Illustrative engagement. A representative scenario showing how we approach this kind of work in entertainment. It does not describe a specific client; outcomes are described qualitatively.
Industry overview
What is shaping entertainment
Live events, theme parks and entertainment brands sell experiences. Ticketing, crowd experience and sponsor value decide whether people come back.
- Ticket pricing and booking ease affect attendance
- On-site experience drives repeat visits
- Sponsors expect audience evidence
Client background
A theme and water park operator, working in Western India. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- A theme and water park operator
- Sector
- Entertainment
- Team engaged
- IT & Development
- Services
- Quantitative research, Offline data collection (CAPI), Data analytics & dashboards
Client challenge
Visitor numbers were good, but online reviews often mentioned long queues and poor value.
Research objectives
- Deliver on the brief: mapping the guest journey at a theme park.
- Design around the real needs of guests.
- 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 2,000 guests.
- guests2,000
Audiences we typically reach in entertainment
Event-goersTheme-park visitorsTicket buyersSponsorsFamilies
Geographic coverage
Coverage: Western India. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Exit survey
- 3Queue observation
- 4Insight
We interviewed guests at exits about each stage of their visit and observed queue times at the busiest rides through the day.
Quantitative researchOffline data collection (CAPI)Data analytics & dashboards
Sampling strategy
- guests
- 2,000
- 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
- Food queues, not ride queues, drove most dissatisfaction.
Guests judge the whole day, not just the rides.
Recommendations
- Build on the core insight: guests judge the whole day, not just the rides.
- Focus effort where the evidence was strongest: food queues, not ride queues, drove most dissatisfaction.
- Re-measure after the changes to confirm the effect.
Business impact
- The park added food counters and mobile ordering.
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
- Interactive dashboard
- 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: what value did sponsors get?