Supporting Share of Voice Analysis Through Granular Advertising Intelligence

A digital strategy firm initiated a focused evaluation of advertising dynamics across the automotive ecosystem, not just to track spend patterns, but to decode brand-level visibility across multiple channels and geographies. The objective was to move from aggregated data views toward granular intelligence that could support accurate Share of Voice analysis.

Challenge Landscape

The client was navigating several layers of complexity:

• Lack of brand-level advertising spend visibility
• Fragmented data across channels and geographies
• Limited clarity on platform-level performance insights
• Tight timelines requiring rapid data delivery and validation

DMA Intelligence Approach

DMA Intelligence structured a high-granularity data framework, integrating channel-level breakdowns, platform segmentation, and regional mapping to deliver a cohesive dataset aligned with strategic requirements.

Key Intelligence Delivered

• Brand-level visibility across multiple advertising channels
• Structured breakdown of platform-specific spend patterns
• Unified dataset enabling accurate Share of Voice modeling

Business Impact

• Enabled transition from aggregated insights to precise SOV analysis
• Supported faster and more confident strategic decisions
• Strengthened internal alignment across digital strategy teams