Enhancing Customer Personalization for an E-Commerce Platform

A mid-sized e-commerce business specializing in fashion and lifestyle products wanted to enhance its customer personalization and recommendation system to boost conversions.

Industry

Retail & E-Commerce

Services

AI-driven recommendation engine, Machine Learning Models

Tools We used

Python, TensorFlow, AWS Lambda, PostgreSQL, React.js

The Problem with Existing System

01

Abandoned Carts

The client faced challenges in offering relevant product recommendations, leading to low engagement rates and abandoned carts.

02

Inefficient System

Their existing rule-based recommendation system was inefficient in handling dynamic customer preferences.

TechKors Solution

TechKors developed an AI-driven recommendation engine using Machine Learning models to analyze customer behavior, purchase history, and browsing patterns. The system was integrated seamlessly into the client’s existing e-commerce platform.

01

Recommendation System

Developed and deployed a real-time recommendation system powered by collaborative filtering and deep learning algorithms.

03

Website Performance

Optimized website performance to handle large-scale AI-driven computations without affecting speed.

02

Customer Segmentation Models

Built customer segmentation models to personalize offers based on user preferences.

Results

Increase Conversion

20% increase in conversion rates due to improved personalized recommendations.

Improve Response Time

30% improvement in average order value (AOV) as customers engaged with AI-powered upselling.

Reduction in Cart Abandonment

Reduction in cart abandonment rates by 25%, improving revenue retention
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