Case study · logistics
Logistics reimagined for the mobile era
We engineered a high-performance website that turned a clunky, desktop-only site into a mobile-first lead-generation engine.
Client
Regional logistics firm (anonymised)
Sector
Logistics and transport
Services deployed
UX strategy, web design, Kadence development
Objective
Increase mobile quote requests
- Published with consent under the Vault Protocol
- 5.0 on Google (opens in a new tab)
01 · The challenge
Digital friction
The client, a mid-sized regional logistics firm, was facing a critical bottleneck. Their analytics revealed that 60 % of their traffic came from mobile devices — specifically, drivers and dispatchers on the road needing quick quotes.
However, their existing eight-year-old website was non-responsive, requiring “pinch-and-zoom” navigation. The “Request a quote” form was buried three clicks deep and asked for 15 unnecessary fields. The result was an 85 % bounce rate on mobile and lost revenue to competitors with easier interfaces.
02 · The strategic solution
Re-engineering the intake, not just the design
We didn’t just redesign the site; we re-engineered their digital intake process. Using the WordPress and Kadence framework for performance and stability, we executed a mobile-first strategy.
Thumb-zone architecture
We designed the mobile interface with the critical calls to action within easy reach of a user’s thumb.
Funnel optimisation
We replaced the 15-field form with a multi-step, conditional-logic form. Users now see only the relevant questions, reducing cognitive load and friction.
Performance engineering
We stripped WordPress bloat, optimised assets and used high-performance hosting to reach sub-one-second load times on 4G networks.
The measurable outcomes
Deployed in under six weeks
The results were immediate and quantifiable, validating the user-centric approach.
+40 %
Quote request volume
98/100
Google mobile speed score
−60 %
Mobile bounce rate
The evidence
What this engagement did and did not measure
The figures above are the outcomes that were tracked at the time. This work predates AI-visibility measurement, so there is no AI citation baseline for it — and we are not going to invent one retrospectively.
Every engagement now starts with a multi-engine audit that records whether the AI assistants name the business when a buyer asks without using its name, and every report carries a Scan ID so any figure can be traced to the run that produced it.
We publish our own audit on the same terms, including the parts that do not flatter us. Read our own report (PDF, opens in a new tab).
Last updated 29 September 2026 · Daryo89 Ltd, Cambridge
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