annie gordon

Reibus Logistics Quote Manager

Reibus streamlined freight quoting by replacing fragmented workflows with the Logistics Quote Manager, reducing quote time from 15–20 minutes to under 5. Consolidating internal lane history and external benchmarks, the tool empowers Logistics Account Managers to respond faster, quote more accurately, and win more high‑value business.

Company

Reibus International

Industry

Logistics

Role

Director of Product Design

Who We Designed For

Job To Be Done

Logistics Account Manager (LAM), Freight Ops Rep

“When I receive a quote request, I want to quickly find past rates and select the right equipment, so I can respond fast and accurately without asking around or guessing.”

LAMs are under pressure to respond to quotes quickly—often with incomplete data, switching between tools like Greenscreens, DAT, Slack, and spreadsheets to get the job done.

User Journey Map

Primary Persona: Rob, Logistics Account Manager

Scenario: Responding to an inbound freight quote request

Goal: Provide an accurate, competitive quote faster than the competition

Logistics Account Manager (LAM), Freight Ops Rep

“When I receive a quote request, I want to quickly find past rates and select the right equipment, so I can respond fast and accurately without asking around or guessing.”

LAMs are under pressure to respond to quotes quickly—often with incomplete data, switching between tools like Greenscreens, DAT, Slack, and spreadsheets to get the job done.

User Journey Map

Primary Persona: Rob, Logistics Account Manager

Scenario: Responding to an inbound freight quote request

Goal: Provide an accurate, competitive quote faster than the competition

Logistics Account Manager (LAM), Freight Ops Rep

“When I receive a quote request, I want to quickly find past rates and select the right equipment, so I can respond fast and accurately without asking around or guessing.”

LAMs are under pressure to respond to quotes quickly—often with incomplete data, switching between tools like Greenscreens, DAT, Slack, and spreadsheets to get the job done.

User Journey Map

Primary Persona: Rob, Logistics Account Manager

Scenario: Responding to an inbound freight quote request

Goal: Provide an accurate, competitive quote faster than the competition

Logistics Account Manager (LAM), Freight Ops Rep

“When I receive a quote request, I want to quickly find past rates and select the right equipment, so I can respond fast and accurately without asking around or guessing.”

LAMs are under pressure to respond to quotes quickly—often with incomplete data, switching between tools like Greenscreens, DAT, Slack, and spreadsheets to get the job done.

User Journey Map

Primary Persona: Rob, Logistics Account Manager

Scenario: Responding to an inbound freight quote request

Goal: Provide an accurate, competitive quote faster than the competition

The Challenge

Manual Quoting Processes Led to Delays, Inconsistency, and Lost Opportunities

Before the redesign, quoting a lane required LAMs to:

Search across DAT, Slack, spreadsheets, and Greenscreens
Ask teammates for past quotes or gut-check rates
Manually factor in special equipment needs or fallback logic
Track quote behavior and outcomes separately (or not at all)

Quote response times could stretch to 15–20 minutes, with high variability in pricing accuracy, especially for complex loads or specialty equipment.

“You had to know who to ask or remember what you did last time—we didn’t have a centralized way to reference anything.”

– Logistics Account Lead

Manual Quoting Processes Led to Delays, Inconsistency, and Lost Opportunities

Before the redesign, quoting a lane required LAMs to:

Search across DAT, Slack, spreadsheets, and Greenscreens
Ask teammates for past quotes or gut-check rates
Manually factor in special equipment needs or fallback logic
Track quote behavior and outcomes separately (or not at all)

Quote response times could stretch to 15–20 minutes, with high variability in pricing accuracy, especially for complex loads or specialty equipment.

“You had to know who to ask or remember what you did last time—we didn’t have a centralized way to reference anything.”

– Logistics Account Lead

Manual Quoting Processes Led to Delays, Inconsistency, and Lost Opportunities

Before the redesign, quoting a lane required LAMs to:

Search across DAT, Slack, spreadsheets, and Greenscreens
Ask teammates for past quotes or gut-check rates
Manually factor in special equipment needs or fallback logic
Track quote behavior and outcomes separately (or not at all)

Quote response times could stretch to 15–20 minutes, with high variability in pricing accuracy, especially for complex loads or specialty equipment.

“You had to know who to ask or remember what you did last time—we didn’t have a centralized way to reference anything.”

