AI-Based Recommendation Engine for Unit Type Selection
Case Studies
Challenges & Solutions
Technical Environment
Results
Executive Summary
Client
A Major Modular Workspaces Company
Industry
Manufacturing, Engineering, Transport & Logistics
Business Problem
Sales teams relied heavily on manual assessment and experience to recommend suitable unit types. This created slower decisions, inconsistent recommendations, and missed conversion opportunities.
Outcome
An AI/ML-based recommendation engine helped sales teams identify suitable unit types faster and make more consistent, data-driven recommendations.
Challenges
- Customer requirements required manual evaluation.
- Unit selection depended on individual sales experience.
- Multiple unit types had to be considered.
- Delayed recommendations could affect customer satisfaction.
- Quotation-to-order conversion needed improvement.
- Sales teams needed faster, actionable insights.
- Customer requirement mapping needed improvement.
Solutions
- Delivered an AI/ML-based unit recommendation engine.
- Automated recommendations for trailers, containers, flex units, GLO units, and cold storage units.
- Matched unit types to customer-specific requirements.
- Supported faster quotation preparation.
- Reduced dependency on manual judgment.
- Improved consistency across sales teams.
- Enabled faster decision-making.
Technical Environment
- AI/ML-based recommendation engine
- Unit type classification and recommendation logic
- Sales ennoblement and quotation support workflow
- Supported categories: trailers, containers, flex units, GLO units, and cold storage units
Results
- Reduced manual effort in unit selection
- Improved recommendation speed
- Improved customer requirement matching
- Increased consistency across sales teams
- Improved sales productivity
- Faster customer responses
- Better quotation-to-order conversion opportunities
- Supported revenue growth through improved recommendations