracking data. Journal of Marketing, 78(5), 24–41.Google Scholar
  206. Zhang, Y., Moe, W. W., & Schweidel, D. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing, 34(1), 100–119.Google Scholar

Copyright information

© Academy of Marketing Science 2018

Authors and Affiliations

  1. 1.Robert J. Trulaske College of BusinessUniversity of MissouriColumbiaUSA

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