Dijital Pazarlama Stratejilerinde Doğal Dil İşleme Kullanımı
Özet
Bu çalışma, dijital pazarlama stratejilerinde Doğal Dil İşleme (DDİ) kullanımını, ilgili yöntemleri ve pazarlama kavramının tarihsel süreçte Pazarlama 1.0’dan Pazarlama 4.0’a evrilmesini ele almaktadır. Teknolojik gelişmeler ve dijitalleşme, geleneksel tek yönlü iletişim modellerini ortadan kaldırarak iki yönlü, etkileşimli ve kişiselleştirilmiş dijital pazarlama yöntemlerine geçişi hızlandırmıştır. Sosyal medya, bloglar, web siteleri ve chatbotlar gibi dijital araçlar aracılığıyla üretilen büyük boyuttaki metin tabanlı ham verilerin analiz edilmesi, firmaların rekabet gücünü artırması ve trendlere ayak uydurması açısından kritik bir önem taşımaktadır. Bu aşamada yapay zekanın alt alanı olan DDİ; Doğal Dil Anlayışı (NLU) ve Doğal Dil Üretimi (NLG) teknolojileriyle metinsel verileri filtreleyip sınıflandırarak karar vericilere anlamlı çıktılar sunar. Tarihsel süreçte Turing Makinesi'nden günümüze uzanan DDİ, günümüzde tüketici davranışlarını, ağızdan ağıza pazarlamayı (e-WOM) ve marka algısını ölçmede etkin rol oynamaktadır. Literatürde LDA, HMM, CNN ve LSTM gibi çeşitli algoritmalar ve modeller kullanılarak eşler arası platformlar, sosyal medya reklamları ve çevrimiçi incelemeler başarıyla analiz edilmektedir. Sonuç olarak DDİ yöntemleri, müşteri merkeziyetçi yaklaşımlarla firmaların küresel pazarda tercih edilebilirliğini artırırken, kişisel verilerin güvenliği gibi önemli sorumlulukları ve kısıtlılıkları da beraberinde getirmektedir.
This study addresses the use of Natural Language Processing (NLP) in digital marketing strategies, the related methods, and the historical evolution of the marketing concept from Marketing 1.0 to Marketing 4.0. Technological advancements and digitalization have accelerated the transition from traditional one-way communication models to two-way, interactive, and personalized digital marketing methods. Analyzing large volumes of text-based raw data generated through digital tools such as social media, blogs, websites, and chatbots is of critical importance for companies to increase their competitiveness and keep pace with trends. At this stage, NLP, a subfield of artificial intelligence, filters and classifies textual data through Natural Language Understanding (NLU) and Natural Language Generation (NLG) technologies, providing meaningful outputs to decision-makers. Developing historically from the Turing Machine to the present day, NLP plays an active role today in measuring consumer behavior, electronic word-of-mouth (e-WOM), and brand perception. In the literature, peer-to-peer platforms, social media advertisements, and online reviews are successfully analyzed using various algorithms and models such as LDA, HMM, CNN, and LSTM. Consequently, while NLP methods increase the preferability of companies in the global market through customer-centric approaches, they also bring along significant responsibilities and limitations, such as the security of personal data.
Referanslar
Amerikan Pazarlama Derneği, Definitions of Marketing, Web Sayfası, Erişim Tarihi: 20/12/2022, Web Adresi: www.ama.org/the-definition-of-marketing-what-is-marketing/
B. Andhyka, Marketing 4.0 a literature review, IOSR Journal of Business and Management (IOSRJBM), 22(4), 49–52, 2020.
A. J. Jara, M. C. Parra ve A. F. Skarmeta, Marketing 4.0: A New Value Added to the Marketing through the Internet of Things, Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Palermo, İtalya, 2012.
P. Kotler, H. Kartajaya ve I. Setiawan, Bölüm 3: Misyonun Tüketicilere Pazarlanması, Marketing 3.0: produits, clients, facteur humain, ISBN: 2804171302, De Boeck, 63-81, 2012.
