AI-Powered E-Commerce Customer Journey Analytics

Admin settings & System state
cart
saved list
Admin reports & CSV export
Customer segmentation analyticsc
Home
Ai customer behaviour
Full funnel journey analytics
Product intelligence & Abandonment

Inspiration

E-commerce websites generate a large amount of customer activity, but understanding the complete journey from search to purchase can be difficult. We wanted to build a system that not only provides an online shopping experience but also helps businesses understand how customers interact with the platform.

What We Built

We built an AI-Powered E-Commerce Customer Journey Analytics platform that connects an e-commerce website with an analytics dashboard. The system tracks customer actions such as searches, product views, wishlist activity, cart additions, checkout and purchases using clickstream data.

The collected data is stored in a PostgreSQL database and used to reconstruct customer journeys, analyze conversion funnels, identify drop-off points and understand customer behaviour.

We also integrated AI-based prediction to estimate purchase probability and cart-abandonment probability based on observed customer behaviour. A recommendation system provides personalized products based on customer interactions and preferences.

How It Works

Customer actions → Clickstream Tracking → FastAPI Backend → PostgreSQL → Customer Journey Analytics → AI Prediction → Product Recommendations → Admin Dashboard

The project includes both a customer-facing e-commerce website and a separate admin analytics dashboard.

Challenges We Faced

The main challenges were connecting frontend actions with backend APIs, storing and organizing clickstream events, reconstructing customer journeys from session data, and creating meaningful AI predictions from behavioural data.

What We Learned

Through this project, we learned full-stack web development, REST APIs, PostgreSQL database management, clickstream analytics, customer journey mapping, machine learning, recommendation systems and integrating AI into a real-world web application.