Leveraging AI for Predictive Consumer Insights in Green Product Marketing: A Case of North Karnataka MSMEs

Overview

This study examines the application of artificial intelligence for generating predictive consumer insights in the context of green product marketing among Micro, Small, and Medium-Sized Enterprises (MSMEs) in North Karnataka. The research addresses the challenge faced by MSMEs in effectively assessing and predicting consumer preferences for environmentally sustainable products due to limited analytical capabilities. As environmental sustainability becomes increasingly central to competitive positioning, MSMEs are compelled to adopt green marketing strategies but often lack the infrastructure and expertise to leverage advanced analytical tools. The study investigates how AI-driven predictive analytics can bridge this gap by providing data-driven insights that enhance marketing effectiveness and consumer preference prediction accuracy.

Methods and approach

The research employed a descriptive and analytical study design to investigate AI adoption and its impact on green marketing effectiveness among MSMEs. Primary data were collected from 150 MSMEs in North Karnataka through a structured questionnaire instrument. The analysis utilized multiple statistical techniques including regression analysis, analysis of variance (ANOVA), correlation analysis, and descriptive statistics to test the hypothesized relationships. These quantitative methods enabled examination of the relationship between AI adoption and predictive accuracy of consumer preferences, as well as the impact on green marketing effectiveness while accounting for potential barriers to implementation.

Key Findings

The findings indicate that AI adoption significantly enhances the prediction accuracy of consumer preferences and positively impacts green marketing effectiveness among the surveyed MSMEs. Statistical analysis confirmed the beneficial relationship between AI implementation and marketing outcomes. However, the study revealed that despite moderate awareness of AI tools among MSMEs, actual adoption rates remain constrained by several barriers. Cost limitations, skill shortages within the workforce, and inadequate infrastructure emerged as primary obstacles preventing broader implementation of AI-driven predictive analytics for green marketing applications in the regional MSME sector.

Implications

The research demonstrates that strategic deployment of AI-based predictive analytics can substantially improve green marketing efficacy and support sustainable growth trajectories for MSMEs in regional economies. The identification of adoption barriers suggests that policy-level interventions are necessary to facilitate broader AI integration among small and medium enterprises. Specifically, capacity-building initiatives addressing skill development, infrastructure enhancement, and cost mitigation are recommended to enable MSMEs to leverage AI technologies effectively. These findings have relevance for policymakers designing support programs for MSMEs and for enterprise managers seeking to enhance environmental marketing strategies through technological adoption in resource-constrained contexts.