Illustrative engagement. A representative scenario showing how we approach this kind of work in transport & logistics. It does not describe a specific client; outcomes are described qualitatively.
Industry overview
What is shaping transport & logistics
Transport, logistics and mobility businesses serve passengers, shippers and fleet owners, and depend on drivers. Reliability, safety and cost decide loyalty.
- Passengers expect real-time information and safety
- Driver retention is a constant challenge
- Shippers value reliability over lowest price
Client background
A logistics company, working in India. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- A logistics company
- Sector
- Transport & logistics
- Team engaged
- IT & Development
- Services
- Offline data collection (CAPI), Qualitative research
Client challenge
Driver turnover was high.
Research objectives
- Deliver on the brief: understanding truck drivers' working conditions.
- Design around the real needs of drivers.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
The work focused on 800 drivers.
- drivers800
Audiences we typically reach in transport & logistics
Passengers and commutersDriversShippers and logistics buyersFleet ownersWarehouse staff
Geographic coverage
Coverage: India. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Driver interviews
- 3Insight
We interviewed drivers at hubs about pay, rest and routes.
Offline data collection (CAPI)Qualitative research
Sampling strategy
- drivers
- 800
- Users and stakeholders from every affected team took part in discovery workshops and acceptance testing.
Data collection
- Requirements were gathered through workshops, process walk-throughs and a review of existing systems and data.
Data quality & validation
- Peer code review on every change
- Automated and manual testing
- User-acceptance testing with the client
- Release checklist and rollback plan
Data analysis
- Usage and performance monitoring after launch
- Defect and support-ticket trends
- Process time before and after
Key findings
- Long waits at loading points caused frustration.
Respect drivers' time.
Recommendations
- Build on the core insight: respect drivers' time.
- Focus effort where the evidence was strongest: long waits at loading points caused frustration.
- Re-measure after the changes to confirm the effect.
Business impact
- The company improved scheduling.
What changed in the business as a result of the work. Impact is described qualitatively.
Deliverables & next steps
- Insight report and management presentation
- Cleaned data file and data tables
- Interview summaries and key verbatims
- Deployed solution with documentation and handover
- Support and maintenance plan
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: what do shippers need?