ASSESSING THE VALUE OF BLACK FRIDAY PROMOTIONS: AN ANALYSIS OF INSTAGRAM USERS’ SENTIMENTS AND BEHAVIORAL RESPONSES

Yıl 2024, , 267 - 282, 21.03.2024

Yavuz Selim Balcıoğlu

https://doi.org/10.17753/sosekev.1389245

Öz

Black Friday, as a significant global retail phenomenon, provides substantial insights into consumer behavior and the effectiveness of marketing strategies. This study explores into the dynamics of consumer engagement by analyzing user-generated content (UGC) on Instagram, focusing on the 2021 Black Friday promotions by key technology companies in Turkey. Utilizing an advanced three-step text mining methodology, the research commences with Latent Dirichlet Allocation (LDA) for organizing data into distinct thematic clusters pertinent to Black Friday promotions. This is followed by a sentiment analysis, executed using Python, to evaluate the emotional nuances of the UGC in relation to these themes and the corresponding company promotions. The concluding phase involves an exhaustive textual analysis (TA) to extract actionable insights, which are instrumental in refining promotional strategies and deepening the comprehension of consumer interactions on social media platforms. The results reveal a predominantly positive reception of exclusive promotions and smartphone deals, highlighting their effectiveness as strategic elements in social media marketing. In contrast, themes linked to perceived fraud, negative feedback, misinformation, and customer service issues elicited adverse reactions from consumers. These contrasting responses emphasize the imperative for brands to develop transparent, authentic marketing communications and robust customer support systems. The study not only offers strategic recommendations for brands aiming to enhance their social media campaigns but also contributes a theoretical framework for future research in digital consumer behavior, especially in the context of significant promotional events like Black Friday.

Anahtar Kelimeler

Black friday, Instagram, Sentiment, Behavioral response

Black Friday Promosyonlarının Değerinin Değerlendirilmesi: Instagram Kullanıcılarının Duyguları ve Davranışsal Tepkilerinin Analizi

Öz

Black Friday, önemli bir küresel perakende olgusu olarak, tüketici davranışları ve pazarlama stratejilerinin etkinliği hakkında önemli içgörüler sunmaktadır. Bu çalışma, Türkiye'deki önde gelen teknoloji şirketlerinin 2021 Black Friday promosyonlarına odaklanarak, Instagram'daki kullanıcı tarafından oluşturulan içerikleri (UGC) analiz ederek tüketici etkileşiminin dinamiklerine derinlemesine bir bakış sunmaktadır. İleri düzey üç aşamalı metin madenciliği metodolojisi kullanılarak, araştırma, Black Friday promosyonlarıyla ilgili verileri belirgin tematik kümeler halinde düzenlemek için Latent Dirichlet Allocation (LDA) ile başlamaktadır. Bunu, bu temalar ve ilgili şirket promosyonlarıyla ilişkili UGC'nin duygusal nüanslarını değerlendirmek için Python kullanılarak yapılan duygu analizi takip etmektedir. Son aşama, kapsamlı bir metin analizi (TA) içermekte olup, bu analiz, promosyon stratejilerini geliştirmeye ve sosyal medya platformlarındaki tüketici etkileşimlerini daha derinlemesine anlamaya yönelik eyleme geçirilebilir içgörüler çıkarmaktadır. Sonuçlar, özel promosyonlara ve akıllı telefon fırsatlarına karşı çoğunlukla olumlu bir tepki gösterildiğini, bunların sosyal medya pazarlamasında etkili stratejik unsurlar olarak öne çıktığını göstermektedir. Buna karşılık, algılanan dolandırıcılık, olumsuz geribildirim, yanlış bilgilendirme ve müşteri hizmetleri sorunlarıyla ilişkili temalar tüketicilerden olumsuz tepkiler almıştır. Bu çelişkili yanıtlar, markaların şeffaf, otantik pazarlama iletişimleri geliştirmelerinin ve sağlam müşteri destek sistemleri oluşturmanın zorunluluğunu vurgulamaktadır. Bu çalışma, sosyal medya kampanyalarını geliştirmeyi hedefleyen markalar için stratejik öneriler sunmanın yanı sıra, özellikle Black Friday gibi önemli promosyon etkinlikleri bağlamında dijital tüketici davranışları üzerine gelecekteki araştırmalar için teorik bir çerçeve katkısında bulunmaktadır.

