Echo Global
Logistics
Optimizing Carrier Experience
Through Intelligent Automation
My Role
Led the end-to-end strategic process to improve freight booking and rate negotiation experiences for EchoDrive carrier users—while reducing operational overhead for internal CSRs (Customer Service Representatives).
55% quote-to-shipment
conversion
Bookings completed
in seconds
Increased load pickup
rates by carriers
Intelligent negotiation and load-matching automation
Impact
ML-powered recommendations
(e.g., “Carriers Like You”)
BI dashboards for ongoing optimization and performance tracking
The Problem
Echo’s freight booking process was manual, opaque, and time-consuming:
Carriers had limited visibility into available freight and how competitive their bids were
Manual quoting and CSR dependencies slowed the booking process
Lack of intelligence made it hard to prioritize which loads to pursue
Bookings could take minutes to hours—leading to lost opportunities
"This new process, combined with our advanced load-matching algorithm, allows carriers to now find available freight and book it in an automated manner within seconds—eliminating inefficiencies associated with the time carriers spend gaining access to freight."
— Stakeholder
The Approach
Led UX strategy for EchoDrive’s automated negotiation and booking experience
Reimagined quoting workflows using ML-powered pricing predictions and BI dashboards to drive continuous optimization
Introduced “Carriers Like You” – a feature concept leveraging historical data and machine learning to surface loads based on similar carrier behavior, preferences, and equipment
Created smart counter-offer interactions that allowed negotiation without enabling users to game the system
Embedded continuous user research into sprints to validate assumptions and refine designs
Collaborated across product, engineering, and data science teams using Agile and Azure DevOps
Before vs After
Carriers submitted rates with no immediate feedback
Dependent on CSR intervention for bid responses
Minimal guidance on pricing strategy or relevant loads
Low system intelligence and high operational cost
After:
Automated Negotiation + Booking
Before:
Manual Negotiation Workflow
Real-time feedback on bid competitiveness
Introduced data-informed counter-offers powered by ML
Smart load suggestions via “Carriers Like You”, using behavioral clustering
Bookings in seconds, increasing carrier throughput and satisfaction
Insights captured through BI dashboards to support product and ops decisions
Key Features & Outcomes
User books load via complete booking button
Automated Carrier Negotiations with ML-generated pricing intelligence
BI-powered dashboards tracked load visibility, negotiation trends, and conversion
“Carriers Like You” feature concept used machine learning to recommend relevant loads based on peer carrier behavior
Public Quoting Tool Redesign drove a 55% quote-to-shipment conversion
Faster response times led to more load pickups per carrier—a win for carriers and Echo
Continuous optimization through usage analytics, feedback loops, and iterative design
User books load via complete booking button
User Passes Echo's Second Offer
Impact & Learnings
Merged operational workflows with ML-driven, user-facing automation
Delivered a smarter booking experience that reduced friction and time to revenue
Enabled data-driven design, informed by usage analytics and business KPIs
Designed guardrails to maintain system integrity while allowing dynamic negotiation
Carrier adoption and load conversion improved, thanks to faster speed, relevant suggestions, and transparent feedback
Built foundational elements (e.g., design systems, recommendation logic, and intelligent workflows) to scale future logistics innovations