International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Analysis Study Research Paper | Statistics | India | Volume 12 Issue 7, July 2023


Consumer Adoption and Retention for New Generation Tobacco Products - A Machine Learning Approach

Dr. Vinay M. R. | Naveen Kumar T [5]


Abstract: There is a significant transformation in the tobacco industry recently towards reducing the health and hazardous specific risk through an array of new innovations and inventions. This is also in compliance with many regulations and restrictions introduced by governing authorities towards safeguarding tobacco consumers' interest. In this regard, the tobacco industry has witnessed substantial portfolio diversification including introduction of new generation product categories called New Categories. The tobacco companies are making huge investment to popularize these products attracting new as well as already existing FMC consumers in order to cope up with global health interest and to promote less risky products. This is also, to cultivate the habit of consuming nicotine free products among consumers. Though, they are witnessing partial success in this upliftment process, there is a high rate of attrition in the New Category segment in very short period of time. Given that continuously changing nature of consumer behavior and their preference, it is imperative to the business to retain the on boarded consumers and convert them as long - term purchasers; else forego huge investment as well as consumer upliftment. In this regard, many tobacco businesses are using conventional analytical options for identifying such vulnerable consumers; however not able to succeed as they are reactive and ineffective in nature. Hence, there is a strong need for proactive Machine Learning models which can predict the probable churning consumers well in advance. A comprehensive Machine Learning models which capture multidimensional aspects like trend, pattern, demographic, behavioral, product attributes, loyalty, usage, and ownership specific factors makes the solution more robust in predicting the probable churn well in advance. As such, we have focused our study on retaining new consumers proactively in the New Category segment. This paper with an empirical analysis discusses the problem statement, business case, solution approach, modelling, integration, implementation, and recommendations of the Machine Learning solution.


Keywords: Tobacco Industry, Data Science, Machine Learning, Consumer Attrition, Supervised Learning, Predictive Modeling, Consumer Segmentation, Probability of Churn, Segmentation Profiling, Retention Measures


Edition: Volume 12 Issue 7, July 2023,


Pages: 794 - 803


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