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 mobility start-up, 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 mobility start-up
- Sector
- Transport & logistics
- Team engaged
- Market Research
- Services
- Consumer panel, Online surveys
Client challenge
The start-up wanted to differentiate.
Research objectives
- Deliver on the brief: research for a ride-hailing app.
- Understand the views, needs and behaviour of riders.
- Produce results that can be compared across groups and locations.
- Give the client evidence it can act on within 4 weeks.
Target audience
The work focused on 2,000 riders.
- riders2,000
Audiences we typically reach in transport & logistics
Passengers and commutersDriversShippers and logistics buyersFleet ownersWarehouse staff
Geographic coverage
Coverage: India. Market: India.
- India
Research methodology
A structured quantitative design, so results could be compared across groups, locations and time.
- 1Brief
- 2Rider survey
- 3Insight
We surveyed riders about safety, pricing and reliability.
Consumer panelOnline surveys
Sampling strategy
- riders
- 2,000
- Online respondents were drawn from verified panel members who matched the screening criteria, with quotas on key profile attributes.
Data collection
- The online survey was mobile-friendly and offered in respondents' languages.
Data quality & validation
- Digital fingerprinting and duplicate checks
- Attention, speed and straight-lining checks
- Review of open-ended answers
- Logic and range checks on the final data
- Work carried out under our ISO 20252 quality procedures
Data analysis
- Cross-tabulation by key groups and locations
- Significance testing on the differences that mattered
Key findings
- Safety features mattered to women riders.
Safety is a feature.
Recommendations
- Build on the core insight: safety is a feature.
- Focus effort where the evidence was strongest: safety features mattered to women riders.
- Re-measure after the changes to confirm the effect.
Business impact
- The app added safety tools.
What changed in the business as a result of the work. Impact is described qualitatively.
Deliverables & next steps
- Cleaned data file and data tables
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: why do drivers stay or leave?