B2B Marketplace: AI-Powered Recommendation Engine
TradeHub Connect • B2B
Implemented AI recommendation engine that increased average order value by 85% and improved buyer-seller matching.
The Challenge
TradeHub's vast product catalog overwhelmed buyers, making it hard to find relevant products and suppliers.
Our Solution
We built an AI-powered recommendation system that learns from user behavior to suggest relevant products and suppliers.
Technologies Used
Project Objectives
- Increase average order value with highly targeted recommendations
- Reduce time-to-purchase for busy procurement teams
- Arm sellers with actionable signals about buyer intent
How We Approached It
Strategic phases that turned insights into measurable outcomes.
Data Infrastructure Readiness
Ensured clean, reliable data pipelines before modeling.
- Audited catalog taxonomy and buyer behavioral data
- Mapped buyer journeys to identify touchpoints for suggestions
- Defined success metrics with revenue operations and support
Model Design & Experimentation
Tested multiple models to balance accuracy and business rules.
- Segmented buyers by industry, order cadence, and spend
- Compared collaborative filtering with deep learning approaches
- Built a human-in-the-loop review workflow for sensitive categories
Experience Integration
Embedded the recommendations across the marketplace and seller tooling.
- Inserted suggestions into search results and product detail pages
- Created cross-sell panels at checkout and reorder moments
- Delivered seller dashboards summarizing intent and warm leads
Key Features Delivered
Personalized Recommendations
Real-time product suggestions respond to buyer behavior and context.
Seller Intelligence Dashboard
Suppliers get visibility into which buyers are engaging and why.
Smart Search Boosting
AI scoring blends with merchandising rules so promoted products stay discoverable.
Deliverables
- Data governance playbook and ETL pipelines
- Recommendation microservice with monitoring and alerts
- Frontend components for personalized placements
- Enablement materials for sales and support teams
Project Timeline
Milestones that guided delivery from discovery to launch.
Discovery & Data Prep
Weeks 1-4Workshops, data cleansing, instrumentation setup
Model Build
Weeks 5-10Feature engineering, training, AB testing framework
Integration & Enablement
Weeks 11-20Frontend rollout, dashboard build, team training
Project Details
Results & Impact
Average Order Value
Better product recommendations
Match Quality
More successful buyer-seller matches
Time to Purchase
Faster product discovery
Repeat Orders
More customers returning
The AI recommendations have made our massive catalog manageable. Buyers find what they need faster, and sellers get better matches.
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