Análisis de contenido y características del mensaje en Twitter: el caso del maquillaje de lujo

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Publicado: nov. 20, 2020
Versiones:
2020-11-20 (1)
DOI: https://doi.org/10.21676/23897848.4146
Palabras clave:
redes sociales; comportamiento del consumidor; análisis de contenido; viaje del consumidor; maquillaje de lujo.

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Cindy Paola Pinzón-Ríos
INALDE Business School, Universidad de La Sabana
Ignacio Osuna-Soto
INALDE Business School, Universidad de La Sabana
Ernesto Barrera-Duque
INALDE Business School, Universidad de La Sabana

Resumen

Este artículo busca comprender el impacto reportado por los usuarios de las redes sociales sobre las estrategias de las marcas de maquillaje de lujo. Para lo cual se utiliza una combinación de metodologías: cualitativa para realizar un análisis de contenido mediante twitter, durante dos meses de 2016. En la metodología cuantitativa se aplicó el Modelo de Regresión de Poisson Inflado Cero (Zero Inflated Poisson Regression Model ) para determinar las características del tweet, relacionadas con: la respuesta y un gran volumen de interacción. Este estudio revela que en el viaje del consumidor, en el reporte del usuario predomina la precompra y poscompra, pero la interacción es mayor en los extremos del recorrido. Además, la interacción de los tweets incrementa con valores hedonistas, específicamente como la belleza, pero sorprendentemente los enlaces y videos dentro del contenido del tweet desmejoran su interacción. De manera aplicada, los especialistas en marketing en marcas de maquillaje de lujo pueden utilizar estos hallazgos para mejorar las estrategias de marketing y explorar nuevas oportunidades en el recorrido del consumidor.

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Cómo citar
Pinzón-Ríos, C. P., Osuna-Soto, I., & Barrera-Duque, E. (2020). Análisis de contenido y características del mensaje en Twitter: el caso del maquillaje de lujo. Clío América, 14(28), 543–560. https://doi.org/10.21676/23897848.4146
Número
Vol. 14 Núm. 28 (2020): julio - diciembre 2020
Sección
Artículo de Investigación Científica y Tecnológica
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Citas

Amatulli, C., Guido, G. & Nataraajan, R. (2015). Luxury purchasing among older consumers: exploring inferences about cognitive Age, status, and style motivations. Journal of Business Research, 68(9), 1945-1952. https://www.sciencedirect.com/science/article/pii/S01482963150 00053?casa_token=xVrpaL7cGAkAAAAA:cOerwIZw19SYmBJp0kbRqc30udpH9cad30hzZscCcNUW09YUBQvUVF1e-2-f657gJb7ezHbe9w

Annie, S. (2012). The potential of social media for luxury brand management. Marketing Intelligence & Planning, 30(7), 687-699. https://www.emerald.com/insight/content/doi/10.1108/0263 4501211273805/full/html?casa_token=avH6_gkNtJYAAAAA:Yoahzh_n6CONaFNKWONqGPfGiRvkL9kQ0OFHLtq2_xwjWYSSlmmm0-YjUWFkgOIFQx6ROwczIMl3xWuWIcojWV17 zvsQzJFXSSn8YvkdemGvGKWlMmk

Balazs, J. A. & Velásquez, J. D. (2016). Opinion mining and information fusion: a survey. Information Fusion, 27, 95-110. https://www.sciencedirect.com/science/article/pii/S15662535150005 36?casa_token=u5X28txOn1cAAAAA:7eEeoOqnTLFTpJIlWFu9FhABEXvodnu6-tO3ojHaNTAnE0tWuTxvOfZTRGv6NSQc04_M0RqSHA

Bello, G., Hernandez J. & Camacho, D. (2017). Detecting discussion communities on vaccination in twitter. Future Generation Computer Systems, 66, 125-136. https://www.sciencedirect.com/science/article/pii/S0167739X16302175?casa_token=wyUyhWBMREAAAAAA:n2nibQ1mGzl3Yjg1j305aEzSLmEOLSLAc7friUxdmW2S2TLlAXan-p8SFYIZMV7178umHkjRjw

