Using Big Data In Analysis Of Consumer Behavior: A Qualitative Study

Year 2023, , 100 - 122, 28.03.2023

Anıl Değermen , Maryam Mohammadabbasi

https://doi.org/10.53306/klujfeas.1220342

Abstract

Big data, which has been used intensively in the field of marketing, is of great importance in the analysis of consumer behavior, in predetermining the changes in consumer needs and wants that may occur in the future, and in the development of marketing strategies suitable for these wishes and needs. Yet, there are few empirical studies of consumer behavior with big data. In this context, the aim of the study is to discuss how big data is used in the analysis of consumer behavior and to reveal its advantages. In addition to this, it is also aimed to observe the practitioners' attitudes on how big data determines consumer behavior. For this purpose, the interview technique was chosen in order to collect data in the study. In the survey, senior managers of 10 different companies in Istanbul, which are currently using big data, were interviewed as field research, and qualitative data analysis was carried out in the NVIVO program. As a result of the analysis, it is seen that companies obtain comprehensive information about consumers by using big data. In light of this information collected, companies can predict consumer behavior, carry out their digitalization activities more efficiently, and develop data-based consumer-specific advertisements.

Keywords

Big Data, Digital Marketing, Consumer Behavior, Social Media

Tüketici Davranışı Analizinde Büyük Verinin Kullanımı: Nitel Bir Çalışma

Abstract

Pazarlama alanında yoğun bir biçimde kullanılmaya başlanan büyük veri, tüketici davranışlarının analizinde, gelecekte oluşabilecek tüketici istek ve ihtiyaç değişikliklerinin önceden belirlemesinde ve söz konusu istek ve ihtiyaçlara uygun pazarlama stratejilerinin geliştirilmesinde büyük önem taşımaktadır. Yine de, büyük verilerle tüketici davranışlarına ilişkin çok az ampirik çalışma var. Bu bağlamda yapılan çalışma ile amaçlanan, büyük verinin tüketici davranışlarının analizinde nasıl ve ne şekilde kullanıldığını ele almak ve tüketici davranışlarının büyük veri kullanımıyla analizinin avantajlarını ortaya koymaktır. Buna ek olarak, büyük verinin tüketici davranışlarını nasıl belirlediğine dair uygulayıcıların görüşlerinin de gözlemlenmesi amaçlanmaktadır. Bu amaçla çalışmada veri toplamak amacıyla görüşme tekniği seçilmiştir. Çalışmanın saha araştırması olarak İstanbul ilinde yer alan ve büyük veri kullanan 10 farklı şirketin üst düzey yöneticileri ile görüşmeler yapılmıştır ve NVIVO programında nitel veri analizi yapılmıştır. Yapılan analizler sonucunda, şirketlerin büyük veriyi kullanarak tüketiciler hakkında kapsamlı bilgiler elde ettiği görülmektedir. Şirketler, toplanan bu bilgiler ışığında tüketici davranışlarını tahmin edebilmekte, dijitalleşme faaliyetlerini daha verimli yürütebilmekte ve veriye dayalı tüketiciye özel reklamlar geliştirebilmektedir.

