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The Effect of Product Personalization on Consumer Purchasing Intention, Customer Satisfaction, Brand Loyalty and Artificial Intelligence Applications with Machine Learning

Year 2024, , 1240 - 1263, 27.09.2024
https://doi.org/10.25295/fsecon.1449755

Abstract

Personalization is the ability of a company to evaluate its customer base as individual entities and achieve customized interactions and transactions based on this evaluation. Advances in information and communication technologies offer new opportunities in collecting and analyzing customer data and carrying out personalized marketing activities. In this context, the use of strategies such as special messages, targeted advertising campaigns, and special offers based on individual customer profiles allows companies to manage customer relations in a more effective and customized way. These developments play an important role in increasing customer satisfaction and loyalty of businesses, optimizing marketing strategies and gaining competitive advantage. The most important of these applications are machine learning and artificial intelligence. Therefore, within the scope of the study, it is aimed to measure the effect of product personalization on consumer purchasing intention, customer satisfaction and the resulting brand loyalty and to present an approach for processing the obtained information with machine learning and artificial intelligence applications. Within the scope of the research, data obtained from questions asked to consumers through digital survey forms with product personalization, purchase intention, customer satisfaction and brand loyalty scales are used. Data were analyzed using IBM SPSS 25.0 and IBM AMOS 26.0 software packages. As a result of the research, it was seen that product personalization has a positive and significant effect on consumer purchase intention and customer satisfaction, and purchase intention and customer satisfaction also have a positive and significant effect on brand loyalty. According to the findings, it was aimed to propose an artificial intelligence-based model that could be useful on consumer behavior and decision-making processes.

Ethical Statement

Çalışma kapsamında gerekli etik kurul onayı alınmış ve sisteme yüklenmiştir.

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Ürün Kişiselleştirmenin Tüketici Satın Alma Niyeti, Müşteri Tatmini, Marka Sadakati Üzerine Etkisi ve Makine Öğrenmesi ile Yapay Zekâ Uygulamaları

Year 2024, , 1240 - 1263, 27.09.2024
https://doi.org/10.25295/fsecon.1449755

Abstract

Kişiselleştirme, bir şirketin müşteri tabanını bireysel varlıklar olarak değerlendirebilme ve bu değerlendirmeye dayalı olarak özelleştirilmiş etkileşim ve işlemleri başarabilme yeteneğidir. Bilgi ve iletişim teknolojilerindeki ilerlemeler, müşteri verilerinin toplanması, analiz edilmesi ve kişiselleştirilmiş pazarlama faaliyetlerinin icra edilmesi konularında yeni olanaklar sunmaktadır. Bu bağlamda, bireysel müşteri profillerine dayalı olarak özel mesajlar, hedefe yönelik reklam kampanyaları, özel teklifler gibi stratejilerin kullanımı, şirketlerin müşteri ilişkilerini daha etkili ve özelleştirilmiş bir biçimde yönetmelerine olanak sağlamaktadır. Bu gelişmeler, işletmelerin müşteri memnuniyetini ve sadakatini artırmak, pazarlama stratejilerini optimize etmek ve rekabet avantajı elde etmek açısından önemli bir rol oynamaktadır. Bu uygulamaların başında makine öğrenmesi ve yapay zekâ gelmektedir. Bu nedenle çalışma kapsamında ürün kişiselleştirmenin tüketici satın alma niyeti, müşteri tatmini ve bunun sonucunda ortaya çıkan marka sadakati üzerindeki etkisinin ölçümlenmesi ve elde edilen bilgilerin makine öğrenmesi ve yapay zekâ uygulamaları ile işlenmesine yönelik bir yaklaşım sunulması amaçlanmaktadır. Araştırma kapsamında tüketicilere dijital anket formlarıyla ürün kişiselleştirme, satın alma niyeti, müşteri tatmini ve marka sadakati ölçekleriyle sorulan sorulardan elde edilen veriler kullanılmaktadır. Veriler IBM SPSS 25.0 ve IBM AMOS 26.0 paket programları kullanılarak analizler yapılmıştır. Araştırma sonucunda ürün kişiselleştirmenin tüketici satın alma niyeti ve müşteri tatmini üzerinde pozitif ve anlamlı bir etkiye sahip olduğu ve satın alma niyeti ve müşteri tatmininin de marka sadakati üzerinde pozitif ve anlamlı bir etkiye sahip olduğu görülmüştür. Elde edilen bulgulara göre tüketici davranış ve karar verme süreçleri üzerinde faydalı olabilecek yapay zekâ temelli bir model önerisinde bulunulması amaçlanmıştır.