Bello, G., Jung, J. & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59. https://www.sciencedirect.com/science/article/pii/S 1566253515000780?casa_token=emJ3kf4ZtfYAAAAA:QpNKs3J-R9PN94JFoTdy0hZ3ffxywr2-DqIjy2OjRPV6D1aB_9CWWF5h1Fv5t9ks8bNJzq-T9A

Bennett, W. & Segerberg, A. (2012). The logic of connective action: Digital media and the personalization of contentious politics. Information, Communication & Society, 15(5), 739-768. https://doi.org/10.1080/1369118X.2012.670661

Boero, M. (2015). The language of fashion in postmodern society: A social semiotic perspective. Semiotica, 2015(207), 303-325. 10.1515/sem-2015-0037

Brant, A. (2016) Using an Algorithm to Figure Out What Luxury Customers Really Want. Harvard Business Review. 1 – 4. https://hbr.org/2016/07/using-an-algorithm-to-figure-out-what-luxury-customers-really-want

Cha, M., Haddadi, H., Benevenuto, F. & Gummadi, P. K. (2010). Measuring user influence in twitter: The million follower fallacy. Icwsm, 10(10-17), 30. https://ojs.aaai.org/index.p hp/ICWSM/article/view/14033

Christakis, N. & Fowler, J (2013). Social contagion theory: examining dynamic social networks and human behavior. Statistics in medicine, 32(4), 556-577. https://www.ncbi.nlm.nih.gov /pmc/articles/PMC3830455/

Deloitte. (2014). Global Power of Luxury Goods: in the hands of the consumer. https://www2.deloitte.com/tr/en/pages/consumer-business/articles/global-powers-of-luxury-goods-2014.html

Demattè, M., Sanabria, D. & Spence, C. (2007). Olfactory–tactile compatibility effects demonstrated using a variation of the Implicit Association Test. Acta psychologica, 124(3), 332-343. https://www.sciencedirect.com/science/article/pii/S0001691806000527?casa_token=3vDxnLV92U8AAAAA:c46jSRY6cRYOlOKWLHQBhnSUdst8dLDX0LeELxfKB7G7N2kaLZE-E7Ne_Nc7btY5ReD-dsGMRg

Feng, D. & O’Halloran, K. (2012). Representing emotive meaning in visual images: A social semiotic approach. Journal of Pragmatics, 44(14), 2067-2084. https://www.sciencedirect.com/science /article/pii/S0378216612002603?casa_token=z_0slUTzduEAAAAA:E8JnPMA64QcoLEgVDWXbQU2XkQzQ-CARs0eDOn5aUvz8lmt-wKpUTyhQEAjt26ztVEITU97gDQ

Freberg, K., Graham, K., McGaughey, K. & Freberg, L. (2011). Who are the social media influencers? A study of public perceptions of personality. Public Relations Review, 37(1), 90-92. https://www.sciencedirect.com/science/article/pii/S0363811110001207?casa_token=wwaMvBq2GvUAAAAA:ZWwjz00gDBNdrV04v9TMGnCJ5yAdzSQjeMRleVlI57YWzWtRC3tcakWyRoLLoQ8QC2ZhqzquOQ

Gandomi, A. & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://www.sciencedirect.com/science/article/pii/S0268401214001066

Gul, S., Mahajan, I., Nisa, N., Tariq, S., Jan, A. & Ahmad, S. (2016). Tweets speak louder than leaders and masses: An analysis of tweets about the Jammu and Kashmir elections 2014. Online Information Review, 40(7), 900-912. https://doi.org/10.1108/OIR-10-2015-0330

Hachaj, T. & Ogiela, M. (2017). Clustering of trending topics in microblogging posts: A graph-based approach. Future Generation Computer Systems, 67, 297-304. https://doi.org/10.1016 /j.future.2016.04.009

