Optimizing ad targeting and content personalization in e-stores through machine learning algorithms
Keywords:
machine learning, Python, customer trust, Personalization, system integration, customer experience optimizationAbstract
This study focuses on e-commerce stores in Algeria and identifies technical challenges related to system policies, customer trust, technical knowledge, data confidentiality, and platform integration. She examines how digital customer relationships and machine learning algorithms can improve user experience, security, and productivity. Using Python for data analysis, the study shows that slow internet speeds and low trust in online payments hinder platform integration and affect ad targeting and content personalization. Proposed solutions include strengthening infrastructure with caching technology, improving security through fraud detection algorithms, and providing artificial intelligence training for store managers. Results show a 60% increase in click-through rates and a 77% increase in conversion rates after implementation, confirming the algorithm's ability to increase engagement and retention. The study claims that these improvements will enhance the competitiveness of Algerian e-commerce in the modern economy by improving customer experience and market efficiency and providing insights for local and global contexts.
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Copyright (c) 2025 Soufayne Baouali, Ikram Khelia, Selma Douah
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