Logistics · 2024
Atlas Freight
Instant quotes in a business that quoted in days
Atlas moves freight across Europe and quoted the way the industry always has: an enquiry form, a spreadsheet, three hours of a pricing analyst, an emailed PDF. We turned the pricing model into a public quoting tool and moved the slow part of the sale to the front of the site.
- Quoting tool
- Data visualisation
- Lead capture
- Client
- Atlas Freight
- Sector
- Logistics
- Year
- 2024
- Disciplines
- Development, Growth
Challenge
What was in the way
Pricing logic lived in eighteen linked spreadsheets maintained by two people. A quote took around three hours of analyst time, and roughly a quarter of enquiries went cold before the PDF was sent because a competitor had answered first.
The website itself made no argument. It listed services and a phone number, which meant every enquiry arrived unqualified and the sales team spent its first call establishing facts a form could have collected.
Approach
How we worked
- 01
Model the pricing, then expose it carefully
We worked with the two analysts to turn the spreadsheets into a documented pricing service with explicit rules, surcharges and margin floors. The public tool returns an indicative range rather than a firm number, which protects margin while still answering the question people came to ask.
- 02
A tool that qualifies while it quotes
Route, load, timing and handling requirements are collected in four short steps with sensible defaults. By the time a lead reaches the sales team it carries a complete brief and an indicative price, so the first call starts where the third used to.
- 03
Make the numbers legible
Results are shown as a range broken into transport, handling and surcharges, with a plain-language note on what would move the figure. An internal dashboard shows the analysts which inputs are driving quotes so the model improves monthly rather than annually.
Deliverables
What we handed over
- Pricing service with documented rules and version history
- Public four-step quoting tool with saved quotes
- Internal dashboard for pricing analysts
- CRM integration with qualified lead routing
- Marketing site rebuild around the tool
- Runbook and training for the pricing team
Results
What changed after launch
40s
Median time to quote
Public tool, from first input to indicative range, against roughly three hours before.
+31%
Quote to booking rate
Six months after launch, comparing quotes issued through the tool with the previous process.
18 to 0
Pricing spreadsheets
All eighteen retired into a single versioned service with an audit trail.
“Our first call now starts where the third call used to. That is worth more than the traffic.”
Stack and tools
What it runs on
Build
- Next.js
- TypeScript
- Node API routes
- PostgreSQL
Data
- Versioned pricing rules
- Server-side calculation
- Audit logging
Growth
- CRM integration
- Lead scoring
- Conversion analytics
Timeline
How long it took
- Weeks 1 to 3
Pricing discovery
Spreadsheet archaeology with the analysts, rules documented and validated against past quotes.
- Weeks 4 to 8
Service build
Pricing service, admin tooling and a test suite checked against two years of historical quotes.
- Weeks 9 to 13
Tool and site
Quoting interface, marketing site rebuild, CRM routing and analytics.
- Weeks 14 to 16
Calibration
Soft launch on one lane, margin review, then release across all routes.