Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends

In the backdrop of increasing sustainability awareness among Indian consumers, this study explores the role of machine learning (ML) in optimizing green influencer marketing strategies to drive eco-conscious purchasing. While eco-friendly consumer behavior and influencer marketing have gained traction, there remains limited empirical evidence on how ML-enabled recommendation systems can enhance green influencer effectiveness in India. Employing a quantitative research design, data were gathered through surveys of 1,500 users of an Indian e-commerce platform. Respondents provided insights on their interactions with green influencers, their perceptions of influencer authenticity and transparency, and the impact of ML-driven recommendations on purchase intent. Factor and correlation analyses examined the relationships among perceived authenticity, consumer trust, and purchase behavior. Findings reveal that influencer trustworthiness, particularly authenticity and transparency, significantly drives consumer engagement with green products. Most respondents expressed willingness to purchase green products when the messaging was authentic and well-targeted. Moreover, ML algorithms were instrumental in identifying top-performing influencers, segmenting audiences by green preferences, and personalizing recommendations, which enhanced engagement and conversion rates. Positive correlations were observed between influencer authenticity, trust, and purchase intention. This study fills a regional gap by offering India-specific, empirical evidence on the synergy between ML-driven marketing and green consumer behavior. Its practical implications are twofold: marketers can leverage these insights to enhance influencer selection and recommendation strategies, while policymakers and researchers gain a data-informed perspective to promote sustainable marketing practices. The study demonstrates that ML-augmented green influencer marketing can effectively elevate sustainability and commercial performance within the Indian e-commerce context.

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Publication Details

Journal
Journal of Business and Social Sciences
Published
2025-09-01
DOI
https://doi.org/10.61453/jobss.v2025no06
Citations
1
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
4.20
Controls
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article

Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends

Neetu Sharma
1 citations
Journal of Business and Social Sciences
Digital Marketing and Social Media
4.20
article

Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends

Neetu Sharma
article en
1 citations

Abstract

In the backdrop of increasing sustainability awareness among Indian consumers, this study explores the role of machine learning (ML) in optimizing green influencer marketing strategies to drive eco-conscious purchasing. While eco-friendly consumer behavior and influencer marketing have gained traction, there remains limited empirical evidence on how ML-enabled recommendation systems can enhance green influencer effectiveness in India. Employing a quantitative research design, data were gathered through surveys of 1,500 users of an Indian e-commerce platform. Respondents provided insights on their interactions with green influencers, their perceptions of influencer authenticity and transparency, and the impact of ML-driven recommendations on purchase intent. Factor and correlation analyses examined the relationships among perceived authenticity, consumer trust, and purchase behavior. Findings reveal that influencer trustworthiness, particularly authenticity and transparency, significantly drives consumer engagement with green products. Most respondents expressed willingness to purchase green products when the messaging was authentic and well-targeted. Moreover, ML algorithms were instrumental in identifying top-performing influencers, segmenting audiences by green preferences, and personalizing recommendations, which enhanced engagement and conversion rates. Positive correlations were observed between influencer authenticity, trust, and purchase intention. This study fills a regional gap by offering India-specific, empirical evidence on the synergy between ML-driven marketing and green consumer behavior. Its practical implications are twofold: marketers can leverage these insights to enhance influencer selection and recommendation strategies, while policymakers and researchers gain a data-informed perspective to promote sustainable marketing practices. The study demonstrates that ML-augmented green influencer marketing can effectively elevate sustainability and commercial performance within the Indian e-commerce context.

Journal of Business and Social SciencesVol. 2025(1)
St Xavier’s College (IN)
Openalex Percentile: Top 6%
Digital Marketing and Social Media
4.20
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Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends — Neetu Sharma · Journal of Business and Social Sciences (2025) | TGRS Research Map | TGRS