Hoe, D., & Sian, C. (2011). An analysis of tweets in response to the death of Michael Jackson. In Aslib Proceedings. Emerald Group Publishing Limited 63, (5), 432-444. https://doi.org/10.1108/00012531111164941

Hosch, B., Amrit, C., Aarts, K. & Dassen, A. (2016). How do online citizens persuade fellow voters? Using Twitter during the 2012 Dutch parliamentary election campaign. Social science computer review, 34(2), 135-152. https://doi.org/10.1177/0894439314558200

Houghton, D., Joinson, A., Caldwell, N. & Marder, B. (2013). Tagger's delight? Disclosure and liking in Facebook: the effects of sharing photographs amongst multiple known social circles. Birmingham Business School: Discussion Paper Series. https://www.econstor.eu/handle/10419/202647

Hutter, K., Hautz, J., Dennhardt, S. & Füller, J. (2013). The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook. Journal of Product & Brand Management, 22(5/6), 342-351. https://doi.org/10.1108/JPBM-05-2013-0299

Hsieh, H. F. & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative health research, 15(9), 1277-1288. https://doi.org/10.1177/1049732305276687

Jansen, B., Zhang, M., Sobel, K. & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11), 2169-2188. https://doi.org/10.1002/asi.21149

Khan, F., Bashir, S. & Qamar, U. (2014). TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 57, 245-257. https://doi.org/10.1016/j.dss.2013.09.004

Kosinski, M., Stillwell, D. & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-5805. https://doi.org/10.1073/pnas.1218772110

Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Sage Publications. https://books.google.com.co/books?hl=es&lr=&id=s_yqFXnGgjQC&oi=fnd&pg=PP1&ots=b3ZS-UolyV&sig=hCMQSPTxzl-dUxoH5AxMfwPeHgs&redir_esc=y#v=onepage&q&f=false

Kulsiri, P. (2012). Self-Concept, Locus of Control, Media Exposure, And Behavior Of Youth Toward Luxury Products Purchase. Journal of Business & Economics Research (Online), 10(1), 11. https://www.clutejournals.com/index.php/JBER/article/view/6729

Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1-14. https://www.tandfonline.com/doi/abs /10.1080/00401706.1992.10485228

Lemon, K. & Verhoef, P. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420

Li, J., Peng, W., Li, T. & Sun, T. (Abril, 2013). Social network user influence dynamics prediction. Asia-Pacific Web Conference. 2013. Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-37401-2_32

McCormick, H. & Livett, C. (2012). Analysing the influence of the presentation of fashion garments on young consumers' online behaviour. Journal of Fashion Marketing and Management: An International Journal, 16(1), 21-41. https://doi.org/10.1108/13612021211203014

Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of econometrics, 33(3), 341-365. https://doi.org/10.1016/0304-4076(86)90002-3

Neves, A., Vieira, R., Mourão, F. & Rocha, L. (2015). Quantifying Complementarity among Strategies for Influencers’ Detection on Twitter. Procedia Computer Science, 51, 2435-2444. https://doi.org/10.1016/j.procs.2015.05.428

Osuna, I. & Pinzón, C. (2017). Comportamiento y experiencia de consumo desde la interconexión e interactividad de la World Wide Web: un recorrido teórico. I+ D REVISTA DE INVESTIGACIONES, 8(2) 35-45. https://doi.org/10.33304/revinv.v08n2-2016004.

Oswald, L. & Oswald, L. (2012). Marketing semiotics: Signs, strategies, and brand value. Oxford University Press. https://books.google.com.co/books?hl=es&lr=&id=42IQnidxP8IC&oi= fnd&pg=PP1&dq=Marketing+semiotics:+Signs,+strategies,+and+brand+value&ots=N88xPpCCpD&sig=aBmkv0KjE0c78DY8m5kWSyT2ENY&redir_esc=y#v=onepage&q=Marketing%20semiotics%3A%20Signs%2C%20strategies%2C%20and%20brand%20value&f=false

Parguel, B., Delécolle, T. & Valette-Florence, P. (2016). How price display influences consumer luxury perceptions. Journal of Business Research, 69(1), 341-348. https://doi.org/10.1016/j.jbusres.2015.08.006

