Built an AI shopping assistant that provides personalized product recommendations, answers questions, and guides customers through purchase decisions, increasing conversion rates and reducing cart abandonment.
Key Results
28% — Increase in conversion rate
35% — Reduction in cart abandonment
$3.2M — Additional revenue in first 6 months
50% — Of product questions answered autonomously
Client Requirements
An online fashion retailer with 2,000+ products struggled with high cart abandonment and low engagement on product pages.
- Provide personalized product recommendations based on browsing behavior
- Answer customer questions about sizing, materials, care instructions, shipping
- Guide customers through style preferences and occasion-based shopping
- Handle abandoned cart recovery via conversational nudges
- Integrate with existing product catalog and inventory system
- Support multilingual conversations (English, Spanish, French)
- Escalate to human support for complex issues or complaints
- Track conversation-driven conversions and revenue attribution
Challenge: Customers browsed extensively but struggled to find the right products. 60% of site visitors left without adding items to cart. Human chat support was expensive and only available during business hours.
Solution Developed
Conversational Interface: Chat widget embedded on product pages and checkout. Proactive prompts based on user behavior (e.g., "Need help choosing a size?").
Product Recommendation Engine: Hybrid model combining collaborative filtering, content-based filtering, and LLM reasoning. Considers style preferences, occasion, budget, and past purchases.
RAG-Based Question Answering: Vector database storing product descriptions, sizing charts, return policies, and FAQs. LLM retrieves relevant info and generates natural responses.
Shopping Assistant Agent: Multi-turn conversational agent that asks clarifying questions, narrows product selection, and suggests complementary items.
Abandoned Cart Recovery: Detects cart abandonment and proactively messages customers with personalized incentives or answers to common concerns.
Human Handoff: Seamless escalation to human support with full conversation context when AI confidence is low or customer requests it.
Activities Performed
- Data analysis: Reviewed 6 months of chat transcripts and browsing data
- Recommendation training: Built collaborative filtering models on purchase history
- Knowledge base: Structured product data, FAQs, policies into vector DB
- Agent development: Built conversational flows with LangGraph
- A/B testing: Tested different prompt strategies and escalation thresholds
- Integration: Connected to Shopify catalog and Zendesk support
- Pilot: Launched to 10% of traffic, measured conversion lift
- Rollout: Expanded to 100% after 4-week pilot
Technologies Used
LLM: OpenAI GPT-4 (conversation, product reasoning)
Orchestration: LangChain, LangGraph (multi-turn agent flows)
Recommendations: TensorFlow (collaborative filtering), scikit-learn
Vector DB: Pinecone (product search, FAQ retrieval)
E-commerce Platform: Shopify API (product catalog, cart, checkout)
Support Integration: Zendesk Chat API (human handoff)
Analytics: Segment, Google Analytics (conversion tracking)
Outcomes
- Conversion rate: 2.1% → 2.7% (28% increase)
- Cart abandonment: 68% → 44% (35% reduction)
- Average order value: +12% (upsell suggestions)
- Customer questions: 50% handled by AI, 50% escalated
- Customer satisfaction: 4.2/5 rating for AI interactions
- Revenue attribution: $3.2M directly tied to AI conversations
Boost e-commerce conversion with AI shopping assistants.