Illustrative engagement. A representative scenario showing how we approach this kind of work in advertising. It does not describe a specific client; outcomes are described qualitatively.
Industry overview
What is shaping advertising
Agencies and advertisers need to know whether campaigns will work before launch and whether they worked afterwards, across TV, digital and outdoor.
- Pre-testing reduces the risk of costly campaigns
- Brand lift is measured across channels
- Audiences are fragmented across platforms
Client background
An outdoor media owner, working in Mumbai and Pune. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- An outdoor media owner
- Sector
- Advertising
- Team engaged
- IT & Development
- Services
- Offline data collection (CAPI), Quantitative research, Data analytics & dashboards
Client challenge
Advertisers asked for audience profiles of each hoarding, but the media owner only had traffic counts.
Research objectives
- Deliver on the brief: understanding who sees outdoor sites on a city corridor.
- Design around the real needs of commuters.
- 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 2,400 commuters.
- commuters2,400
Audiences we typically reach in advertising
Target audiencesBrand managersMedia plannersViewers and readersAgency teams
Geographic coverage
Coverage: Mumbai and Pune. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Street intercepts
- 3Commute diaries
- 4Insight
Interviewers spoke with commuters at stations and junctions along the corridor, recording routes, travel times and demographics, supported by short commute diaries.
Offline data collection (CAPI)Quantitative researchData analytics & dashboards
Sampling strategy
- commuters
- 2,400
- 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
- Profiles showed clear differences between morning and evening audiences on the same sites.
Context sells outdoor media better than counts alone.
Recommendations
- Build on the core insight: context sells outdoor media better than counts alone.
- Focus effort where the evidence was strongest: profiles showed clear differences between morning and evening audiences on the same sites.
- Re-measure after the changes to confirm the effect.
Business impact
- The sales team began selling day-parts with audience descriptions instead of flat site rates.
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: will this ad work?