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
A digital media agency, working in India & Singapore. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- A digital media agency
- Sector
- Advertising
- Team engaged
- IT & Development
- Services
- Data analytics & dashboards, API development & integration
Client challenge
Account teams spent two days each month pulling numbers from ad platforms into spreadsheets for client reports.
Research objectives
- Deliver on the brief: one dashboard for every programmatic campaign.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
Scope: Eight ad platforms.
Audiences we typically reach in advertising
Target audiencesBrand managersMedia plannersViewers and readersAgency teams
Geographic coverage
Coverage: India & Singapore. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Platform data connectors
- 3Client dashboards
- 4Insight
We connected the agency's ad platforms through their APIs into one data store and built client-facing dashboards with shared definitions for reach, cost and conversions.
Data analytics & dashboardsAPI development & integration
Sampling strategy
- 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
- Shared definitions matter more than prettier charts.
Recommendations
- Build on the core insight: shared definitions matter more than prettier charts.
- Track adoption and support tickets, and plan the next release from real usage.
Business impact
- Reports became available on demand, and account teams moved the time saved into optimisation work.
Impact is described qualitatively.
Deliverables & next steps
- 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?