ModaStyle: 23% Conversion Increase with Product RAG Assistant

Company Overview

ModaStyle is a French fashion e-commerce platform selling clothing and accessories for women aged 25-45. With a catalog of 8,000+ products and 150,000 monthly visitors, they faced the classic e-commerce challenge: helping customers find exactly what they're looking for.

The Challenge

Product Discovery Problem

• Bounce rate: 67% on category pages
• Search abandonment: 45% of searches led to exits
• Average time to purchase: 12 minutes (too long)
• 28% of customers contacted support for product recommendations

Limitations of Traditional Search

• Keyword-based search missed intent ("dress for beach wedding")
• Filters too complex for casual shoppers
• No understanding of style preferences or body types
• Product descriptions weren't optimized for search

High Return Rate

• 32% return rate on clothing
• Main reason: "Didn't match expectations" (54%)
• Cost: €15 per return processing

The Solution: RAG Product Assistant

ModaStyle implemented a conversational product assistant using Ailog, trained on:

Product Knowledge Base

• 8,000+ product descriptions with detailed attributes
• Size guides for each brand
• Style guides and outfit combinations
• Customer reviews and ratings
• Seasonal trend reports

Natural Language Understanding

The assistant handles queries like:

• "I need a dress for a summer wedding, budget around €150"
• "What goes well with these navy blue pants?"
• "I'm size 38 in Zara, what size for your brand?"
• "Show me sustainable fashion options"

Implementation

Phase 1: Product Catalog Integration (Week 1)

• Connected PrestaShop product feed
• Enriched product data with style attributes
• Created size conversion tables

Phase 2: Conversational Design (Week 2)

• Designed conversation flows for common scenarios
• Created style recommendation logic
• Built outfit combination suggestions

Phase 3: Testing & Optimization (Week 3-4)

• A/B tested widget placement
• Optimized prompts for fashion context
• Trained on edge cases from beta testers

Results After 4 Months

Conversion Metrics

Revenue Impact

• Additional monthly revenue: €45,000
• Annualized: €540,000 additional revenue
• ROI on RAG investment: 150x

Customer Experience

Sample Conversations

Style Recommendation

Customer: "I have a job interview next week for a marketing position. I want to look professional but modern."

Assistant: "For a marketing interview, you want to balance professionalism with creative flair. Based on your size (42) and style preferences, I recommend:

1. Navy Tailored Blazer (€129) - Modern cut, subtle texture
2. White Silk Blouse (€79) - Elegant but not stiff
3. High-waisted Trousers (€89) - Flattering fit

Would you like to see these items or explore other options?"

Size Assistance

Customer: "I'm usually a Medium but your sizes seem different. I'm 170cm, 65kg."

Assistant: "Based on your measurements, I recommend a size 40 in our collection. Our brand runs slightly smaller than average. For tops, the 40 will give you a comfortable fit with room to move. Would you like me to show you items in size 40 that match your style preferences?"

Key Success Factors

1. Rich Product Data

Invested in enriching product attributes beyond basic descriptions:

• Occasion tags (wedding, office, casual)
• Style attributes (bohemian, minimalist, classic)
• Fit descriptions (relaxed, fitted, oversized)
• Sustainability certifications

2. Personalization

The assistant remembers:

• Size preferences across brands
• Style preferences from past interactions
• Budget range
• Color preferences

3. Visual Integration

Combined text responses with product images and direct "Add to Cart" actions.

4. Honest Recommendations

Trained the assistant to:

• Suggest alternatives if a product might not fit well
• Mention potential issues (e.g., "this runs large")
• Recommend against impulse purchases when relevant

Technical Implementation

RAG Configuration

Product catalog: 8,000 items
Embedding model: OpenAI text-embedding-3-small
Retrieval: Hybrid search with category filtering
LLM: GPT-4 Turbo (fashion-tuned prompts)
Temperature: 0.5 (creative but factual)

Integration Points

• PrestaShop product sync (real-time inventory)
• Customer account integration (size/style history)
• Cart integration (add to cart from chat)
• Analytics integration (conversion tracking)

ROI Analysis

Investment

• Ailog subscription: €99/month
• Product data enrichment: €2,000 (one-time)
• Integration development: €3,000 (one-time)

Monthly Returns

• Additional revenue: €45,000
• Return reduction savings: €5,000
• Support cost savings: €2,500

Payback period: 4 days

Lessons Learned

1. Product data quality is everything - The assistant is only as good as your product information
2. Fashion needs personality - Generic responses don't work; inject brand voice
3. Size confidence drives purchases - Solving size uncertainty has the biggest conversion impact
4. Don't over-sell - Honest recommendations build trust and reduce returns

Future Plans

ModaStyle is expanding the RAG assistant to:

• WhatsApp integration for mobile shoppers
• Visual search ("find items similar to this photo")
• Personal stylist appointments via chat
• Post-purchase outfit suggestions

Conclusion

For e-commerce, RAG isn't just customer support—it's a sales multiplier. ModaStyle's 23% conversion increase demonstrates that conversational product discovery can dramatically outperform traditional search and filters.

The key is treating the assistant as a knowledgeable sales associate, not just a search box with AI.

Ready to transform your e-commerce? Try Ailog free and connect your product catalog in minutes.

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This case study is based on aggregated data from e-commerce implementations. Company name and some figures have been adjusted for confidentiality.