AI/ML

Intelligent Search with NLP

Improving search success rate from 45% to 78% using semantic understanding

NLPSearchSemantic SearchML

5 months (concept to launch)

Team of 8

Product Manager

🎯The Problem

Traditional keyword-based search was failing users. Analysis showed: - 55% of searches returned zero results (mostly due to typos, synonyms, or natural language queries) - Average time to find desired item: 4.2 minutes - 30% of support tickets were search-related - Users expressed frustration with exact-match requirements

🔍My Approach

Developed a multi-phase strategy to transform search: 1. **Research Phase** - Analyzed 1M+ search queries to identify failure patterns - Interviewed 40 users about search behavior - Benchmarked against industry leaders (Amazon, Google) 2. **Technical Evaluation** - Evaluated Elasticsearch vs vector search (FAISS, Pinecone) - Tested pre-trained models (BERT, sentence-transformers) - Calculated infrastructure costs at scale 3. **ML Strategy** - Semantic search for understanding intent - Query expansion for typos and synonyms - Personalized ranking based on user context - Autocomplete with learning from popular queries

💡The Solution

Built a three-tier intelligent search system: **Tier 1: Query Understanding** - NLP-based spell correction and synonym expansion - Intent classification (product search, support question, etc.) - Query reformulation suggestions **Tier 2: Semantic Retrieval** - Embedded catalog items using sentence-transformers - Vector similarity search for semantic matching - Hybrid scoring combining keyword + semantic relevance **Tier 3: Personalized Ranking** - ML ranker considering user history, preferences, popularity - Context-aware results (location, time, device) - A/B tested ranking algorithms

Technologies Used

ElasticsearchBERTsentence-transformersPythonFastAPIRedisPostgreSQL

📈Impact & Results

+73%

Search Success Rate

From 45% to 78% of searches resulted in user action

-82%

Zero Results

Reduced from 55% to 10% of searches

-57%

Time to Find

Average search time dropped from 4.2 to 1.8 minutes

-65%

Support Tickets

Search-related support requests decreased significantly

+$8M

Revenue

Annual impact from improved search conversions

💭Key Learnings

•Vector search added cost but value justified it - key was hybrid approach (keyword + semantic)
•Model fine-tuning on domain data (2 weeks effort) improved relevance by 25%
•Autocomplete ROI was highest - quick win before full semantic search
•User feedback loop critical - added "Was this helpful?" to gather training data
•Search analytics became product gold mine - informed roadmap priorities

Want to Learn More?

I'd be happy to discuss this project in more detail, share additional insights, or answer any questions.

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