Illustrative engagement. A representative scenario showing how we approach this kind of work in insurance. It does not describe a specific client; outcomes are described qualitatively.
Industry overview
What is shaping insurance
Insurance is bought for peace of mind and judged at the moment of a claim. Agents, digital journeys and claims experience shape trust.
- Many buyers do not fully understand their policies
- Claims experience defines the brand
- Agents remain important alongside digital
Client background
A motor insurer, 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 motor insurer
- Sector
- Insurance
- Team engaged
- Market Research
- Services
- Quantitative research, Online surveys
Client challenge
The insurer considered a usage-based policy but was unsure about demand.
Research objectives
- Deliver on the brief: testing a pay-as-you-drive motor policy.
- Understand the views, needs and behaviour of car owners.
- 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 1,500 car owners.
- car owners1,500
Audiences we typically reach in insurance
Policy buyers and holdersClaimantsAgents and advisersProspectsScheme beneficiaries
Geographic coverage
Coverage: India. Market: India.
- India
Research methodology
A structured quantitative design, so results could be compared across groups, locations and time.
- 1Brief
- 2Concept test
- 3Price sensitivity
- 4Insight
We tested the concept with car owners, measuring appeal, privacy concerns and price expectations.
Quantitative researchOnline surveys
Sampling strategy
- car owners
- 1,500
- Business respondents were screened on role, company size and involvement in the decision before being invited.
- 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
- Price and trade-off analysis
- Comparison of concepts, messages or products against each other
Key findings
- Low-mileage urban drivers showed strong interest if data privacy was clear.
Usage-based products need clear privacy promises.
Recommendations
- Build on the core insight: usage-based products need clear privacy promises.
- Focus effort where the evidence was strongest: low-mileage urban drivers showed strong interest if data privacy was clear.
- Re-measure after the changes to confirm the effect.
Business impact
- The product was launched for that segment.
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: how do people choose a policy?