Beauty Meets Bytes: Revolutionizing Cosmetic Marketing with AI & ML in the Indian Market

Authors

  • Monali Sharma St. Francis Institute of Management and Research, Borivali (W), Mumbai
  • Christina Shiju SIES Institute of Management, Nerul, Navi Mumbai

DOI:

https://doi.org/10.70917/ijcisim-2026-5065

Keywords:

Artificial Intelligence, Machine Learning, TAM, consumer, cosmetic

Abstract

The cosmetic industry has undergone significant transformation with the advent of Artificial Intelligence (AI) and Machine Learning (ML). These technologies have enhanced consumer experiences by personalizing product offerings, improving marketing strategies, and streamlining customer engagement. This paper explores the use of AI and ML in four critical areas of cosmetic marketing: virtual testing of products, product customization, face mapping for personalized recommendations, and the communication, promotion, and sales channels. Furthermore, the paper examines how ML algorithms predict consumer behavior, optimize marketing efforts, and maximize customer satisfaction. The study has focused on major brands in India L’oreal and Lakme with other brands in the cosmetic marketspace for women like Sugar Cosmetics, Lotus Herbal and Forest Essentials. The paper has assessed the usage of AI and ML by these brands for customer engagement and marketing . Global giant brands like L'oreal with their global presence were found to be heavily focused on integrating AI and ML at all levels with the consumer, probably due to the financial resources for the investment. Lakme, an established profitable brand from India was found to engage with consumers to a greater extent as compared to newer brands like Forest Essentials, Sugar and Lotus Herbal. All brands were found to have significant  level of AI and ML engagement, though the technology used was found to differ.

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Published

2026-08-24

How to Cite

Monali Sharma, & Christina Shiju. (2026). Beauty Meets Bytes: Revolutionizing Cosmetic Marketing with AI & ML in the Indian Market. International Journal of Computer Information Systems and Industrial Management Applications, 18(19s), 591–601. https://doi.org/10.70917/ijcisim-2026-5065

Issue

Section

Original Articles