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DİJİTAL PAZARLAMADA YAPAY ZEKA VE MAKİNE ÖĞRENİMİ KULLANIMI

Year 2023, Volume: 22 Issue: 85, 135 - 142, 27.01.2023
https://doi.org/10.17755/esosder.1152771

Abstract

Günümüzde toplumlar iki büyük zorlukla karşı karşıyadır bunlar: teknoloji ve insan potansiyelidir. Robotların işyerlerinde insanların yerini alacağından endişelenen analistler, gazeteciler ve büyük beyinler arasında bir süredir yapay zeka hayaletleri dolaşmaktadır. Yapay zeka sayesinde yaratılan sohbet robotları kullanıcı deneyimini iyileştirmektedir ve böylece dijital pazarlama uzmanları için en güçlü modern araçlardan biri haline gelmiştir. Pazarlamada makine öğreniminin uygulanması, insan davranışını anlamaya ve tahmin etmeye imkan tanımaktadır. Davranışı, geliri, fiyat farkı yüzdesi, pazar payı, likidite, uzun vadede müşteri değeri ve müşteriyi elde tutma olasılığı gibi temel faktörlerin analiz edilip uygulanması müşteri tabanlı pazarlama stratejisi ile sonuçlanmaktadır. Bu makalenin amacı, pazarlamada yapay zekanın uygulama alanlarını ve olanaklarını araştırmaktır. Hem fiziksel hem de bilişsel faaliyetlerin yapay zekâ ve makine öğrenimi ile değiştirilebileceği varsayımına odaklanan bu çalışma, tanımlayıcı bir araştırma tekniği kullanarak yapay zekânın dijital pazarlamadaki rolünü araştırmıştır. Pazarlama ve yapay zekâ ile ilgili çeşitli sektörlerden profesyonellerle yapılan bir inceleme, yapay zekânın pazarlama operasyonları üzerinde bir etkisi olduğunu ve gelecekte daha büyük bir etkiye sahip olacağını ortaya koymaktadır. Veri odaklı yeni yöntemleri dijital pazarlama stratejileri ile bütünleştirmek şirketlere teknik ilerleme açısından stratejik avantaj sağlamaktadır. Üretilen büyük hacimli verilerden alınan dersleri belirleyerek, makine öğrenimi gelecekteki olayları tahmin edebilir ve karar alma sürecine yardımcı olabilmektedir. Bu özellik, işletmelerin stratejik karar alma süreçleri üzerinde önemli bir etkiye sahiptir ve basitleştirmektedir. Çalışmanın ihtiyaç değerlendirmesine göre, pazarlamacıların ML teknolojilerine yönelik tutumları ve anlayışlarının yanı sıra operasyonel ve stratejik yönetimi desteklemek için alımları ve kullanımları hakkında çok az şey bilinmektedir. Akıllı robotik teknolojiler şirketlerin internet pazarlaması için ideal olarak görülmektedir. Kullanılan bu teknolojiler sayesinde tüketicilerin ne istediği belirlenebilmektedir ve teklifleri özelleştirilmektedir. Toplanan veriler ile içerik oluşturmak basitleşmektedir. Böylelikle çok sayıda veri toplanarak iş seçeneklerine dönüştürülmektedir.