L. Alexander, The Who, What, Why, & How of Digital Marketing, Web Sayfası, Erişim Tarihi: 21/12/2022, Web Adresi: https://blog.hubspot.com/marketing/what-is-digital-marketing.
B. Wierenga, Bölüm 1: Marketing and Artificial Intelligence: Great Opportunities, Marketing Intelligent Systems Using Soft Computing: Managerial and Research Applications, ISBN: 978-3-642-15605-2, Springer, 1–8, 2010.
K. McCormick, 6 Most Popular Social Media Platforms 2022- Unobvious Intel WordStream, Web Sayfası, Erişim Tarihi: 15/12/2022, Web Adresi: https://www.wordstream.com/blog/ws/2022/01/11/most-popular-social-media-platforms
Euromonitor, Pandemic Gives Rise to More Sophisticated Online Shoppers, Web Sayfası, Erişim Tarihi: 13/12/2022, Web Adresi: www.euromonitor.com/article/pandemic-gives-rise-to-more-sophisticated-online-shoppers
Euromonitor, What Are the 10 Global Consumer Trends in 2022, Web Sayfası, Erişim Tarihi:18/12/2022, Web Adresi: https://www.euromonitor.com/article/what-are-the-10-global-consumer-trends-in-2022
H. Kartajaya, P. Kotler, ve D. H. Hooi, Bölüm 4: Marketing 4.0: moving from traditional to digital, Asian Competitors Marketing for Competitiveness in the Age of Digital Consumers, ISBN: 11135, World Scientific, 99–123, 2019.
S. Rao, V. Srivatsala ve V. Suneetha, Optimizing Technical Ecosystem of Digital Marketing, Artificial Intelligence and Evolutionary Computations in Engineering Systems, 691–703, 2016.
T. P. Nagarhalli, V. Vaze ve N. K. Rana, Impact of Machine Learning in Natural Language Processing: A Review, 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021.
R. C. Schank, Conceptual dependency: A theory of natural language understanding, Cognitive Psychology, 3(4), 552–631, 1972.
R. Dale, H. Moisl ve H. Somers, Handbook of Natural Language Processing. CRC Press, 2000.
P. E. Genest ve G. Lapalme, Framework for abstractive summarization using text-to-text generation, Proceedings of the workshop on monolingual text-to-text generation, 64-73, 2011.
T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang ve X. He, “Attngan: Fine-grained text to image generation with attentional generative adversarial networks, arXiv:1711.10485, 2018.
M. Rohrbach, W. Qiu, I. Titov, S. Thater, M. Pinkal ve B. Schiele, Translating video content to natural language descriptions, Proceedings of the IEEE international conference on computer vision, Sydney, Australia, 2013.
Y. Kang, Z. Cai, C.-W. Tan, Q. Huang ve H. Liu, Natural language processing (NLP) in management research: A literature review, Journal of Management Analytics, 7(2), 139–172, 2020.
S. Gabel, D. Guhl ve D. Klapper, P2V-MAP: Mapping Market Structures for Large Retail Assortments, J. Mark. Res., 56(4), 557–580, 2019.
T. Y. Lee ve E. T. Bradlow, Automated Marketing Research Using Online Customer Reviews, Journal of Marketing Research, 48(5), 881–894, 2011.
S. R. Das ve M. Y. Chen, Yahoo! for Amazon: Sentiment extraction from small talk on the web, Manage. Sci., 53(9), 1375–1388, 2007.
O. Toubia, G. Iyengar, R. Bunnell ve A. Lemaire, Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption, J. Mark. Res., 56(1), 18–36, 2019.
A. Ansari, Y. Li ve J. Z. Zhang, Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach, Mark. Sci., 37(6), 987–1008, 2018.
J. Liu ve O. Toubia, A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries, Mark. Sci., 37(6), 930–952, 2018.
J. Hartmann, J. Huppertz, C. Schamp ve M. Heitmann, Comparing automated text classification methods, International Journal of Research in Marketing, 36(1), 20–38, 2019.
A. Humphreys, R. J. H. Wang, Automated text analysis for consumer research, Journal of Consumer Research, 44(6), 1274-1306, 2018.
