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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63191
Volume:
10 |
5 |
May 2026
Pages:
1-14
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
HPKV Business School
Central University of Himachal Pradesh
HPKV Business School
Central University of Himachal Pradesh
HPKV Business School
Central University of Himachal Pradesh
HPKV Business School
Central University of Himachal Pradesh
The rapid growth of technology combined with the widespread use of the Internet has resulted in an explosive increase in data creation by businesses and the tools available to them. This has created an opportunity to use artificial intelligence (AI) to provide customers with unique experiences on various platforms. In today’s digital marketplace, personalisation via AI is revolutionising how organisations use AI to better understand customers on an individual level, anticipate their actions, and dynamically create content to suit them in real time (Huang & Rust, 2018; Wedel & Kannan, 2016). Companies such as Netflix provide examples of how this occurs by using complex algorithms developed through machine learning and analysing large amounts of data on consumer behaviour to develop personalised recommendations for their customers. This has had a major impact on consumer engagement and purchase decisions. Several industry studies have confirmed the importance of personalisation, including what IBM (2023) reports: a significant number of consumers prefer interactions enabled by AI because they enhance their overall digital experience, showing growing confidence in automated personalisation systems. In another instance by McKinsey & Company (2023), approximately 71% of consumers expect organisations to provide a personalised experience, and a significant portion of them become dissatisfied with organisations that fail to provide this level of service. These studies illustrate the development of a new paradigm in consumer expectations, whereby providing a personalised experience has transitioned from a competitive advantage to an essential ingredient of digital ecosystems. Historically, personalisation in digital settings has been limited primarily through simple strategies, such as sending targeted emails or creating rule- based recommendation systems. The research done by MacKenzie et al. (2011) mention that in the past, businesses have typically personalised their customers by focusing on their identity and previous purchases. With advancements in AI technology and Data Analytics, businesses can now personalise through real-time methods that are constantly evolving based on user behaviour, contextual data signals, and predictive models (Adomavicius & Tuzhilin, 2005). This transition has enabled a shift from mass marketing to hyper-personalisation as a digital experience, thereby offering increased relevance for customers through a more efficient use of their time. Consumers have access to an immense volume of data within the digital economy today, requiring businesses to provide customers with personalised recommendations to simplify the decision-making process. AI-enabled technologies allow customers to alleviate cognitive overload by filtering out unwanted or irrelevant content and presenting only what is most appropriate aligned to each user's preference (Tam & Ho, 2006). Netflix is an excellent example of an organisation that utilises effective AI personalisation. Netflix has developed its own proprietary recommendation engine that uses each viewer's viewing history, including preferences and behavioural patterns, to provide relevant, customised content recommendations. This helps retain the viewer and encourages them to continue using the platform through extended periods of binge watching and exploring new content (Gomez-Uribe & Hunt, 2015). Multiple studies have provided empirical research demonstrating that AI personalised marketing affects customer behaviour. According to Lemon and Verhoef (2016), providing personalised experiences for customers leads to higher overall customer satisfaction, increased engagement with the company, and stronger customer loyalty to the brand. Ultimately, these effects lead customers to continue using a brand’s services and being retained as subscribers. While AI personalisation has many advantages, it also raises concerns regarding data privacy and transparency in the way algorithms operate. The continual growth of AI personalisation can result in a personalisation-privacy paradox. Owing to this phenomenon, consumers want to receive relevant or customised experiences through AI personalisation, but they also have concerns about how their personal data are collected and how companies use this data to create these types of customised experiences.
KEYWORDS
Research has indicated that personalisation using artificial intelligence (AI) leads to many benefits for consumers in e-commerce activities by enhancing engagement and loyalty through personalised experiences (Iqbal et al., 2025). Several websites (including Netflix) provide personalisation through AI-based predictive algorithms which analyse individual user preferences to provide greater personalisation and help deliver the best experience possible for the consumer by providing them with recommendations based on their previous browsing history (Raji et al., 2024). AI systems have an adaptive structure which allows them to continually revise their recommendations based on past user interaction data, causing increased viewing times and increased satisfaction from total viewing time (Babatunde, et al., 2024). As such, AI-powered product/collaborative consumption recommendations will create a more satisfactory experience for consumers (because of a higher level of match between what a consumer wants and a recommended product), and therefore will enhance the likelihood that a purchase will occur and will increase the chances of a consumer being loyal to the company by increasing the perceived benefit associated with the customised recommendations provided through the use of AI systems to create an aesthetically pleasing and formalised profile of the consumer (BAŞER and Akıncı, 2020).Considering that investing in high-quality AI personalisation engines will provide organisations with increased customer retention through a better understanding of the relationship between AI personalisation and customer engagement (i.e. higher levels of retention will result from using AI personalisation; see Leghemo et al. 2024; Mishra et al. 2026), based on this success, major e-commerce sites (for example, Airbnb, Spotify) have seen significant increases in conversion rates, resulting in; additionally, the use of AI has allowed both to enhance their respective search algorithms and provide users with personalised content that meets their specific requirements (i.e. search parameters). The data reveal that AI plays a major role in changing consumer interactions in digital environments. In addition, organisations are leveraging AI to enhance customer experience through brand loyalty across multiple digital ecosystems (Ahmed et al., 2025; Kamkankaew et al., 2024; Sipos, 2025). For organisations to effectively leverage AI for personalisation, it is critical that they develop a sufficient data infrastructure and advanced tools for analysing the data complexities associated with integrating multiple data sources through sophisticated statistical methodologies to eliminate any potential biases associated with the algorithms used to develop the AI applications and cultivate an ethical corporate culture that establishes trust with the customer base (Babatunde et al., 2024; Iqbal et al., 2025).
Abishek, M. N., & Judi, E. K. (2025). A Study on Exploring Consumer Engagement with Ai-Driven Experiences on Netflix Streaming Platform. In International Conference on Artificial Intelligence in Commerce and Management (pp. 93-100). Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99 Agoro, H., Templer, S., & Maddog, M. (2021). AI-Powered Personalization: Enhancing User Experience through Customized Shopping Recommendations. Ahmed, S. M. M., Owais, M., Raza, M., Nadeem, Q., & Ahmed, B. (2025). The impact of AI-driven personalization on consumer engagement and brand loyalty. Qlantic Journal of Social Sciences, 6(1), 311-323. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction, judgment, and complexity: a theory of decision-making and artificial intelligence. In The economics of artificial intelligence: An agenda (pp. 89-110). University of Chicago Press. Ali, A., Raza, A., Sayed, M. M. M., Qureshi, B. A., & Memon, Y. M. (2025). Data-driven insights: Machine learning approaches for Netflix content analysis and visualization. Journal of Engineering Research and Reports, 27(4), 278–290. https://doi.org/10.9734/jerr/2025/v27i41471 Amat, F., Chandrashekar, A., Jebara, T., & Basilico, J. (2018, September). Artwork personalization at Netflix. In Proceedings of the 12th ACM conference on recommender systems (pp. 487-488).https://doi.org/10.1145/3240323.3241729 Amatriain, X. (2013, August). Big & personal: data and models behind netflix recommendations. In Proceedings of the 2nd international workshop on big data, streams and heterogeneous source Mining: Algorithms, systems, programming models and applications (pp. 1-6). Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A netflix case study. In Recommender systems handbook (pp. 385-419). Boston, MA: Springer US. Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and willingness to be profiled online. Management Science, 52(1), 13–28. https://doi.org/10.1287/mnsc.1060.0568
Publication Date
5/15/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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