How ConvoSearch Boosted Revenue for D2C Brands with Qdrant

How ConvoSearch Boosted E-commerce Revenue with Qdrant

Driving E-commerce Success Through Enhanced Search

E-commerce retailers face intense competition and constant pressure to increase conversion rates. ConvoSearch , an AI-powered recommendation engine tailored for direct-to-consumer (D2C) e-commerce brands, addresses these challenges by delivering hyper-personalized search and recommendations. With customers like The Closet Lover and Uncle Reco achieving dramatic revenue increases, ConvoSearch relies heavily on high-speed vector search to ensure relevance and accuracy at scale.

Overcoming Latency and Customization Limits

Initially, ConvoSearch utilized Pinecone for vector search operations but faced significant limitations: high latency, insufficient customizability, and strict metadata constraints. Pinecone’s query latency of 50–100ms was too slow, and network latency added an additional 50–70ms, severely impacting ConvoSearch’s real-time re-ranking capabilities crucial for personalized recommendations.

Shardul Aggarwal, CEO of ConvoSearch, highlighted, “Latency and customizability were huge issues for us. We needed a solution that was faster, more flexible, and could handle large volumes of metadata without compromises.”

Implementing a Powerful Vector Search Infrastructure

ConvoSearch transitioned to Qdrant for its powerful vector search capabilities, including low-latency queries, expansive metadata storage, and advanced customizability. With Qdrant, queries returned in just 10ms—significantly faster than the previous solution. Qdrant’s robust metadata handling allowed ConvoSearch to incorporate extensive product data and user interactions seamlessly, dramatically improving recommendation accuracy.

The transition to Qdrant enabled ConvoSearch to host its infrastructure on dedicated servers, leveraging NVIDIA GPUs for computationally intensive re-ranking tasks. This provided significant cost savings, better resource optimization, and exceptional speed.

Using Multiple Layers for Highly Relevant Search

ConvoSearch’s approach involves multiple layers. It uses advanced AI models for deep product understanding, extracting semantic context from product data, images, and user interactions. These insights feed into sophisticated re-ranking pipelines, ensuring hyper-personalized and highly relevant search results and recommendations. By continuously optimizing the relevancy of recommendations in real-time, ConvoSearch significantly boosts customer engagement and conversions.

Eliminating the no results dilemma: Standard search vs. ConvoSearch

Immediate and Significant Revenue Impact

The results were transformative:

• Median revenue increase of 23–24% for ConvoSearch’s customer base.
• The Closet Lover experienced a remarkable 60% revenue uplift.
• Uncle Reco saw an incremental revenue boost of $100,000 within a single month after deploying ConvoSearch.

Shardul Aggarwal emphasized, “The impact was immediate. Brands could just plug in our solution and start seeing revenue growth without any changes to their front-end.”

Qdrant’s infrastructure delivered substantial operational benefits:

• Reduced query latency from 50–100ms to approximately 10ms.
• Enhanced scalability, managing thousands of daily product updates efficiently.
• Enabled detailed product understanding and real-time personalization, setting ConvoSearch apart from competitors.

Scaling the Recommendation Engine

By shifting to Qdrant, ConvoSearch not only overcame infrastructure challenges but significantly enhanced their ability to drive tangible business outcomes for their customers. As Aggarwal summarized, “Our move to Qdrant transformed our recommendation engine capabilities, making us indispensable to our clients.”