AI for Customer Experience & Sales in Global E-Commerce
The Problem: As global e-commerce platforms race to adopt AI, many lack strategic clarity on which technologies actually improve customer experience and drive conversion especially in diverse market contexts.
The Work
Secondary Research & Strategic Analysis
Researched the use of machine learning in e-commerce including predictive personalization, recommendation engines and engagement automation.
Relevance to Education: Highlights how AI systems can shape user journeys, adapt to learning behaviors and improve or distort personalization in edtech and learning analytics.
The Insight
AI tools that succeed in e-commerce are not just technically advanced, they’re behaviorally aligned. The research emphasized that successful implementation depends on clear user segmentation, frictionless flow and cultural adaptation

Why It Matters
This project sharpened my understanding of how AI systems intersect with consumer behavior, sales logic and customer trust and gave me a practical lens for connecting policy-level AI discussions to business outcomes.


Tools Used
- Zotero
- Google Scholar
- Canva (presentation)
- Notion
- Academic journal databases
- Miro (mapping frameworks)
Skills Highlighted
- Secondary research
- Behavioral analysis
- Strategic synthesis
- AI application evaluation
- Insight distillation
- Business storytelling
