Trade Cockpit
Internal product design, vehicle trade management
The full case study: from spreadsheets and Slack to a structured trade lifecycle.
Trade Cockpit
From spreadsheets and Slack to a structured trade lifecycle.
Gowago manages vehicles returned after leasing contracts end or are terminated early. Each return starts a trade: vehicle intake, inspection, repair, resale and financial settlement, across four teams.
The problem
- 01
No single source of truth
Trade data was scattered across HubSpot, Google Sheets, Slack and email threads. There was no single place to see the full status of a trade.
- 02
Every handoff was a Slack message
Approvals for repairs, invoicing and pricing were informal, error-prone and untraceable. Remarketing, Finance and Logistics coordinated through constant manual messages.
- 03
Profit visible only at closing
Without structured financial tracking, standing days, break-even prices and margins showed up at the very end of a trade, too late to act on.
My role
- Role
- UX / Product Designer
- Team
- Two designers, working with Product and Engineering
- My part
- Led UX discovery and workflow mapping, designed the trade lifecycle model, created wireframes and UI, tested concepts with the Remarketing team
- Scale
- 5 trade subtypes, 10 lifecycle stages, 4 teams, 50+ data fields per trade
The research
- 01 / 04
Stakeholder interviews
In-depth interviews with the Remarketing team to understand daily workflows, pain points and tool usage.
- 02 / 04
As-is process mapping
Collaborative workshops to document the exact steps teams follow for each trade type.
- 03 / 04
Data model analysis
An audit of HubSpot properties and spreadsheets to identify the key data points and how they relate across the trade lifecycle.
- 04 / 04
Concept testing
Early wireframes tested with users to validate lifecycle stages, terminology and the layout of the Trade Cockpit.
What it found
- 01
Stage ambiguity
Users were unsure which lifecycle stage a trade was in, because there was no shared definition. That led to duplicated effort and missed handoffs between Remarketing and Finance.
- 02
Tool fragmentation
A single trade meant switching between HubSpot, Sheets, a partner portal and several Slack threads. People spent more time finding information than acting on it.
- 03
No early financial visibility
Break-even and profit were visible only at the very end. Teams needed an estimated profit and loss earlier, to make informed pricing and repair decisions.
- 04
Different trade scenarios
End-of-lease returns involve different entities and workflows than trade-ins. The solution had to support every subtype without becoming overwhelming.
The solution
The Trade Cockpit: one screen to manage the entire trade lifecycle, built on a single trade record that connects the vehicle, the leasing contract, the supplier or buyer, the listing and the transactions. Because the complexity was in the workflow logic rather than the visual layout, I prototyped directly in code to test stage progression, lifecycle logic and data relationships.
The impact
- 01
Centralised trade operations
One source of truth for every trade, replacing fragmented tools with a single lifecycle view across all stages.
- 02
Reduced coordination overhead
Fewer status checks and handoffs across teams. Trade progress is visible at a glance, in real time.
- 03
Earlier financial decisions
Profit and loss visible at every stage, for earlier and more confident pricing and margin decisions.
- 04
A platform for future automation
The structured data model and lifecycle stages lay the groundwork for rule-based automation.
What I learned
- 01
Internal tools require system thinking
The main challenge was structuring data and workflows, not just designing interfaces.
- 02
Validate workflow logic early
Prototyping in code helped validate lifecycle stages before committing to visual design.
- 03
Structured data unlocks automation
A clear data model made it possible to introduce reporting and automation later.