35% Conversion Lift: Shopify Seller Uses Qdrant + OpenAI for AI-Powered Product Recommendations

Is your e-commerce store attracting visitors but failing to convert them into buyers? In most cases, the issue lies in search results that fail to connect visitors with the right products. When someone searches for “Laptop bag that fits a 15-inch MacBook” and the search returns generic laptop bags, they leave. This case study discusses how an e-commerce store with similar challenges upgraded its store with a GPT-powered shopping assistant.

Executive Summary

A leading e-commerce apparel store experienced low sales due to declining product discovery as the catalog grew to over 25,000 SKUs, despite significant website visits. To address this, the company implemented an AI-powered shopping assistant built using OpenAI for natural language understanding and a vector database (Qdrant) for semantic product retrieval, integrated directly with its Shopify store. The solution enabled customers to search and interact using conversational queries rather than relying on exact keyword matches and to hep customers find products faster.

Challenges

The following challenges affected the online store before the AI shopping assistant was introduced.

• Traditional rule-based recommendations ignored the nuanced intent in queries, causing irrelevant suggestions and high abandonment.

• The keyword search did not understand the semantic context, synonyms, or related concepts in heterogeneous product catalogs.

• Scaling fast similarity searches across thousands of products led to latency issues with conventional databases.

• Limited real-time personalization struggled with dynamic inventory, user behavior, and multimodal data like images.

• Poor mobile experiences led to high cart abandonment and a lack of immediate and conversational guidance.

• Customers increasingly expected hyper-personalized and round-the-clock support with mounting competition.

The Solution

The e-commerce store’s challenges were addressed by integrating Shopify with Qdrant and OpenAI to recommend products via chat.

Here’s how the integration improved product discovery and overall customer experience.

• Created OpenAI embeddings for product titles, descriptions, reviews, and multimodal image data to capture rich semantics.
• Indexed vectors in Qdrant enabled ultra-fast and accurate similarity searches at scale.
• Developed a GPT-powered chat widget that generates query embeddings instantly and retrieves top matches via Qdrant.
• Implemented hybrid search combining dense vectors with metadata filters for price, category, stock, and promotions.
• Enabled natural language responses with product cards, explanations, and direct add-to-cart links.
• Added proactive engagement based on browsing behavior for timely recommendations.

Implementation Challenges

The team completed implementation in 7 days and had to overcome the following challenges:-

• The team overcame challenges in batching large catalogs for embedding generation while managing OpenAI API rate limits and costs by processing in smaller chunks and using efficient scripting to stay within limits.
• Employed lightweight JavaScript and Shopify's theme app extensions to avoid conflicts with existing layouts and ensure mobile responsiveness.
• Set up webhooks for automatic incremental updates and partial re-indexing in Qdrant, preventing full catalog reloads.

Results

• The AI assistant managed over 40% of site interactions, providing 24/7 personalized support.
• 35% uplift in conversion rates for recommended products versus standard suggestions.
• 28% increase in average order value through upsell and cross-sell recommendations.
• 22% longer average session duration, reflecting deeper customer engagement.
• 15% reduction in bounce rates and a significant decrease in support tickets for queries like sizing or styles.

Key Learnings

• Recommendations improve with clean and consistent product descriptions and metadata, making data quality important before integration.
• Choosing the right embedding strategy is crucial for faster product discovery, and relevant recommendations.

Looking Ahead

Following the excellent results from the AI shopping assistant, the e-commerce store is planning to enable in-chat add-to-cart and simplify checkout to improve the customer experience further.

Contact us to build your AI shopping assistant, powered by SayOne’s expertise and our strategic partnership with Qdrant for smarter, scalable product discovery.