Illustrative engagement. A representative scenario showing how we approach this kind of work in gaming & betting. It does not describe a specific client; outcomes are described qualitatively.
Industry overview
What is shaping gaming & betting
Our work in gaming and betting supports regulators, operators and researchers with responsible-gaming, awareness and player-protection studies, under strict ethical rules.
- Regulation is evolving quickly
- Player-protection tools need evidence of use
- Public attitudes inform policy
Client background
A state lottery operator, working in Kerala. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- A state lottery operator
- Sector
- Gaming & betting
- Team engaged
- Market Research
- Services
- Quantitative research, Offline data collection (CAPI)
Client challenge
The operator wanted to understand perceptions of fairness and the social causes funded by the lottery.
Research objectives
- Deliver on the brief: brand perceptions of a state lottery.
- Produce results that can be compared across groups and locations.
- Give the client evidence it can act on within 5 weeks.
Target audience
Scope: 1,500 adults.
Audiences we typically reach in gaming & betting
Adult playersThe general publicRegulatorsOperators' compliance teamsSupport organisations
Geographic coverage
Coverage: Kerala. Market: India.
- India
Research methodology
A structured quantitative design, so results could be compared across groups, locations and time.
- 1Brief
- 2Household survey
- 3Insight
Interviewers surveyed adults about trust, awareness of funded causes and participation.
Quantitative researchOffline data collection (CAPI)
Sampling strategy
- Respondents were selected across Kerala using quotas set in advance for each location and respondent type, so no single area or group dominated.
Data collection
- adults
- 1,500
- 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
- Few people knew which causes the lottery funded.
Show the good that revenue does.
Recommendations
- Build on the core insight: show the good that revenue does.
- Focus effort where the evidence was strongest: few people knew which causes the lottery funded.
- Re-measure after the changes to confirm the effect.
Business impact
- The operator began communicating funded projects.
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: what are public attitudes?