Predictive and Personalized Marketing: How AI and ML are Revolutionizing Sales on Social Media in Pune City

Jairaj Sasane; Ganesh Waghmare; Arvind Burande; Sanket Charkha; Harshali Bhalerao; Pallavi Patil; Ashok Todmal; Shravasti Jain

doi:10.63931/ijchr.v7iSI2.525

Authors

Jairaj Sasane Dr. D Y Patil Dnyan Prasad University School of Management and Research Pune, India https://orcid.org/0009-0004-7957-795X
Ganesh Waghmare MIT College of Management MIT Art, Design and Technology University, Pune, (Maharashtra), India https://orcid.org/0000-0001-9396-3225
Arvind Burande Indira School of Business Studies Savitribai Phule Pune University Pune, India https://orcid.org/0000-0002-1010-6672
Sanket Charkha Savitribai Phule Pune University, Sinhgad Institutes Pune, India https://orcid.org/0009-0001-1289-3294
Harshali Bhalerao Dr. D Y Patil Dnyan Prasad University School of Management and Research Pune, India https://orcid.org/0000-0001-7193-0502
Pallavi Patil Dr. D Y Patil Dnyan Prasad University School of Management and Research Pune, India https://orcid.org/0009-0004-5257-4767
Ashok Todmal MIT College of Management MIT Art, Design and Technology University, Pune, (Maharashtra), India https://orcid.org/0000-0002-0144-7758
Shravasti Jain Ramachandran International Institute of Management, Pune

DOI:

https://doi.org/10.63931/ijchr.v7iSI2.525

Keywords:

Artificial Intelligence (AI),, Machine Learning (ML),, Predictive Marketing, Personalised Marketing, Consumer Behaviour, Social Media Marketing, Data Privacy, Pune City, Technology Acceptance Model (TAM),, Theory of Planned Behaviour (TPB).

Abstract

The haste with which Artificial Intelligence (AI) and Machine Learning (ML) have been integrated into marketing strategies transformed how companies engage with their customers, through the social media platforms. The study is titled Predictive and Personalized Marketing: How AI and ML are Revolutionizing Sales on social media in Pune City and examines the effect of predictive analytics and personalized marketing on the perception of the customer, their trust, purchasing intentions, and sales outcomes. Primary data were collected using a mixed approach in which 250 respondents were obtained using structured questionnaires, but these data were complemented by semi-structured interviews. The quantitative research with the use of SPSS revealed that predictive analytics significantly facilitates perceptions of AI adoption, whereas customization significantly increases customer engagement and buying behavior. Trust turned out to be a critical intermediary, and the protection of privacy and openness were primary stimulus levers to adoption.

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