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 warehousing 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 warehousing company
- Sector
- Transport & logistics
- Team engaged
- Market Research
- Services
- Quantitative research, Offline data collection (CAPI)
Client challenge
Turnover was high.
Research objectives
- Deliver on the brief: understanding warehouse workforce retention.
- Understand the views, needs and behaviour of workers.
- Produce results that can be compared across groups and locations.
- Give the client evidence it can act on within 5 weeks.
Target audience
The work focused on 1,000 workers.
- workers1,000
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
- 2Worker survey
- 3Insight
We surveyed workers about pay, shifts and conditions.
Quantitative researchOffline data collection (CAPI)
Sampling strategy
- workers
- 1,000
- Respondents were selected across India using quotas set in advance for each location and respondent type, so no single area or group dominated.
Data collection
- Trained local-language interviewers conducted interviews in person on tablets (CAPI), with time and location recorded for every interview.
Data quality & validation
- Back-checks on a share of completed interviews
- Time and location checks on field interviews
- Supervisor accompaniment in the field
- 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
- Shift predictability mattered.
Predictability retains workers.
Recommendations
- Build on the core insight: predictability retains workers.
- Focus effort where the evidence was strongest: shift predictability mattered.
- Re-measure after the changes to confirm the effect.
Business impact
- The company fixed shift schedules.
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
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: why do drivers stay or leave?