Future use of artificial intelligence for SKU promotion in retail : Applications, perceptions, and strategic challenges
*Justina JosephCorresponding authorjustina3007@gmail.comDepartment of Management StudiesPatel Group of InstitutionsMehsana, Gujarat, 384120, India0009-0001-1407-628XView full profile → , Chiragkumar B. Rathodcbrathod.2018@gmail.comDepartment of Business ManagementFaculty of Management StudiesSmt. S. B. Patel Institute of Business Management, Sankalchand Patel UniversityVisnagar, Gujarat, 384315, India0009-0007-3564-5887View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jun 2026
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2436
- Pages:
- 3393–3403
Abstract
The implementation of Artificial Intelligence (AI) in retail promotional strategies marks a paradigm shift from mass marketing to granular, stock-keeping unit (SKU) level customization. AI-driven SKU promotion can stimulate consumer interest through contextual, geo-fenced and inventory-friendly offers, while real-time data streams allow retailers to surface high-conversion SKUs, optimize stock rotation and minimize holding costs. AI recommendations also reduce search fatigue and encourage exploration of new product categories. However, privacy concerns, algorithmic bias and ethical risks, particularly when behavioral patterns are inferred without the direct consent of the user, constrain large-scale adoption. This paper examines how AI systems use customer behavior data, transaction history, demographic segmentation and contextual triggers to promote SKUs to particular groups of consumers. A quantitative, cross-sectional design was adopted, surveying 150 retail consumers exposed to AI-based solutions such as virtual try-ons, smart shelves and recommendation engines. Findings indicate that perceived AI accuracy, transparency and reduced cognitive load have a significant effect on trust and purchase intention, while omnichannel integration enhances satisfaction. The privacy problem remains but is mitigated by perceived usefulness and transparency. The paper underscores the need to align AI potential with data governance and ethical execution in order to gain sustainable competitive advantage in the retail business.
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References
[1] McKinsey & Company, “The economic potential of generative AI: The next productivity frontier,” McKinsey & Company (2023).
[2] PwC, “AI in retail: From personalization to profitability,” PwC (2023).
[3] T. H. Davenport, A. Guha, D. Grewal, and T. Bressgott, “How Artificial Intelligence will change the future of marketing,” J. Acad. Mark. Sci., vol. 48, no. 1, pp. 24–42 (2020).
[4] M.-H. Huang and R. T. Rust, “A strategic framework for Artificial Intelligence in marketing,” J. Acad. Mark. Sci., vol. 49, no. 1, pp. 30–50 (2021).
[5] K. D. Martin and P. E. Murphy, “The role of data privacy in marketing,” J. Acad. Mark. Sci., vol. 45, no. 2, pp. 135–155 (2017).
[6] S. Puntoni, R. W. Reczek, M. Giesler, and S. Botti, “Consumers and Artificial Intelligence: An experiential perspective,” J. Mark., vol. 85, no. 1, pp. 131–151 (2021).
[7] N. F. Awad and M. S. Krishnan, “The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization,” MIS Q., vol. 30, no. 1, pp. 13–28 (2006).
[8] OECD, Artificial Intelligence in Society, Paris, France: OECD Publishing (2022), doi: 10.1787/eedfee77-en.
[9] P. C. Verhoef, T. Broekhuizen, Y. Bart, A. Bhattacharya, J. Q. Dong, N. Fabian, and M. Haenlein, “Digital transformation: A multidisciplinary reflection and research agenda,” J. Bus. Res., vol. 122, pp. 889–901 (2021).
[10] A. Sharma, R. Agarwal, and S. Aggarwal, “Factors influencing social media marketing having the impact on brand awareness and brand image,” Journal of Statistics and Management Systems, vol. 26, no. 3, pp. 745–754 (2023).
[11] K. K. Singh and M. S. Kulkarni, “Customer behaviour in a retail store using IoT & Machine Learning in a cloud environment,” J. Inf. Optim. Sci., vol. 45, no. 8, pp. 1993–2005 (2024).
[12] V. D. Gowda, G. Poornima, H. L. Parashivamurthy, S. Suneetha, K. D. V. Prasad, and S. Chitta, “Real time image analytics in IoT networks: Statistical and AI models for efficient processing,” J. Inf. Optim. Sci., vol. 46, no. 4-B, pp. 1335–1346 (2025).
[13] M.-H. Huang and R. T. Rust, “Artificial Intelligence in service,” J. Serv. Res., vol. 21, no. 2, pp. 155–172 (2018).
[14] A. Bleier, C. M. Harmeling, and R. W. Palmatier, “Creating effective online customer experiences,” J. Mark., vol. 83, no. 2, pp. 98–119 (2019).
[15] M. D. Raza and R. Nikhat, “Purchasing pattern of consumers towards online shopping of electronic goods,” Journal of Statistics and Management Systems, vol. 26, no. 3, pp. 463–472 (2023).
[16] D. Shin, “The effects of explainability and causability on perception,trust,and acceptance: Implications for explainable AI,” Int. J. Hum.-Comput.Stud.,, vol. 146, p. 102551 (2021).
[17] M. Giebelhausen, S. G. Robinson, N. J. Sirianni, and M. K. Brady, “Touch versus tech: When technology functions as a barrier or a benefit to service encounters,” J. Mark., vol. 78, no. 4, pp. 113–124 (2014).
[18] T. Hilken, K. de Ruyter, M. Chylinski, D. Mahr, and D. I. Keeling, “Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences,” J. Acad. Mark. Sci., vol. 45, no. 6, pp. 884–905 (2017).
[19] V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,” MIS Q., vol. 36, no. 1, pp. 157–178 (2012).
[20] A. Mehrabian and J. A. Russell, An Approach to Environmental Psychology. Cambridge, MA: MIT Press (1974).
[21] C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50 (1981).
[22] L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Struct. Equ. Model., vol. 6, no. 1, pp. 1–55 (1999).