Anahtar Kelimeler

Black friday, Instagram, Duygu analizi, Tepkisel davranış

Etik Beyan

Pazarlama, Yönetim Bilişim Sistemleri

Kaynakça

• Amicucci, A. (2022). ShopSmall because ArtAintFree: Instagram artists’ rhetorical identification with community values. Computers and Composition, 64, 1-16. https://doi.org/10.1016/j.compcom.2022.102710.
• Basilio, M. P., Brum, G. S., & Pereira, V. (2020). A model of policing strategy choice: The integration of the Latent Dirichlet Allocation (LDA) method with ELECTRE I. Journal of Modelling in Management, 15(3), 849-891. https://doi.org/10.1108/JM2-10-2018-0166.
• Bolos, C., Idemudia, E. C., Mai, P., Rasinghani, M., & Smith, S. (2016). Conceptual models on the effectiveness of e-marketing strategies in engaging consumers. Journal of International Technology and Information Management, 25(4), 37-50. https://doi.org/10.58729/1941-6679.1293.
• Bullinaria, J. A., & Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior research methods, 39, 510-526. https://doi.org/10.3758/BF03193020.
• Cachia, R., Compañó, R., & Da Costa, O. (2007). Grasping the potential of online social networks for foresight. Technological Forecasting and Social Change, 74(8), 1179-1203.
• Cailleux, H., Mignot, C., & Kapferer, J. N. (2009). Is CRM for luxury brands? Journal of Brand Management, 16(5-6), 406-412. https://doi.org/10.1057/bm.2008.51.
• Choi, D., & Kim, P. (2013). Sentiment analysis for tracking breaking events: a case study on twitter. In Intelligent Information and Database Systems: 5th Asian Conference, ACIIDS 2013, Kuala Lumpur, Malaysia, March 18-20, 2013, Proceedings, Part II 5 (pp. 285-294). Springer Berlin Heidelberg.
• Chung, S., & Cho, H. (2017). Fostering parasocial relationships with celebrities on social media: Implications for celebrity endorsement. Psychology & Marketing, 34(4), 481-495. https://doi.org/10.1002/mar.21001.
• Daugherty, T., Eastin, M. S., & Bright, L. (2008). Exploring consumer motivations for creating user-generated content. Journal of interactive advertising, 8(2), 16-25. https://doi.org/10.1080/15252019.2008.10722139.
• De Chernatony, L., & Riley, F. D. O. (1999). Experts' views about defining services brands and the principles of services branding. Journal of Business Research, 46(2), 181-192. https://doi.org/10.1016/S0148-2963(98)00021-6.
• de Oliveira, L. M., & Goussevskaia, O. (2020, December). Topic trends and user engagement on Instagram. In 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (pp. 488-495). IEEE. https://doi.org/10.1109/WIIAT50758.2020.00073.
• Delamater, R. J., & Mcnamara, J. R. (1986). The social impact of assertiveness: Research findings and clinical implications. Behavior Modification, 10(2), 139-158. https://doi.org/10.1177/01454455860102001.
• de la Torre-Abaitua, G., Lago-Fernández, L. F., & Arroyo, D. (2021). A compression-based method for detecting anomalies in textual data. Entropy, 23(5), 618. https://doi.org/10.3390/e23050618.
• De Lisle, J. (2011). The benefits and challenges of mixing methods and methodologies: Lessons learnt from implementing qualitatively led mixed methods research designs in Trinidad and Tobago. Caribbean curriculum, 18, 87-120.
• De Swert, K. (2012). Calculating inter-coder reliability in media content analysis using Krippendorff’s Alpha. Center for Politics and Communication, 15, 1-15.
• Elsbree, C. (2022). Black Friday Pricing Behavior at Walmart, Bachelor thesis, Oregon State University.
