Personalized Digital Marketing for Sustainable Consumer Behaviour: A Study on E-Commerce Platforms, Customer Engagement, and Green Consumption Practices

AI-based personalization is changing the way customers interact with online shopping platforms, but it is important that these developments also support environmental sustainability. This study examines how personalization techniques such as recommendation systems, data analysis, and language-based tools can encourage customers to make eco-friendly choices, reduce unnecessary purchases, and minimize product waste. The research focuses on how personalized shopping experiences can promote sustainable buying habits, lower product returns, and support a circular economy.Using a mixed-method approach, the study analyzes case studies of digital platforms that combine personalized customer experiences with sustainable practices. These platforms are evaluated based on important factors such as customer engagement, purchase behavior, reduction in environmental impact, and improvement of product lifecycle management. Although personalization offers several advantages for sustainable e-commerce, challenges related to data privacy, fairness, and responsible use remain. The study emphasizes the need for responsible and environmentally conscious approaches that provide better customer experiences while supporting long-term sustainability goals.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-02
DOI
https://doi.org/10.5281/zenodo.21126685
Primary Topic
Environmental Sustainability in Business
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Personalized Digital Marketing for Sustainable Consumer Behaviour: A Study on E-Commerce Platforms, Customer Engagement, and Green Consumption Practices

G. Swathi Reddy, Dr. B. RAVI KUMAR, G Ravinder
Zenodo (CERN European Organization for Nuclear Research)
Environmental Sustainability in Business
article

Personalized Digital Marketing for Sustainable Consumer Behaviour: A Study on E-Commerce Platforms, Customer Engagement, and Green Consumption Practices

G. Swathi Reddy, Dr. B. RAVI KUMAR, G Ravinder
article en

Abstract

AI-based personalization is changing the way customers interact with online shopping platforms, but it is important that these developments also support environmental sustainability. This study examines how personalization techniques such as recommendation systems, data analysis, and language-based tools can encourage customers to make eco-friendly choices, reduce unnecessary purchases, and minimize product waste. The research focuses on how personalized shopping experiences can promote sustainable buying habits, lower product returns, and support a circular economy.Using a mixed-method approach, the study analyzes case studies of digital platforms that combine personalized customer experiences with sustainable practices. These platforms are evaluated based on important factors such as customer engagement, purchase behavior, reduction in environmental impact, and improvement of product lifecycle management. Although personalization offers several advantages for sustainable e-commerce, challenges related to data privacy, fairness, and responsible use remain. The study emphasizes the need for responsible and environmentally conscious approaches that provide better customer experiences while supporting long-term sustainability goals.

Zenodo (CERN European Organization for Nuclear Research)
Responsible consumption and production, Life in Land
Openalex Percentile: Top 6%
Environmental Sustainability in Business
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.