References

  • • Boddu, R. S. K., Santoki, A. A., Khurana, S., Koli, P. V., Rai, R., & Agrawal, A. (2022). An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing. Materials Today: Proceedings, 56, 2288-2292.
  • • Cheng, F. C., & Wang, Y. S. (2018). The do not track mechanism for digital footprint privacy protection in marketing applications. Journal of Business Economics and Management, 19(2), 253-267.
  • • Chittenden, L., & Rettie, R. (2003). An evaluation of e-mail marketing and factors affecting response. Journal of Targeting, Measurement and Analysis for Marketing, 11(3), 203-217.
  • • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
  • • Heimbach, I., Kostyra, D. S., & Hinz, O. (2015). Marketing automation. Business & Information Systems Engineering, 57(2), 129-133.
  • • Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(1), 96-113.
  • • Jain, A., & Pandey, A. K. (2017). Multiple quality optimizations in electrical discharge drilling of mild steel sheet. Materials Today: Proceedings, 4(8), 7252-7261.
  • • Jain, A., & Pandey, A. K. (2019). Modeling and optimizing of different quality characteristics in electrical discharge drilling of titanium alloy (Grade-5) sheet. Materials Today: Proceedings, 18, 182-191.
  • • Jain, A., Yadav, A. K., & Shrivastava, Y. (2020). Modelling and optimization of different quality characteristics in electric discharge drilling of titanium alloy sheet. Materials Today: Proceedings, 21, 1680-1684.
  • • Järvinen, J., & Karjaluoto, H. (2015). The use of Web analytics for digital marketing performance measurement. Industrial Marketing Management, 50, 117-127.
  • • Kapoor, K. K., Dwivedi, Y. K., & Piercy, N. C. (2016). Pay-per-click advertising: A literature review. The Marketing Review, 16(2), 183-202.
  • • Li, S., Li, J. Z., He, H., Ward, P., & Davies, B. J. (2011). WebDigital: A Web-based hybrid intelligent knowledge automation system for developing digital marketing strategies. Expert Systems with Applications, 38(8), 10606-10613.
  • • M. Stone, ‘‘The new (and ever-evolving) direct and digital marketing ecosystem”, Journal of Direct, Data and Digital Marketing Practice, vol. 16, no. 2, pp. 71-74, 2014
  • • Melewar, T. C., & Smith, N. (2003). The Internet revolution: some global marketing implications. Marketing intelligence & planning.
  • • Mouha, R. A. (2021). Internet of Things (IoT). Journal of Data Analysis and Information Processing, 9(2), 77-101.
  • • Murgai, A. (2018). Transforming digital marketing with artificial intelligence. International Journal of Latest Technology in Engineering, Management & Applied Science, 7(4), 259-262.
  • • Panwar, V., Sharma, D. K., Kumar, K. P., Jain, A., & Thakar, C. (2021). Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm. Materials Today: Proceedings, 46, 6474-6481.
  • • Plassmann, H., Venkatraman, V., Huettel, S., & Yoon, C. (2015). Consumer neuroscience: applications, challenges, and possible solutions. Journal of marketing research, 52(4), 427-435.
  • • Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15-26.
  • • Suta, P., Lan, X., Wu, B., Mongkolnam, P., & Chan, J. H. (2020). An overview of machine learning in chatbots. Int J Mech Engineer Robotics Res, 9(4), 502-510.
  • • Wieckowski, A., Ma, J., Schwarz, H., Marpe, D., & Wiegand, T. (2019, September). Fast partitioning decision strategies for the upcoming versatile video coding (VVC) standard. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 4130-4134). IEEE.
  • • Wymbs, C. (2011). Digital marketing: The time for a new “academic major” has arrived. Journal of Marketing Education, 33(1), 93-106.

Using Artificial İntelligence and Machine Learning in Digital Marketing

Year 2023, Volume: 22 Issue: 85, 135 - 142, 27.01.2023
https://doi.org/10.17755/esosder.1152771

Abstract

Technology and human potential provide two of the biggest problems facing modern societies. The fears that machines will eventually replace people in the workforce have been swirling for a while among analysts, journalists, and great thinkers. Artificial intelligence-powered chatbots enhance the user experience, making them one of the most effective tools available today for digital marketers. Understanding and forecasting consumer behavior is now possible thanks to the use of machine learning in marketing. A customer-based marketing strategy is the outcome of analyzing and applying important aspects like behavior, revenue, price difference percentage, market share, liquidity, long-term customer value, and customer retention. This article's goal is to investigate the potential uses for artificial intelligence in marketing. This study used a descriptive research methodology to investigate the function of artificial intelligence in digital marketing, focusing on the hypothesis that both physical and cognitive activities can be substituted by artificial intelligence and machine learning. According to a survey of marketing and AI experts from a range of industries, AI has already had an impact on marketing operations and will continue to do so in the future. Companies gain a competitive advantage in terms of technological advancement by integrating new data-driven methodologies with digital marketing strategies. Machine learning can forecast future occurrences and support decision-making by identifying lessons learnt from the massive amounts of data created. Enterprises' strategic decision-making processes are significantly impacted and made simpler by this feature. Little is known about marketers' attitudes toward and comprehension of ML technologies, as well as their acquisition and use to support operational and strategic management, according to the study's needs assessment. Intelligent robotic technologies are thought to be perfect for online marketing for businesses. These technologies enable the determination of consumer preferences and the customization of offerings. It gets easier to create content using the obtained data. As a result, a substantial amount of data is gathered and converted into business choices.