Pinzón, C., Osuna, I. & Jaramillo, L. (2018). Digital Marketing Strategies for Luxury Cosmetics Brands: Latin America's Case–Colombia. In Ozuem, W. & Azemi, Y. Digital Marketing Strategies for Fashion and Luxury Brands. (1Ed, pp. 126-144). IGI Global. https://books.google.com.co/books?hl=es&lr=&id=z-Y7DwAAQBAJ&oi=fnd&pg=PR1&dq= Digital+Marketing+Strategies+for+Fashion+and+Luxury+Brands&ots=gDKmBD5L5E&sig=TqTDBqac2ebNkDbDlQA2_OYGqzQ&redir_esc=y#v=onepage&q=Digital%20Marketing%20Strategies%20for%20Fashion%20and%20Luxury%20Brands&f=false

Powers, T., Advincula, D., Austin, M., Graiko, S. & Snyder, J. (2012). Digital and social media in the purchase decision process. Journal of advertising research, 52(4), 479-489. 10.2501/JAR-52-4-479-489

Reilly, C., Salinas, D. & De Leon, D. (10-13 March 2014). Ranking users based on influence in a directional social network. Computational Science and Computational Intelligence (CSCI), 2014 International Conference. Las Vegas, USA. 10.1109/CSCI.2014.127

Richardson, A. (2010). Using customer journey maps to improve customer experience. Harvard Business Review, 15(1). http://www.iimagineservicedesign.com/wp-content/uploads/2015/09/Using-Customer-Journey-Maps-to-Improve-Customer-Experience.pdf

Riff, D., Lacy, S. & Fico, F. (2014). Analyzing media messages: Using quantitative content analysis in research. Routledge. https://doi.org/10.4324/9780429464287

Rokka, J. (2015). Self-Transformation and Performativity of Social Media Images. NA-Advances in Consumer Research. 43. https://www.acrwebsite.org/volumes/1019697/volumes/v43/NA-43

Smith, A., Fischer, E. & Yongjian, C. (2012). How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113. https://doi.org/10.1016/j.intmar.2012.01.002

Smith, A., Fischer, E. & Yongjian, C. (2011). Differences in brand-related user-generated content across three social media sites: An inductive content analysis. NA-Advances in Consumer Research 39, 766. https://www.acrwebsite.org/volumes/1009529/volumes/v39/NA-39

Steinmann, S., Mau, G. & Schramm‐Klein, H. (2015). Brand communication success in online consumption communities: An experimental analysis of the effects of communication style and brand pictorial representation. Psychology & Marketing, 32(3), 356-371. https://doi.org/10.1002/mar.20784

Stieglitz, S. & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217-248. https://doi.org/10.2753/MIS0742-1222290408

Think With Google. (2013). Introducing Gen C: The YouTube Generation. https://ssl.gstatic.com/think/docs/introducing-gen-c-the-youtube-generation_research-studies.pdf

Twitter. (2016) Privacy policy. https://twitter.com/privacy?lang=es

Vidal, L., Ares, G. & Jaeger, S. (2016). Use of emoticon and emoji in tweets for food-related emotional expression. Food Quality and Preference, 49, 119-128. https://doi.org/10.1016/j.foodqual.2015.12.002

Vidal, L., Ares, G., Machín, L. & Jaeger, S. (2015). Using Twitter data for food-related consumer research: A case study on “what people say when tweeting about different eating situations”. Food Quality and Preference, 45, 58-69. https://doi.org/10.1016/j.foodqual.2015.05.006

Wiedmann, K., Hennigs, N. & Siebels, A. (2009). Value‐based segmentation of luxury consumption behavior. Psychology & Marketing, 26(7), 625-651. https://doi.org/10.1002/mar.20292

Zhang, J. & Mao, E. (2016). From online motivations to ad clicks and to behavioral intentions: An empirical study of consumer response to social media advertising. Psychology & Marketing, 33(3), 155-164. https://doi.org/10.1002/mar.20862