References

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  • Churchill, E. F. (2013). Putting the Person Back into Personalization. Interactions, 20(5), 12-15.
  • Da Silveira, G., Borenstein, D. & Fogliatto, F. S. (2001). Mass Customization: Literature Review and Research Directions. International Journal of Production Economics, 72(1), 1-13.
  • Davenport, T. H. & Ronanki, R. (2019). Gerçek Dünya İçin Yapay Zekâ. N. Özata (Çev.), Yapay Zekâ. Harvard Business Review’den En Etkili Yönetim Fikirleri (7-29). İstanbul: Optimist Yayınları.
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  • Durmuş, B., Yurtkoru, E. S. & Çinko, M. (2022). Sosyal Bilimlerde SPSS’le Very Analizi [Data Analysis with SPSS in Social Science]. The Kitap Yayınları.
  • Evergage and Researchscape International. (2019). Marketing Cloud Personalization. https://www.salesforce.com/products/marketing-personalization/
  • Exposebox. (2022). The Power of Product Recommendation and Why Your Online Business Should Implement It in 2022. https://exposebox.com/the-power-of-product-recommendation-2022/
  • Forbes. (2023). Coca-Cola’s Latest Generative Ai Initiative Is All About Festive Customer Engagement. https://www.forbes.com/sites/bernardmarr/2023/12/14/coca-colas-latest-generative-ai-initiative-is-all-about-festive-customer-engagement/?sh=388bcd0b3708
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  • Franke, N. & Piller, F. (2004). Value Creation by Toolkits for User Innovation and Design: The Case of the Watch Market. Journal of Product Innovation Management, 21(6), 401-415.
  • Franke, N., Keinz, P. & Steger, C. J. (2009). Testing the Value of Customization: When Do Customers Really Prefer Products Tailored to Their Preferences?. Journal of Marketing, 73(5), 103-121.
  • Harvard Business Review. (2020). Starbucks: Winning on Rewards, Loyalty, and Data. https://d3.harvard.edu/platform-digit/submission/starbucks-winning-on-rewards-loyalty-and-data/
  • Hennig-Thurau, T., Marchand, A. & Marx, P. (2012). Can Automated Group Recommender Systems Help Consumers Make Better Choices?. Journal of Marketing, 76(5), 89-109.
  • Ho, S. Y. & Bodoff, D. (2014). The Effects of Web Personalization on User Attitude and Behavior. MIS Quarterly, 38(2), 497-A10.
  • IBM & NRF. (2019). The Coming AI Revolution in Retail and Consumer Products: Intelligent automation is transforming both industries in unexpected ways. https://cdn.nrf.com/sites/default/files/2019-01/The%20coming%20AI%20revolution.pdf
  • ICT Media. (2021). Yapay Zekâ E-Ticaretin DNA’sını Değiştiriyor. https://ictmedia.com.tr/haber/yapay-zeka-e-ticaretin-dnasini-degistiriyor
  • Klaus, P. P. & Maklan, S. (2013). Towards a Better Measure of Customer Experience. International Journal of Market Research, 55(2), 227-246.
  • Kramer, J., Noronha, S. & Vergo, J. (2000). A User-Centered Design Approach to Personalization. Communications of the ACM, 43(8), 44-48.
  • Lee, E. J. & Park, J. K. (2009). Online Service Personalization for Apparel Shopping. Journal of Retailing and Consumer Services, 16(2), 83-91.
  • Lindecrantz, E., Gi, M. T. P. & Zerbi, S. (2020). Personalizing the Customer Experience: Driving Differentiation in Retail. https://www.mckinsey.com/industries/retail/our-insights/personalizing-the-customer-experience-driving-differentiation-in-retail
  • Malhotra, N. K. (2004). Marketing Research an Applied Orientation, 4. Edition, Pearson
  • Marr, B. & Ward, M. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. West Sussex: Wiley.
  • McCarthy, J. (1959). Programs with Common Sense.
  • McKinsey Company (2019). How Retailers Can Keep Up with Consumers. https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
  • Montgomery, A. L. & Smith, M. D. (2009). Prospects for Personalization on the Internet. Journal of Interactive Marketing, 23(2), 130-137.
  • Mugge, R., Schoormans, J. P. & Lange, A. D. (2007). Consumers' Appreciation of Product Personalization. ACR North American Advances.
  • Nadimpalli, M. (2017). Artificial Intelligence-Consumers and Industry Impact. International Journal of Economics & Management Sciences, 6(03), 4-6.
  • Pallant, J., Sands, S. & Karpen, I. (2020). Product Customization: A Profile of Consumer Demand. Journal of Retailing and Consumer Services, 54, 102030.
  • Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N. & Chrissikopoulos, V. (2014). Shiny Happy People Buying: The Role of Emotions on Personalized E-Shopping. Electronic Markets, 24, 193-206.
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There are 74 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Abdullah Ballı 0000-0003-2689-6610

Early Pub Date September 20, 2024
Publication Date September 27, 2024
Submission Date March 15, 2024
Acceptance Date July 4, 2024
Published in Issue Year 2024

Cite

APA Ballı, A. (2024). The Effect of Product Personalization on Consumer Purchasing Intention, Customer Satisfaction, Brand Loyalty and Artificial Intelligence Applications with Machine Learning. Fiscaoeconomia, 8(3), 1240-1263. https://doi.org/10.25295/fsecon.1449755

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