Revolutionizing Product Search & Recommendation with AI

Decision Intelligence for Transforming Product Discovery

How AI Enhances Product Recommendations for Better Engagement

Achieving Personalization with Machine Learning Models

Real-World Impact of AI-Powered Search and Recommendations

AI’s Role in Enhancing Search Accuracy and Relevance

Personalization at Scale: The Power of AI Recommendations

Key Machine Learning Models for Product Search Optimization

Case Study: AI-driven Transformation in Product Discovery

Improving Customer Experience with AI-powered Search

AI-driven Strategies for Personalized Product Suggestions

Optimizing Product Discovery with Machine Learning

Overcoming Challenges in AI-powered Search Implementations

Synopsis

A Fortune 500 specialty materials manufacturer partnered with USEReady’s Decision Intelligence practice to modernize its product search and discovery. The company’s existing keyword-based search system was inefficient, often returning irrelevant results and frustrating customers.

USEReady implemented a Generative AI-powered search solution, built on a strong data foundation enabled by Snowflake, and leveraged Natural Language Processing (NLP) and Semantic Search. This enabled customers to find the right products faster. The transformation resulted in a 60% improvement in search efficiency, higher engagement, and an 80% increase in customer satisfaction, driving better conversion rates and long-term customer loyalty.

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Customer

A global leader in specialty materials manufacturing, the client serves a range of industries including aerospace, automotive, construction, consumer goods, electronics, and energy. With over a century of experience, the Fortune 500 firm is known for its innovation, quality, and ability to solve complex material challenges for industrial applications.

Business Challenges

The company’s vast product catalog spans thousands of specialized materials used in diverse industrial applications. However, its existing keyword-based search system was failing. Customers struggled to find relevant materials due to slow and inaccurate search results, leading to lost opportunities and lower engagement. To resolve this, the company needed a more intelligent, intuitive search mechanism that could understand complex user queries and deliver precise, relevant recommendations at scale.

USEReady Solution

USEReady’s Decision Intelligence practice developed an AI-powered product search and discovery solution built on a Snowflake data platform and leveraged advanced AI capabilities, including Generative AI, NLP, and Semantic Search.
Intelligent Data Processing for Product Search
  • Unified structured and unstructured data from multiple sources such as Snowflake, Microsoft Word, and Excel
  • Leveraged Snowflake's elastic scalability and secure data sharing to create a centralized, enriched knowledge base of product attributes, properties, and applications
  • Enabled real-time access and processing for downstream AI models
Custom Named Entity Recognition (NER) Model
  • Developed a domain-specific AI model to accurately extract and categorize key product attributes
  • Enabled precise material identification, helping users find products based on features, performance, and industry-specific requirements
Lexical & Semantic Search for Greater Accuracy
  • Lexical search improved traditional keyword matching
  • Semantic search understood context and user intent, delivering more relevant and intuitive results
Generative AI-Powered Personalized Recommendations
  • Used OpenAI's Generative AI to produce smart, natural-language responses
  • Delivered personalized material recommendations and alternatives based on Snowflake-hosted data
  • Enabled customers to interact conversationally with the AI engine, improving user experience

Key Outcomes

The implementation of this AI-powered product discovery solution delivered measurable business impact:
60% faster product searches
60% faster product searches, reducing friction in the customer journey
80% increase in user satisfaction
80% increase in user satisfaction, enhancing retention and brand loyalty
Significantly higher engagement and conversion rates, driving business growth
Significantly higher engagement and conversion rates, driving revenue growth
More precise and intelligent search results, improving decision-making for customers
More precise, intelligent search results powered by AI-trained on Snowflake-managed datasets
AI-driven personalized recommendations, unlocking cross-selling and upselling opportunities
AI-driven, contextual recommendations built from real-time Snowflake queries, unlocking cross-selling and upselling opportunities

Lessons Learned

This success story highlights critical best practices for implementing AI in product discovery:
  • Combining multiple AI techniques (NLP, Generative AI, Semantic Search) maximizes accuracy and relevance
  • Clean, well-structured, and centrally managed data is crucial (Snowflake enabled data unification and access for downstream AI use)
  • Continuous testing and model refinement are key to continuously improving search quality
  • Scaling AI across business functions, with Snowflake as the single source of truth, can unlock new efficiencies and revenue streams

Conclusion

For manufacturers (whether B2B or B2C) AI-driven product search can do wonders. The ability to find the right product quickly and accurately enhances customer experience, boosts engagement, and drives revenue. By integrating Generative AI, NLP, and Semantic Search on top of a modern data stack powered by Snowflake, organizations can eliminate inefficiencies, reduce friction in the buyer journey, and make smarter decisions with intelligent, context-aware search. AI can’t just optimize search; it can transform how companies interact with their customers and manage their product data. As product catalogs grow and customer expectations evolve, scaling AI-driven search and discovery will be key to staying ahead of the competition.
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