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

Öz

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.

Anahtar Kelimeler

• Ürün Kişiselleştirme
• Satın Alma Niyeti
• Müşteri Memnuniyeti
• Marka Sadakati
• Yapay Zekâ

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

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.

Keywords

• Product Personalization
• Purchase Intention
• Customer Satisfaction
• Brand Loyalty
• Artificial Intelligence

Kaynakça

1. Aaker, D.A., Kumar,V. & Day, G.S., (2007). Marketing Research, 9. Edition, John Wiley & Sons, Danvers
2. Accenture. (2023). Data and Analytics Services. https://www.accenture.com/us-en/insights/song/customer-experience-index
3. Adomavicius, G., Bockstedt, J. C., Curley, S. P. & Zhang, J. (2018). Effects of Online Recommendations on Consumers’ Willingness to Pay. Information Systems Research, 29(1), 84-102.
4. Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., ... & Zhang, Z. J. (2008). Putting One-to-One Marketing to Work: Personalization, Customization, and Choice. Marketing Letters, 19, 305-321.
5. Arslan, K. (2020). Eğitimde Yapay Zekâ ve Uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71-88.
6. Bagozzi, R. P. & Yi, Y. (2012). Specification, Evaluation, and Interpretation of Structural Equation Models. Journal of the Academy of Marketing Science, 40, 8-34.
7. Barber, N., Kuo, P. J., Bishop, M. & Goodman Jr, R. (2012). Measuring Psychographics to Assess Purchase Intention and Willingness to Pay. Journal of Consumer Marketing, 29(4), 280-292.
8. Bardakci, A. & Whitelock, J. (2003). Mass‐Customisation in Marketing: The Consumer Perspective. Journal of Consumer Marketing, 20(5), 463-479.