Keywords

Büyük Veri, Dijital Pazarlama, Tüketici Davranışları, Sosyal Medya

References

• Aktan, E. (2018). Büyük Veri: Uygulama Alanları, Analitiği ve Güvenlik Boyutu. Bilgi Yönetimi, 1(1), 1–22. https://doi.org/10.33721/by.403010.
• Angwin, J. (2011). Latest in Web Tracking: Stealthy ‘Supercookies’. The Wall Street Journal, 19 August 2011, 2.
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
• Bauer, C., & Strauss, C. (2016). Location-Based Advertising On Mobile Devices. Management Review Quarterly, 66(3), 159-194.
• Berber, L. K. (2014). Çevrimiçi Davranışsal Reklamcılık (Online Behavioral Advertising) Uygulamaları Özelinde Kişisel Verilerin Korunması, İstanbul: On İki Levha Yayıncılık.
• Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online Behavioral Advertising: A Literature Review And Research Agenda. Journal Of Advertising, 46(3), 363-376.
• Brand Finance. (2021). Media 25 2021. https://brandfinance.com/images/upload/media_25_free Bujlow, T., Carela-Español, V., Solé-Pareta, J., & Barlet-Ros, P. (2015). Web Tracking: Mechanisms, Implications, and Defenses arXiv preprint arXiv:1507,07872.
• Burnham, K. (2014). Facebook’s WhatsApp Buy: 10 Staggering Stats. https://www.informationweek.com/software/social/facebooks-whatsapp-buy-10-stagger ing-stats- /d/d-id/1113927
• Castelluccia, C., & Narayanan, A. (2012). Privacy Considerations Of Online Behavioural Tracking. European Network and Information Security Agency (ENISA).
• Chan, A. T. S. (2004). Cookies On-the-Move: Managing Cookies on a Smart Card. Proceedings of the 2004 ACM Symposium On Applied Computing, 1693-1697.
• Chandra, S., Ray, S., & Goswami, R. T. (2017, January). Big Data Security: Survey on Frameworks and Algorithms. 2017 IEEE 7th International Advance Computing Conference (IACC), 48-54.
• Cox, M., & Ellsworth, D. (1997). Application-Controlled Demand Paging for Out-of-core Visualization. Proceedings. Visualization'97 (Cat. No. 97CB36155), 235-244.
• Cyganek, B., Graña, M., Krawczyk, B., Kasprzak, A., Porwik, P., Walkowiak, K. and Woźniak, M. (2016). A Survey of Big Data Issues in Electronic Health Record Analysis. Applied Artificial Intelligence, 30(6), 497-520.
• Davenport, T. (2018). Big Data @Work. M. Çavdar (çev.) İstanbul: Türk Hava Yolları Yayınları.
• Debattista, J., Lange, C., Scerri, S., & Auer, S. (2015, December). Linked ‘Big’ Data: Towards a Manifold Increase in Big Data Value and Veracity. In 2015 IEEE/ACM 2nd International Symposium on Big Data Computing (BDC), 92-98.
• Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the Big Data Ecosystem. 2014 International conference on collaboration technologies and systems (CTS), 104-112).
• Demirağ, B. (2017). Marka Konumlandırma Stratejilerinin Belirlenmesinde Hedef Pazar Seçimi ve Tüketici Algılamalarına Dayalı Gerçekleştirilen Konumlandırma Strateji Hatalarına İlişkin Çözüm Önerileri. Route Educational and Social Science Journal, 4(7), 449-464.
• Desjardins, J. (2015). Order From Chaos: How Big Data Will Change the World. Retrieved from https://www.visualcapitalist.com/order-from-chaos-how-big-data-will-change-the-world/
• Diebold, F. X. (2003). Big Data Dynamic Factor Models For Macroeconomic Measurement And Forecasting. Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society,” (edited by M. Dewatripont, LP Hansen and S. Turnovsky, 115-122.
• Doğan, K., & Arslantekin, S. (2016). Büyük Veri: Önemi, Yapısı ve Günümüzdeki Durum. Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi, 56(1).
• DOMO. (2018). Data Never Sleeps 6. https://www.domo.com/learn/datanever-sleeps 6 Dülger, Ü. (2016). Büyük Veri Nedir. Yeni Türkiye, 89,503-508.
• Eckersley, P. (2014). How Unique Is Your Web Browser?. Electronic Frontier Foundation, Retrieved 16 January 2016. Ege, B. (2013). Rastlantının Bittiği Yer Big Data. Bilim ve Teknik, 550, 22-26.
• Electronic Frontier Foundation. (2009). Online Behavioral Tracking and Targeting Concerns and Solutions from the Perspective of: Center for Digital Democracy, Consumer Federation of America, Consumers Union, Consumer Watchdog, 160 Electronic Frontier Foundation, Privacy Lives, Privacy Rights Clearinghouse, Privacy Times, U.S. Public Interest Research Group, The World Privacy Forum. Legislative Primer, https://www.eff.org/files/onlineprivacylegprimersept09
• Ertemel, A. V. (2015). Consumer Insight As Competitive Advantage Using Big Data And Analytics. International Journal of Commerce and Finance, 1(1), 45-51.
• Fang, Z., & Li, P. (2014). The Mechanism Of “Big Data” Impact On Consumer Behavior. American Journal of Industrial and Business Management, 4, 45-50.
• Gahi, Y., Guennoun, M., & Mouftah, H. T. (2016). Big Data Analytics: Security and Privacy Challenges. 2016 IEEE Symposium on Computers and Communication (ISCC), 952-957.
• Gandomi, A., & Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, And Analytics. International Journal Of İnformation Management, 35(2), 137-144.
