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 microinsurance programme, working in Bihar. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- A microinsurance programme
- Sector
- Insurance
- Team engaged
- IT & Development
- Services
- Offline data collection (CAPI), Government research
Client challenge
Enrolment in a crop and life microinsurance scheme was low.
Research objectives
- Deliver on the brief: insurance awareness among rural households.
- Design around the real needs of households.
- 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 2,500 households.
- households2,500
Audiences we typically reach in insurance
Policy buyers and holdersClaimantsAgents and advisersProspectsScheme beneficiaries
Geographic coverage
Coverage: Bihar. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Household survey
- 3Insight
Interviewers surveyed households about awareness, understanding and trust in insurance.
Offline data collection (CAPI)Government research
Sampling strategy
- households
- 2,500
- 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
- Many households did not understand how claims worked.
Real claim stories build trust.
Recommendations
- Build on the core insight: real claim stories build trust.
- Focus effort where the evidence was strongest: many households did not understand how claims worked.
- Re-measure after the changes to confirm the effect.
Business impact
- The programme added claim stories from local beneficiaries.
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
- Evaluation findings with programme recommendations
- 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: how do people choose a policy?