Executive Summary

  • Client: A leading e-commerce retailer operating globally.
  • Challenge: Customers struggled to find personalized product recommendations, leading to lower conversions and higher cart abandonment.
  • Solution: Implemented an AI-powered personal shopping assistant that provides real-time, context-aware recommendations.
  • Results:
    • 25% increase in average order value.
    • 30% higher conversion rate due to personalized recommendations.
    • 40% boost in customer engagement through AI-driven interactions.

“American Chase’s AI shopping assistant transformed how our customers shop, making their experience seamless and highly personalized.”

  – Head of Digital Commerce

Client Background

Who They Are

A global e-commerce brand selling fashion, electronics, and home goods with millions of daily visitors.

Pre-Challenge State

  • Customers had difficulty discovering relevant products.
  • Basic filters and static recommendations led to low personalization.
  • High cart abandonment rates due to lack of tailored suggestions.

“Customers often left our site without purchasing because they couldn’t easily find what they needed.“

 – VP of E-Commerce

The Challenge

Pain Points

  1. Low Product Discovery: Customers had to manually browse large inventories.
  2. Generic Recommendations: Existing recommendation engines were not personalized.
  3. High Cart Abandonment: Lack of engagement led to lost sales.

Business Impact

  • Missed revenue opportunities due to poor personalization.
  • Customer frustration from irrelevant product suggestions.
  • Low repeat purchases as shoppers didn’t find the experience engaging.

Client Goals

  • Provide hyper-personalized shopping recommendations.
  • Improve customer engagement and boost conversions.
  • Reduce cart abandonment through real-time assistance.

The Solution

Approach

  • Built an AI-powered shopping assistant that interacts with customers via chat and voice.
  • Integrated real-time behavioral tracking to suggest relevant products dynamically.
  • Used NLP and deep learning to understand customer intent and preferences.

Tools & Technologies Used

  • Conversational AI: Rasa (for chatbot interactions).
  • Personalized Recommendations: OpenAI GPT + Hugging Face Transformers.
  • Speech Recognition: Whisper (for voice-based shopping).
  • Cloud AI Services: Google Vertex AI (for real-time data processing).
  • Integration & Automation: LangChain (for chatbot logic and memory).

Key Features

  1. Conversational Shopping Assistant: AI chatbot helps users find products via chat or voice.
  2. Real-Time Personalized Suggestions: AI analyzes browsing history and behavior to suggest relevant products.
  3. Virtual Try-On (for Fashion & Accessories): Uses AI to let users visualize products.
  4. Seamless Checkout Assistance: AI nudges users to complete purchases by offering discounts and reminders.

Implementation Process

Timeline

  • Phase 1 (Requirement Analysis & AI Model Selection): 4 weeks.
  • Phase 2 (Development & AI Training): 8 weeks.
  • Phase 3 (Integration & Testing): 6 weeks.

Team Structure

  • AI Engineers
  • Data Scientists
  • UX Designers
  • Cloud Architects

Overcoming Hurdles

  • Challenge:  Users were skeptical about AI recommendations.
    • Solution: AI continuously learned from interactions to improve suggestions.
  • Challenge: Ensuring fast response times.
    • Solution: Optimized cloud-based AI models for real-time processing.

Results and Impact

Quantitative Metrics

25%

Increase in Average Order Value

Customers bought more due to better recommendations.

30%

Higher Conversion Rate

Personalized product suggestions improved sales

40%

Boost in Engagement

Shoppers interacted more with the AI assistant.

Qualitative Benefits

  • 🏆 Better User Experience: Customers found relevant products faster.
  • 🔄 Higher Customer Retention: Personalized shopping increased repeat purchases.

“Our AI shopping assistant made online shopping feel like having a personal stylist. Customers love the experience!“

  – Head of Customer Experience

Project Snapshot

  • Client: Global E-Commerce Brand
  • Project Duration: 4 months
  • Technologies: OpenAI GPT, Rasa, Whisper, Google Vertex AI, LangChain
  • Key Metric: 30% higher conversion rate

“American Chase’s AI-powered shopping assistant revolutionized online shopping, delivering highly personalized, engaging experiences.“

Summary

Developed an AI-based shopping assistant that increased customer engagement and sales by 25%.