Boosting E-Commerce Search Relevance with Unified Product Intelligence

Overview 

A leading online retailer was struggling with low conversion rates and poor search relevance caused by fragmented and inconsistent product data. Customers frequently encountered irrelevant results, missing attributes and confusing product listings, leading to frustration and abandoned sessions.

SparkVerse AI implemented a data unification and semantic search solution that transformed the retailer’s product discovery experience, resulting in measurable improvements in relevance, engagement and revenue.

The Challenge

The retailer’s product catalog was sourced from multiple vendors and internal systems, each using different schemas, naming conventions and attribute definitions. This created several critical issues which resulted in customers struggling to find relevant products efficiently, and the business experienced lower engagement and reduced on-site conversions:

• Fragmented product data across supplier feeds and internal databases
• Inconsistent attributes (e.g., size, color, specifications) across listings
• Missing metadata limiting search accuracy
• Keyword-based search that failed to understand user intent.

Solution 

SparkVerse AI addressed these challenges by deploying its Data Ingestion and Enrichment Engine, enabling data unification across fragmented vendor feeds and internal systems. The engine consolidated, standardized and enriched the retailer’s entire product catalog, creating a reliable foundation for intelligent discovery.

Key solution components included:

• Automated data ingestion from multiple vendor and supplier feeds
• Attribute normalization and enrichment to establish a consistent, high-quality product schema
• Semantic embeddings to represent products and user queries beyond exact keyword matching
• Knowledge graph construction to model relationships between products, attributes, and categories
• AI-driven semantic search that interprets user intent and contextual meaning

This unified and semantically enriched product knowledge enabled the search engine to deliver precise and relevant results, even when customer queries were vague, incomplete or phrased differently from the underlying product metadata.

Outcome / Impact 

Within just 60 days, the retailer observed significant performance gains:

• 35% improvement in search relevance, measured through user engagement signals
• 22% increase in on-site conversions, driven by better product discovery
• 40% reduction in product data processing time, improving operational efficiency

The AI-powered search experience reduced customer frustration, increased session depth, and accelerated time-to-purchase.

Technologies Used 

• SparkVerse Data Ingestion and Enrichment Engine
• Transformer-based semantic embeddings
• Elastic vector search infrastructure
• Knowledge graph construction framework

Why It Matters

This case study demonstrates how clean, enriched and semantically structured product data is foundational to modern e-commerce success. By moving beyond traditional keyword-based search, businesses can deliver intent-aware discovery experiences that directly impact conversion rates and customer satisfaction.

Ready to Transform Your Product Search?

SparkVerse AI helps enterprises unlock the full value of their product data with AI-driven enrichment, semantic search, and knowledge graph solutions.

Contact SparkVerse AI to see how intelligent product discovery can drive measurable business outcomes.

Disclaimer

This case study presents representative results that illustrate SparkVerse AI’s capabilities. Client details have been anonymized to protect confidentiality.