The news: US marketing decision-makers are wasting money on marketing investments that can’t produce measurable results.

• Although 9 in 10 say marketing drives growth, just half measure what leads to better outcomes, per Haus’ 2026 Marketing Decision Confidence Index. The other half only measures what’s easy, expected, or visible.
• 78% of decision-makers believe at least 10% of spend is wasted due to insufficient measurement, including 7% who say that number is 30% or more.

That uncertainty is leading marketers to boost spend on reliable channels. Brands expect to significantly increase their investments on Google Search, TikTok, YouTube, and Meta.

The challenge: Marketers lack confidence in the tools they do have to measure results.

• About one-third of marketing leaders are concerned about reliability (34%), conflicting data (33%), and inconsistent measurement across channels (30%).
• One-quarter distrust measurement over a lack of transparency (26%), too many metrics (25%), and lack of access to data (24%).

Without reliable tools and data to gauge a project’s results accurately, brands will continue to throw money away.

Zooming in: Marketing measurement solutions are fragmented. While one method stands above the rest—60% of decision-makers trust incrementality testing the most—other preferences are more distributed.

• 40% say media mix modeling (MMM) is their most trusted measurement solution.
• 37% prefer either in-platform reporting or multi-touch attribution (MTA).
• 31% trust first- and/or last-touch attribution, and 29% cite post-purchase surveys.

The lack of a standard measurement solution across firms further fragments data access, leading to a lack of trust in metrics.

Recommendations for marketers: Accurate measurement has consequences beyond current campaigns. Without clear insights, marketers are throwing money at a mediocre solution and hoping for better results. Instead, they need to:

• Partner with a third party to ensure results are unbiased and error-free.
• Collect good, reliable data. To speed up processes, deploy AI to clean data and eliminate duplications and inaccuracies.
• Measure performance often to ensure accurate results.