• Farenga, L. (2012). The Financial Crisis and Repercussions for the Insurance Sector. Rivista trimestrale di diritto dell’economia, (4), 254-279.
• Fisman, R., & Svensson, J. (2007). Are corruption and taxation really harmful to growth? Firm level evidence. Journal of development economics, 83(1), 63-75. https://doi.org/10.1016/j.jdeveco.2005.09.009.
• Giannakis, M., Dubey, R., Yan, S., Spanaki, K., & Papadopoulos, T. (2022). Social media and sensemaking patterns in new product development: demystifying the customer sentiment. Annals of Operations Research, 308, 145-175. https://doi.org/10.1007/s10479-020-03775-6.
• Gupta, A., & Jhunjhunwala, K. (2016). Analysing brand sentiment with social media and open source Big Data tools. Journal of Digital & Social Media Marketing, 3(4), 338-347.
• Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing science, 19(1), 4-21. https://doi.org/10.1287/mksc.19.1.4.15178.
• He, W., Tian, X., Chen, Y., & Chong, D. (2016). Actionable social media competitive analytics for understanding customer experiences. Journal of Computer Information Systems, 56(2), 145-155.
• Hollebeek, L. D., & Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. Journal of interactive marketing, 45(1), 27-41.
• Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision support systems, 52(3), 674-684.
• Jin, S. V., & Ryu, E. (2020). “I'll buy what she's# wearing”: The roles of envy toward and parasocial interaction with influencers in Instagram celebrity-based brand endorsement and social commerce. Journal of Retailing and Consumer Services, 55, 102121. https://doi.org/10.1016/j.jretconser.2020.102121.
• Kinanti, L. A. B., Dewatmoko, S., & Abdillah, F. (2023). The Influence Of Environmental Factors And Content Personalization On Consumer Engagement In Marketing Campaigns With Consumer Perceived Value As A Mediator. Management Studies and Entrepreneurship Journal (MSEJ), 4(6), 9810-9818.
• Kralj Novak, P., Smailović, J., Sluban, B., & Mozetič, I. (2015). Sentiment of emojis. PloS one, 10(12), e0144296. https://doi.org/10.1371/journal.pone.0144296.
• Krippendorff, K. (1995). On the reliability of unitizing continuous data. Sociological Methodology, 47-76. https://doi.org/10.2307/271061.
• Levine, R. A., & Casella, G. (2006). Optimizing random scan Gibbs samplers. Journal of Multivariate Analysis, 97(10), 2071-2100. https://doi.org/10.1016/j.jmva.2006.05.008.
• Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49, 51-70.
• Li, Y., Zhang, Z., Peng, Y., Yin, H., & Xu, Q. (2018). Matching user accounts based on user generated content across social networks. Future Generation Computer Systems, 83, 104-115. https://doi.org/10.1016/j.future.2018.01.041.
• Li, S. G., Zhang, Y. Q., Yu, Z. X., & Liu, F. (2021). Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process. Electronic Commerce Research, 21, 1083-1112. https://doi.org/10.1007/s10660-020-09401-8.
• McAuley, J., & Leskovec, J. (2013, October). Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems (pp. 165-172).
• McEnally, M., & De Chernatony, L. (1999). The evolving nature of branding: Consumer and managerial considerations. Academy of Marketing Science Review, 2(1), 1-16.
• Noble, C. H., & Mokwa, M. P. (1999). Implementing marketing strategies: Developing and testing a managerial theory. Journal of marketing, 63(4), 57-73. https://doi.org/10.1177/002224299906300406.
• Paschen, J., Wilson, M., & Robson, K. (2020). # BuyNothingDay: Investigating consumer restraint using hybrid content analysis of Twitter data. European Journal of Marketing, 54(2), 327-350. https://doi.org/10.1108/EJM-01-2019-0063.