• Gerhardt, B., Griffin, K., & Klemann, R. (2012). Unlocking Value in the Fragmented World of Big Data Analytics How Information Infomediaries Will Create a New Data Ecosystem. Cisco Internet Business Solutions Group, 7. Ghoreishi, M. et al. (2017). A Thematic Analysis of the Conceptual Framework of E-Learning in Higher Education. Interdisciplinary Journal of Virtual Learning in Medical Sciences. 8(1): e11498.
• Goes, P. B. (2014). Big Data and IS Research. MIS Quarterly: Management Information Systems, 38(3), iii-viii.
• Gökalp, M. O., Kayabay, K., Çoban, S., Yandık, Y. B., & Eren, P. E. (2018). Büyük Veri Çağında İşletmelerde Veri Bilimi. 5th International Management Information Systems Conference, 94-97.
• Gunday, G., Ulusoy, G., Kilic, K., & Alpkan, L. (2011). Effects Of Innovation Types On Firm Performance. International Journal of Production Economics, 133(2), 662-676.
• Hasdemir, U. (Ed.). (2005). A’dan Z’ye Pazarlama. İstanbul: Kapital Medya Hizmetleri A.Ş.
• Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016). Big Data and Consumer Behavior: İmminent Opportunities. Journal of Consumer Marketing, 33(2), 89-97.
• Hurwitz, J., Nugent, A., Halper, D. F., & Kaufman, M. (2013). Big Data for Dummies, John Wiley & Sons Inc. Hoboken, NJ, USA, 12-48.
• Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R., & Buyya, R. (2016). The Anatomy Of Big Data Computing. Software: Practice and Experience, 46(1), 79-105.
• Kurtbaş, İ. (2016). Marka Yönetimi and Başarılı Markanın Yarar ve Etkileri. Karadeniz Uluslararası Bilimsel Dergi, (32), 75-98.
• Laperdrix, P., Bielova, N., Baudry, B., & Avoine, G. (2020). Browser fingerprinting: A survey. ACM Transactions on the Web (TWEB), 14(2), 1-33.
• Lueth, K. L. (2018). State of the IoT 2018: Number of IoT Devices Now At 7B – Market Accelerating. IOT ANALYTICS. https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/ Nill, A., & Aalberts, R. J. (2014). Legal and Ethical Challenges of Online Behavioral Targeting in Advertising. Journal Of Current Issues & Research In Advertising, 35(2), 126-146.
• Office of the Privacy Commissioner of Canada. (2011). Report on the 2010 Office of the Privacy Commissioner of Canada’s Consultations on Online Tracking. Profiling and Targeting, and Cloud Computing. https://www.priv.gc.ca/media/1961/report_201105_e.pdf
• Ohlhorst, F. J. (2013). Big Data Analytics: Turning Big Data Into Big Money, USA: John Wiley & Sons. Pesenson, M. Z., Pesenson, I. Z., & McCollum, B. (2010). The Data Big Bang And The Expanding Digital Universe: High-Dimensional, Complex And Massive Data Sets In An Inflationary Epoch. Advances in Astronomy, 1–16.
• Radicati, S., & Levenstein, J. (2015). Email Statistics Report, Palo Alto, CA, U.S.A.: The Radicati Group, Inc. Simsek, H., & Yildirim, A. (2000). Vocational Schools İn Turkey: An Administrative And Organizational Analysis. International Review of Education, 46(3), 327-342.
• Sun, H., & Heller, P. (2012). Oracle Information Architecture: An Architect’s Guide to Big Data. Oracle, Redwood Shores.
• Syverson, P., & Traudt, M. (2018). {HSTS} Supports Targeted Surveillance. 8th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 18).
• Şener E. (2017). 2017 Big Data (Büyük Veri) Trendleri. Digital Age. https://digitalage.com.tr/2017-big-data-buyuk-veri-trendleri/
• Taşdelen, H., & Şentürk, Z. A. (2018). İnternet Reklamcılığında Davranışsal Hedeflemenin Tüketici Satın Alma Davranışına Etkisi. İNİF E-Dergi, 3(2), 175-190.
• Tirtea, R., Castelluccia, C., & Ikonomou, D. (2011). Bittersweet cookies. Some security and privacy considerations. European Union Agency for Network and Information Security-ENISA.
• Tracy, S. J. (2013). Qualitative Research Methods, Collecting Evidence, Crafting Analysis, Communicating Impact. Blackwell Publishing, John Wiley & Sons.
• Turner, D., Shroeck, M., & Shockly, R . (2012). Analytics: The real-world use of big data in financial services. IBM Global Business Services, 1-12. https://www.ibm.com/downloads/cas/E4BWZ1PY
• Twitter Usage Statistics. (t.y.). (2021). https://www.internetlivestats.com/twitter-statistics/
• Vijayarani, S., & Sharmila, S. (2016). Research In Big Data: An Overview. Informatics Engineering. International Journal (IEIJ), 4 (3), 1-20.
• Wang, X., & He, Y. (2016). Learning From Uncertainty For Big Data: Future Analytical Challenges And Strategies. IEEE Systems, Man, and Cybernetics Magazine, 2(2), 26-31.
• Yavuz. Ş. (2013). Türk Toplumunun Tüketim Toplumuna Dönüşümünde Reklamcılığın Rolü. İletişim Kuram ve Araştırma Dergisi, (36), 220- 240.
• Zhang, C., & Tan, T. (2020, May). The Impact of Big Data Analysis on Consumer Behavior. Journal of Physics: Conference Series, 1544(1), 012165.
• Zhang, Y., Ren, J., Liu, J., Xu, C., Guo, H., & Liu, Y. (2017). A Survey On Emerging Computing Paradigms For Big Data. Chinese Journal of Electronics, 26(1), 1-12.