Unlock $30B in Marketing Value with AI and Better Measurement
▼ Summary
– Most marketers report that their current measurement approaches are inadequate, failing to provide sufficient coverage, consistency, timeliness, and trust across all media channels.
– Measurement bias often dictates strategy, leading to underinvestment in harder-to-measure channels like CTV or podcasts and overinvestment in easily tracked lower-funnel activities.
– AI has the potential to unlock tens of billions in value for marketing measurement, but it requires clean, standardized data to function effectively, which most organizations lack.
– Operational infrastructure is a major bottleneck, as inconsistent data quality, manual workflows, and team silos prevent effective measurement and will be exposed by AI.
– Fixing measurement requires a structural shift, including automated workflows, standardized data, team alignment on KPIs, and using insights for optimization, not just validation.
Marketing teams today face a relentless challenge: connecting the dots across a fragmented landscape of online and offline channels. Hours are spent wrangling data, while reliance on simplistic last-touch attribution or opaque marketing mix models leaves leaders questioning their investments. Are budgets flowing to the channels that truly drive growth, or simply to those that are easiest to measure? This measurement bias isn’t just an analytical headache; it’s a strategic straitjacket that dictates where money goes, often undervaluing crucial mid-funnel efforts like brand campaigns or podcast sponsorships.
A recent industry report reveals a stark reality: the majority of marketers admit their measurement approaches lack coverage, consistency, timeliness, and trust. When a channel like connected TV, retail media, or audio is difficult to track, it frequently gets less investment or is skipped altogether. This isn’t smart allocation; it’s allowing flawed visibility to steer the strategy. The core issue is that many models confuse correlation with causation. Just because a channel is present at the moment of conversion doesn’t mean it caused the sale. Without proper incrementality testing, teams risk optimizing for coincidence rather than genuine contribution.
This is where artificial intelligence promises a revolution, with the potential to unlock tens of billions in media value and productivity gains. The critical catch, however, is that AI’s power is entirely dependent on the quality of its fuel: clean, standardized data. Most organizations struggle with inconsistent taxonomies and disparate data definitions across platforms, making it impossible to reliably connect customer exposure to business outcomes. While AI can automate data preparation and model tuning, implementing it on a broken foundation only automates existing problems at a greater scale.
Initiatives are emerging to build the necessary infrastructure, focusing on creating standardized taxonomies and a unified framework that links exposure and behavior to outcomes. The goal is to provide the coherent foundation AI requires to function effectively. The potential payoff is substantial, enabling budget shifts to previously undervalued channels and freeing nearly ten percent of a team’s time from manual data prep for strategic work.
The primary bottleneck is not a lack of advanced technology, but operational infrastructure. Inconsistent data quality, manual workflows, and siloed teams create friction. If the underlying infrastructure is flawed, AI will magnify those flaws, raising concerns about legal risk, model accuracy, and ultimately, trust. Industry contracts are increasingly including clauses for AI transparency and accountability, meaning teams will soon need to prove their models meet rigorous new standards.
Addressing this requires a structural shift, not just a new software purchase. It demands collaboration across planning, analytics, data, legal, and operations. Key steps include building automated, repeatable workflows to increase measurement frequency, fixing core data quality issues to ensure consistent inputs, and aligning teams around shared key performance indicators instead of disconnected dashboards. Measurement must evolve from a tool for historical validation into a live system for continuous optimization, informing future plans rather than just reporting on the past.
While these needs are not new, the rise of AI makes ignoring them untenable. The technology and the frameworks are being developed. To capture the immense opportunity and redirect billions in investment wisely, the industry needs a collective commitment to modernize its foundations and push platform partners toward universal standards. The path forward is clear: stop patching outdated systems and start rebuilding the core infrastructure to empower truly intelligent marketing.
(Source: MarTech)