V. Shankar ve S. Parsana, An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing, J. of the Acad. Mark. Sci., 50(6), 1324–1350, 2022.
F. Caldieraro, J. Z. Zhang, M. Jr. Cunha ve J. D. Shulman, Strategic information transmission in peer-to-peer lending markets. Journal of Marketing, 82(2), 42-63, 2018.
G. Xiong ve S. Bharadwaj, Prerelease Buzz Evolution Patterns and New Product Performance, Marketing Science, 33(3), 401–421, 2014.
X. Liu, P. V. Singh ve K. Srinivasan, A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing, Marketing Science, 35(3), 363–388, 2016.
M. Hermosilla, F. Gutiérrez-Navratil ve J. Prieto-Rodríguez, Can Emerging Markets Tilt Global Product Design? Impacts of Chinese Colorism on Hollywood Castings, Marketing Science, 37(3), 356–381, 2018.
D. Lee, K. Hosanagar ve H. S. Nair, Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook, Management Science, 64(11), 5105–5131, 2018.
D. Herhausen, S. Ludwig, D. Grewal, J. Wulf ve M. Schoegel, Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities, J. Mark., 83(3), 1–21, 2019.
F. Villarroel Ordenes, D. Grewal, S. Ludwig, K. D. Ruyter, D. Mahr ve M. Wetzels, Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages, Journal of Consumer Research, 45(5), 988–1012, 2019.
S. Melumad, J. J. Inman ve M. T. Pham, Selectively Emotional: How Smartphone Use Changes User-Generated Content, Journal of Marketing Research, 56(2), 259–275, 2019.
N. Archak, A. Ghose ve P. G. Ipeirotis, Deriving the Pricing Power of Product Features by Mining Consumer Reviews, Management Science, 57(8), 1485–1509, 2011.
S. Tirunillai ve G. J. Tellis, Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation, Journal of Marketing Research, 51(4), 463–479, 2014.
F. Villarroel Ordenes, S. Ludwig, K. de Ruyter, D. Grewal ve M. Wetzels, 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, 2017.
X. Liu, D. Lee ve K. Srinivasan, Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning, J. Mark. Res., 56(6), 918–943, 2019.
J. Büschken ve G. M. Allenby, Sentence-based text analysis for customer reviews, Marketing Science, 35(6), 953-975, 2016.
J. Büschken ve G. M. Allenby, Improving Text Analysis Using Sentence Conjunctions and Punctuation, Marketing Science, 39(4), 727–742, 2020.
O. J. Rutz, G. P. Sonnier ve M. Trusov, A New Method to Aid Copy Testing of Paid Search Text Advertisements, Journal of Marketing Research, 54(6), 885–900, 2017.
T. Dotzel ve V. Shankar, The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis, Journal of Marketing, 83(5), 133–152, 2019.
M. Hu, C. Dang ve P. K. Chintagunta, Search and Learning at a Daily Deals Website, Marketing Science, 38(4), 609–642, 2019.
J. Berger, A. Humphreys, S. Ludwig, W. W. Moe, O. Netzer ve D. A. Schweidel, Uniting the Tribes: Using Text for Marketing Insight, Journal of Marketing, 84(1), 1–25, 2020.
D. Hovy, S. Melumad ve J. J. Inman, Wordify: A Tool for Discovering and Differentiating Consumer Vocabularies, Journal of Consumer Research, 48(3), 394–414, 2021.
N. Y. Jalali ve P. Papatla, Composing tweets to increase retweets, International Journal of Research in Marketing, 36(4), 647–668, 2019.
S. A. M. Vermeer, T. Araujo, S. F. Bernritter ve G. van Noort, Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media, International Journal of Research in Marketing, 36(3), 492–508,2019.
O. Netzer, R. Feldman, J. Goldenberg ve M. Fresko, Mine Your Own Business: Market-Structure Surveillance Through Text Mining, Marketing Science, 31(3), 521–543, 2012.
N. Zhong and D. A. Schweidel, Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model, Marketing Science, 39(4), 827–846, 2020.
