AI-Powered E-commerce Recommendation System

Enhanced shopping experience through personalised product discovery and intelligent recommendations.

PROJECT OVERVIEW

In today's competitive e-commerce market, an international European cosmetics retailer with 2 million monthly visitors wanted to improve their customer experience while increasing revenue per visitor.

The business faced key challenges including generic product displays, low customer engagement, and weak cross-selling strategies that limited their growth potential.

Softblues’s team developed an AI-powered recommendation system using machine learning algorithms and real-time behavior tracking, delivering personalized shopping experiences, intelligent product suggestions, and optimized content to convert more visitors into customers and boost satisfaction.

TECHNOLOGY

Python, TensorFlow, Collaborative Filtering, Content-Based Filtering, Real-time Analytics, Redis, PostgreSQL, API Integration, Machine Learning, Customer Segmentation, A/B Testing Framework

Client Goals and Objectives

Increase conversion rates by showing relevant products to each customer.

Boost average order value through smart cross-selling and upselling.

Reduce cart abandonment with personalized product suggestions.

Improve customer experience with tailored shopping recommendations.

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Data Processing
Pipeline

Database
Architecture

AI Agents

Context
Management

Comparison
and Analysis

Value and Impact Delivered

Conversion Rate Boost

Increased overall conversion rate by 35% through personalized product suggestions.

Revenue Growth

Boosted average order value by 28% with intelligent cross-selling recommendations.

Customer Engagement

Significantly improved customer session duration with relevant product discoveries.

Cart Recovery

Reduced cart abandonment by 22% through personalized follow-up recommendations.

Business Efficiency

Automated majority of product merchandising decisions with AI-driven insights.

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