9. Bauer, H. H., Falk, T. & Hammerschmidt, M. (2006). eTransQual: A Transaction Process-Based Approach for Capturing Service Quality in Online Shopping. Journal of Business Research, 59(7), 866-875.
10. Bughin, J., Seong, J. Manyika, J., Chui, M. & Joshi, R. (2018). Modeling the Global Economic Impact of AI. McKinsey. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-theai-frontier-modeling-the-impact-of-ai-on-the-world-economy
11. Busser, J. A. & Shulga, L. V. (2019). Involvement in Consumer-Generated Advertising: Effects of Organizational Transparency and Brand Authenticity on Loyalty and Trust. International Journal of Contemporary Hospitality Management, 31(4), 1763-1784
12. Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J. & Mavrommatis, A. (2020). From Data to Action: How Marketers Can Leverage AI. Business Horizons, 63(2), 227-243.
13. Chang, P. L. & Chieng, M. H. (2006). Building Consumer-Brand Relationship: A Cross‐Cultural Experiential View. Psychology & Marketing, 23(11), 927-959.
14. Chaudhuri, A. & Holbrook, M. B. (2001). The Chain of Effects from Brand Trust and Brand Affect to Brand Performance: The Role of Brand Loyalty. Journal of Marketing, 65(2), 81-93.
15. Chellappa, R. K. & Sin, R. G. (2005). Personalization Versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma. Information Technology and Management, 6, 181-202.
16. Churchill, E. F. (2013). Putting the Person Back into Personalization. Interactions, 20(5), 12-15.
17. 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.
18. 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.
19. Dawson, C. (2018). Amazon Custom Enables Selling with Personalisation in the UK. https://tamebay.com/2018/03/amazon-custom-enables-selling-with-personalisation-in-the-uk.html
20. de Pechpeyrou, P. (2009). How Consumers Value Online Personalization: A Longitudinal Experiment. Direct Marketing: An International Journal, 3(1), 35-51.
21. Deloitte. (2015). Global Power of Retailing 2015: Embracing Innovation. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Consumer-Business/gx-cb-global-powers-of-retailing.pdf
22. Deloitte. (2015). The Deloitte Consumer Review Made-To-Order: The Rise of Mass Personalisation. https://www2.deloitte.com/content/dam/Deloitte/ch/Documents/consumer-business/ch-en-consumer-business-made-to-order-consumer-review.pdf
23. Dick, A. S. & Basu, K. (1994). Customer Loyalty: Toward An Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22, 99-113.
24. Duran, C. (2021). Yapay Zekâ Temelli Pazarlama. Geleneksel Pazarlamanın Sonu mu?. N. Ö. İyigün & M. K. Yılmaz (Ed.), Yapay Zekâ Güncel Yaklaşımlar ve Uygulamalar (31-50). İstanbul: Beta Kitap.
25. Durmuş, B., Yurtkoru, E. S. & Çinko, M. (2022). Sosyal Bilimlerde SPSS’le Very Analizi [Data Analysis with SPSS in Social Science]. The Kitap Yayınları.
26. Evergage and Researchscape International. (2019). Marketing Cloud Personalization. https://www.salesforce.com/products/marketing-personalization/
27. 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/
28. 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
29. Fornell, C. & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics.
30. 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.
31. 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.
32. 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/
33. 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.
34. Ho, S. Y. & Bodoff, D. (2014). The Effects of Web Personalization on User Attitude and Behavior. MIS Quarterly, 38(2), 497-A10.
35. 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
36. ICT Media. (2021). Yapay Zekâ E-Ticaretin DNA’sını Değiştiriyor. https://ictmedia.com.tr/haber/yapay-zeka-e-ticaretin-dnasini-degistiriyor
37. Klaus, P. P. & Maklan, S. (2013). Towards a Better Measure of Customer Experience. International Journal of Market Research, 55(2), 227-246.
38. Kramer, J., Noronha, S. & Vergo, J. (2000). A User-Centered Design Approach to Personalization. Communications of the ACM, 43(8), 44-48.
39. Lee, E. J. & Park, J. K. (2009). Online Service Personalization for Apparel Shopping. Journal of Retailing and Consumer Services, 16(2), 83-91.
40. 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
41. Malhotra, N. K. (2004). Marketing Research an Applied Orientation, 4. Edition, Pearson
42. Marr, B. & Ward, M. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. West Sussex: Wiley.
43. McCarthy, J. (1959). Programs with Common Sense.
44. 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
45. Montgomery, A. L. & Smith, M. D. (2009). Prospects for Personalization on the Internet. Journal of Interactive Marketing, 23(2), 130-137.
46. Mugge, R., Schoormans, J. P. & Lange, A. D. (2007). Consumers' Appreciation of Product Personalization. ACR North American Advances.
47. Nadimpalli, M. (2017). Artificial Intelligence-Consumers and Industry Impact. International Journal of Economics & Management Sciences, 6(03), 4-6.
48. Pallant, J., Sands, S. & Karpen, I. (2020). Product Customization: A Profile of Consumer Demand. Journal of Retailing and Consumer Services, 54, 102030.
49. 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.
50. Peppers, D. & Rogers, M. (1993). The One-to-One Future. Doubleday, New York, NY.
51. Piller, F. T. & Müller, M. (2004). A New Marketing Approach to Mass Customisation. International Journal of Computer Integrated Manufacturing, 17(7), 583-593.
52. Pine, B.J. (1992), Mass Customization: The New Frontier in Business Competition, Harvard Business School Press, USA.
53. Pradeep, A. K., Appel, A. & Sthanunathan, S. (2020). Pazarlama ve ürün inovasyonunda yapay zekâ. Trendleri Öğrenmeye Müşterilerle Bağ Kurmaya ve Satış Yapmaya Yönelik Yeni ve Güçlü Araçlar. T. Gezer (Çev.). İstanbul: EAE Yayınları. Prentice Hall, New Jersey.
54. Rosenfeld, A. & Kraus, S. (2018). Predicting Human Decision-Making. Predicting Human Decision-Making: From Prediction to Action (21-59). Cham: Springer International Publishing.
55. Senecal, S. & Nantel, J. (2004). The Influence of Online Product Recommendations on Consumers’ Online Choices. Journal of Retailing, 80(2), 159-169.
56. Servoz, M. (2019). AI-the Future of Work? Work of the Future! on How Artificial Intelligence, Robotics and Automation Are Transforming Jobs and the Economy in Europe. https://op.europa.eu/en/publication-detail/-/publication/096526d7-17d8-11ea-8c1f-01aa75ed71a1
57. Shabbir, J. & Answer T. (2015). Artificial Intelligence and its Role in Near Future. Journal of Latex Class Files, 14(8), 1-11.
58. Shankar, V. (2018). How Artificial Intelligence (AI) Is Reshaping Retailing. Journal of Retailing, 94(4), 5-11.
59. Sterne, J. (2017). Artificial Intelligence for Marketing: Practical Applications. John Wiley & Sons.
60. Tabachnick, B. G. & Fidell, L. S. (2007). Experimental Designs Using ANOVA (Vol. 724). Belmont, CA: Thomson/Brooks/Cole.
61. Tabachnick, B. G. & Fidell, L. S. (2013). Using Multivariate Statistics. Boston, Pearson
62. Tam, K. Y. & Ho, S. Y. (2005). Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective. Information Systems Research, 16(3), 271-291.
63. Tegmark, M. (2019). Yaşam 3.0 Yapay Zekâ Çağında İnsan Olmak. İstanbul: Pegasus Yayınları.
64. Thirumalai, S. & SINHA, K.K. (2009), “Customization Strategies in Electronic Retailing: Implications of Customer Purchase Behavior”, Decision Sciences, 40 (1), 5-36.
65. Thornhill, J. (2018). Britain Urged to Take Ethical Advantage in Artificial Intelligence. Financial Times. https://www.ft.com/content/b21d1fb8-3f3e-11e8-b9f9-de94fa33a81e
66. Vesanen, J. & Raulas, M. (2006). Building Bridges for Personalization: A Process Model for Marketing. Journal of Interactive Marketing, 20(1), 5-20.
67. Vesanen, J. (2007). “What is personalization? A conceptual framework”, European Journal of Marketing, Cilt: 41, No: 5/6, 2007, s. 409-418
68. Vesanen, J. (2007). What is Personalization? A Conceptual Framework. European Journal of Marketing, 41(5/6), 409-418.
69. Walker, D. (2017). Augmented Retail. https://www.lsnglobal.com/micro-trends/article/21938/augmented-retail-1.
70. Wind, J. & Rangaswamy, A. (2001). Customerization: The Next Revolution in Mass Customization. Journal of Interactive Marketing, 15(1), 13-32.
71. Xiao, B. & Benbasat, I. (2007). E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly, 137-209.
72. Yoo, S. J. & Chang, Y. J. (2005). An Exploratory Research on the Store Image Attributes Affecting Its Store Loyalty. Seoul Journal of Business, 11(1), 19-41.
73. Zeng, J. (2019). Survey Respondents Think More Can Be Done to Form Better Customer Relationships. American Marketing Association. https://www.ama.org/marketing-news/marketers-coming-up-short-on-personalization/
74. Zikmund, W. G. (1997). Business Research Methods, 5. Edition, The Dryden Press, Orlando.