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Real-World Case Study

See how server-side tracking transformed one company's marketing measurement and ROI.

Company Profile​

Background​

  • Industry: Professional services
  • Size: 25 employees
  • Monthly ad spend: £15,000
  • Platforms: Meta, LinkedIn, Google Ads

Problem​

  • 70% data loss from ad blockers
  • Huge discrepancies between platforms
  • Can't track conversions accurately
  • Wasted ad spend

Implementation​

Timeline​

  • Week 1: Server setup and GTM configuration
  • Week 2: BigQuery setup and integration
  • Week 3: Platform integrations
  • Week 4: Testing and validation
  • Week 5: Launch

Challenges​

  • Initial server configuration issues
  • BigQuery schema design
  • Platform API setup
  • User ID implementation

Solutions​

  • Used Stape for server hosting
  • Iterative BigQuery schema design
  • Platform-by-platform integration
  • Cookie-based user ID

Results​

Data Accuracy​

  • Before: 30% data accuracy (70% loss)
  • After: 100% data accuracy
  • Improvement: 233% increase

Platform Discrepancies​

  • Before: 50-90% discrepancies
  • After: Less than 5% discrepancies
  • Improvement: Consistent data

Ad Performance​

  • Before: ROAS: 2.5x
  • After: ROAS: 3.8x
  • Improvement: 52% increase

Cost Savings​

  • Before: £4,500/month wasted spend
  • After: Optimized spend, better targeting
  • Savings: £2,000-3,000/month

ROI​

  • Investment: £2,000 setup + £600/year hosting
  • Annual Savings: £24,000-36,000
  • ROI: 1,000-1,500%

Lessons Learned​

What Worked​

  • Phased implementation
  • Testing at each step
  • Platform-by-platform approach
  • BigQuery for analysis

What We'd Do Differently​

  • Start with BigQuery setup
  • More thorough testing
  • Better documentation
  • Earlier user ID implementation

Key Takeaways​

  • Server-side tracking is essential
  • BigQuery provides powerful analysis
  • Proper testing is critical
  • ROI is significant

Next Step: Review Next Steps & Resources for your implementation plan and helpful resources.