– Logistics Account Lead

Manual Quoting Processes Led to Delays, Inconsistency, and Lost Opportunities

Before the redesign, quoting a lane required LAMs to:

Search across DAT, Slack, spreadsheets, and Greenscreens
Ask teammates for past quotes or gut-check rates
Manually factor in special equipment needs or fallback logic
Track quote behavior and outcomes separately (or not at all)

Quote response times could stretch to 15–20 minutes, with high variability in pricing accuracy, especially for complex loads or specialty equipment.

“You had to know who to ask or remember what you did last time—we didn’t have a centralized way to reference anything.”

– Logistics Account Lead

The Solution

A Purpose-Built Quoting Tool with Lane Intelligence and Smart Defaults

We created the Logistics Quote Manager: a centralized quoting interface that guides LAMs through the process with embedded intelligence, historical data, and built-in logic for equipment fallbacks.

Key Features:

Lane History Access: 15-day, 30-day, and last-run pricing
Smart Equipment Logic: Automatically checks Conestoga fallbacks or hotshot availability
Quote Submission & Tracking: Ties each quote to behavior and outcome data
Inline Feedback: Ensures reps know what’s needed and when
Reduced Tool Switching: Everything needed to quote lives in one place

Design focused on clarity, speed, and confidence—empowering reps to quote without hesitation.

A Purpose-Built Quoting Tool with Lane Intelligence and Smart Defaults

Key Features:

Lane History Access: 15-day, 30-day, and last-run pricing
Smart Equipment Logic: Automatically checks Conestoga fallbacks or hotshot availability
Quote Submission & Tracking: Ties each quote to behavior and outcome data
Inline Feedback: Ensures reps know what’s needed and when
Reduced Tool Switching: Everything needed to quote lives in one place

Design focused on clarity, speed, and confidence—empowering reps to quote without hesitation.

A Purpose-Built Quoting Tool with Lane Intelligence and Smart Defaults

Key Features:

Lane History Access: 15-day, 30-day, and last-run pricing
Smart Equipment Logic: Automatically checks Conestoga fallbacks or hotshot availability
Quote Submission & Tracking: Ties each quote to behavior and outcome data
Inline Feedback: Ensures reps know what’s needed and when
Reduced Tool Switching: Everything needed to quote lives in one place

Design focused on clarity, speed, and confidence—empowering reps to quote without hesitation.

A Purpose-Built Quoting Tool with Lane Intelligence and Smart Defaults

Key Features:

Lane History Access: 15-day, 30-day, and last-run pricing
Smart Equipment Logic: Automatically checks Conestoga fallbacks or hotshot availability
Quote Submission & Tracking: Ties each quote to behavior and outcome data
Inline Feedback: Ensures reps know what’s needed and when
Reduced Tool Switching: Everything needed to quote lives in one place

Design focused on clarity, speed, and confidence—empowering reps to quote without hesitation.

UX Enhancements

Before vs. After

Before	After
No quick reference for previous quotes; unclear pricing trends	Centralized form for quote entry with inline historical data
Context switching between multiple platforms; tribal knowledge; risk of missing data	Single screen displays internal lane history and external benchmarks
No visibility into prior run data; error-prone rate guessing; no conestoga logic	Automated suggestions based on recent rates, equipment variants, and market signals
Lack of confidence in pricing; fear of losing margin or opportunity	Inline logic validation, historical context, and tighter rate confidence ranges
No feedback loop; no quote version history or lane tracking	Quote behavior is logged by lane; insights inform future pricing strategies

Business Impact

Results at a Glance:

Quote times reduced from 15–20 mins → under 5 minutes
Lower error rates in equipment selection and fallback logic
Increased quote volume and faster rep ramp-up
Less tribal knowledge reliance, more standardized process

“This cut the back-and-forth down dramatically. Our reps finally had a tool that guided them instead of slowing them down.” — Head of Logistics Operations

My Role

Product Design Lead

UX Strategy · Workflow Design · Logic Mapping

My contributions:

Led discovery with LAMs, logistics ops, and product leadership
Mapped quote flows and pain points through workshops and interviews
Designed wireframes and prototypes for logic-based quoting
Created the fallback equipment flow and lane data entry points
Supported rollout and feedback loop with pilot team

Tools: Figma · Miro · Slack · Sheets · Internal Data Dashboards

Reflection

Designing for logistics reps required empathy for their pace, context-switching, and the pressure to quote confidently with imperfect data. Our goal was to give them a tool that felt as fast and responsive as their own thinking—without the cognitive load.

If I revisited today, I’d explore:

Tracking quote-to-order conversion rates directly in the tool
Proactive flagging for stale pricing or underquoted lanes
Integration with carrier preferences or scorecards for higher win rates