• Phinzi, K., Abriha, D., & Szabó, S. (2021). Classification efficacy using k-fold cross-validation and bootstrapping resampling techniques on the example of mapping complex gully systems. Remote Sensing, 13(15), 2980. https://doi.org/10.3390/rs13152980.
• Recuero-Virto, N., & Valilla-Arrospide, C. (2022). Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction. British Food Journal, 124(12), 4339-4353. https://doi.org/10.1108/BFJ-07-2021-0803.
• Reyes-Menendez, A., Saura, J. R., & Filipe, F. (2020). Marketing challenges in the# MeToo era: Gaining business insights using an exploratory sentiment analysis. Heliyon, 6(3). https://doi.org/10.1016/j.heliyon.2020.e03626.
• Rohm, A., D. Kaltcheva, V., & R. Milne, G. (2013). A mixed-method approach to examining brand-consumer interactions driven by social media. Journal of research in Interactive Marketing, 7(4), 295-311. https://doi.org/10.1108/JRIM-01-2013-0009.
• Ross, A. S., Roth, J., & Waxman, I. (2008). Resource-intensive endoscopic procedures: Do the dollars make sense? Gastrointestinal Endoscopy, 68(4), 642-646. https://doi.org/10.1016/j.gie.2008.02.056.
• Sailer, A., Wilfing, H., & Straus, E. (2022). Greenwashing and bluewashing in black Friday-related sustainable fashion marketing on Instagram. Sustainability, 14(3), 1494. https://doi.org/10.3390/su14031494.
• Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Evaluating security and privacy issues of social networks based information systems in Industry 4.0. Enterprise Information Systems, 16(10-11), 1694-1710. https://doi.org/10.1080/17517575.2021.1913765.
• Sbaraini Fontes, G., & Marques, F. P. J. (2023). Defending democracy or amplifying populism? Journalistic coverage, Twitter, and users’ engagement in Bolsonaro’s Brazil. Journalism, 24(8), 1634-1656. https://doi.org/10.1177/14648849221075429.
• Shukla, P. S., & Nigam, P. V. (2018). E-shopping using mobile apps and the emerging consumer in the digital age of retail hyper personalization: An insight. Pacific Business Review International, 10(10), 131-139.
• Sobh, R., & Martin, B. A. (2011). Feedback information and consumer motivation: the moderating role of positive and negative reference values in self‐regulation. European Journal of Marketing, 45(6), 963-986. https://doi.org/10.1108/03090561111119976.
• Okdie, B. M., Guadagno, R. E., Bernieri, F. J., Geers, A. L., & Mclarney-Vesotski, A. R. (2011). Getting to know you: Face-to-face versus online interactions. Computers in human behavior, 27(1), 153-159. https://doi.org/10.1016/j.chb.2010.07.017.
• Van Heuven, W. J., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). SUBTLEX-UK: A new and improved word frequency database for British English. Quarterly journal of experimental psychology, 67(6), 1176-1190. https://doi.org/10.1080/17470218.2013.850521.
• Wang, T., Thai, T. D. H., Ly, P. T. M., & Chi, T. P. (2021). Turning social endorsement into brand passion. Journal of Business Research, 126, 429-439. https://doi.org/10.1016/j.jbusres.2021.01.011.
• Zimmer, L. (2006). Qualitative meta‐synthesis: a question of dialoguing with texts. Journal of advanced nursing, 53(3), 311-318. https://doi.org/10.1111/j.1365-2648.2006.03721.x.
• Zheng, X., Cheung, C. M., Lee, M. K., & Liang, L. (2015). Building brand loyalty through user engagement in online brand communities in social networking sites. Information Technology & People, 28(1), 90-106. https://doi.org/10.1108/ITP-08-2013-0144.