References

  • • Boddu, R. S. K., Santoki, A. A., Khurana, S., Koli, P. V., Rai, R., & Agrawal, A. (2022). An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing. Materials Today: Proceedings, 56, 2288-2292.
  • • Cheng, F. C., & Wang, Y. S. (2018). The do not track mechanism for digital footprint privacy protection in marketing applications. Journal of Business Economics and Management, 19(2), 253-267.
  • • Chittenden, L., & Rettie, R. (2003). An evaluation of e-mail marketing and factors affecting response. Journal of Targeting, Measurement and Analysis for Marketing, 11(3), 203-217.
  • • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
  • • Heimbach, I., Kostyra, D. S., & Hinz, O. (2015). Marketing automation. Business & Information Systems Engineering, 57(2), 129-133.
  • • Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(1), 96-113.
  • • Jain, A., & Pandey, A. K. (2017). Multiple quality optimizations in electrical discharge drilling of mild steel sheet. Materials Today: Proceedings, 4(8), 7252-7261.
  • • Jain, A., & Pandey, A. K. (2019). Modeling and optimizing of different quality characteristics in electrical discharge drilling of titanium alloy (Grade-5) sheet. Materials Today: Proceedings, 18, 182-191.
  • • Jain, A., Yadav, A. K., & Shrivastava, Y. (2020). Modelling and optimization of different quality characteristics in electric discharge drilling of titanium alloy sheet. Materials Today: Proceedings, 21, 1680-1684.
  • • Järvinen, J., & Karjaluoto, H. (2015). The use of Web analytics for digital marketing performance measurement. Industrial Marketing Management, 50, 117-127.
  • • Kapoor, K. K., Dwivedi, Y. K., & Piercy, N. C. (2016). Pay-per-click advertising: A literature review. The Marketing Review, 16(2), 183-202.
  • • Li, S., Li, J. Z., He, H., Ward, P., & Davies, B. J. (2011). WebDigital: A Web-based hybrid intelligent knowledge automation system for developing digital marketing strategies. Expert Systems with Applications, 38(8), 10606-10613.
  • • M. Stone, ‘‘The new (and ever-evolving) direct and digital marketing ecosystem”, Journal of Direct, Data and Digital Marketing Practice, vol. 16, no. 2, pp. 71-74, 2014
  • • Melewar, T. C., & Smith, N. (2003). The Internet revolution: some global marketing implications. Marketing intelligence & planning.
  • • Mouha, R. A. (2021). Internet of Things (IoT). Journal of Data Analysis and Information Processing, 9(2), 77-101.
  • • Murgai, A. (2018). Transforming digital marketing with artificial intelligence. International Journal of Latest Technology in Engineering, Management & Applied Science, 7(4), 259-262.
  • • Panwar, V., Sharma, D. K., Kumar, K. P., Jain, A., & Thakar, C. (2021). Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm. Materials Today: Proceedings, 46, 6474-6481.
  • • Plassmann, H., Venkatraman, V., Huettel, S., & Yoon, C. (2015). Consumer neuroscience: applications, challenges, and possible solutions. Journal of marketing research, 52(4), 427-435.
  • • Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15-26.
  • • Suta, P., Lan, X., Wu, B., Mongkolnam, P., & Chan, J. H. (2020). An overview of machine learning in chatbots. Int J Mech Engineer Robotics Res, 9(4), 502-510.
  • • Wieckowski, A., Ma, J., Schwarz, H., Marpe, D., & Wiegand, T. (2019, September). Fast partitioning decision strategies for the upcoming versatile video coding (VVC) standard. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 4130-4134). IEEE.
  • • Wymbs, C. (2011). Digital marketing: The time for a new “academic major” has arrived. Journal of Marketing Education, 33(1), 93-106.
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section Articles
Authors

Suat Okay 0000-0002-6042-6682

Early Pub Date January 27, 2023
Publication Date January 27, 2023
Submission Date August 2, 2022
Published in Issue Year 2023 Volume: 22 Issue: 85

Cite

APA Okay, S. (2023). DİJİTAL PAZARLAMADA YAPAY ZEKA VE MAKİNE ÖĞRENİMİ KULLANIMI. Elektronik Sosyal Bilimler Dergisi, 22(85), 135-142. https://doi.org/10.17755/esosder.1152771

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Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.

ESBD Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Türk Patent ve Marka Kurumu tarafından tescil edilmiştir. Marka No:2011/119849.