The BEATS Framework: A Smarter Approach to Marketing Analytics

by Ben Dutter •

Marketing measurement can feel overwhelming. With endless data sources, conflicting reports, and evolving tracking methods, it’s easy to get lost. But here’s the truth: the confusion isn’t inherent, it’s driven by unnecessary complexity.

Effective measurement boils down to two fundamental principles:

  1. Knowing when to use what (i.e., choosing the right measurement method for the right scenario)
  2. Understanding what to trust more (i.e., prioritizing certain data sources over others when they conflict)

By following these principles, you can avoid common measurement mistakes and make more confident, data-driven decisions. Let’s break it down.

When to Use What: Choosing the Right Measurement Method

Not all marketing metrics serve the same purpose. The type of measurement you use should align with both your business needs and your company’s stage of growth.

Here are the primary categories of measurement:

  • Overall blended metrics (revenue, customer acquisition cost)
  • Tests & experiments (pre/post-tests, control groups)
  • Math & trends (media mix modeling (MMM), run rates)
  • Tech-based tracking (attribution, UTM tracking, pixels)
  • Customer surveys (“How did you hear about us?”, brand lift studies)

How should you apply them?

For startups & smaller brands: Stick to blended metrics and occasional tests or surveys. No need for complex models.

For larger brands ($100M+): Focus on incrementality testing and comprehensive modeling to optimize budget decisions.

For different decision types:

  • Big budget decisions? Use blended metrics and long-term media mix modeling.
  • Want a quick cause-and-effect gut check? Use incrementality experiments.
  • Testing ad creatives (e.g., Red vs. Blue ads)? Rely on in-platform attribution—but never for budget allocation.

Key takeaway: The bigger the decision, the closer the measurement should be to the P&L. Tactical, granular decisions can rely on tech-based tracking, but strategic decisions require broader, more robust data.

What to Trust More: The BEATS Framework

With so many measurement methods available, conflicting data is inevitable. So, what should you trust when different sources tell different stories?

Introducing BEATS, a simple prioritization framework:

B.E.A.T.S. = Business > Experiments > Analyses > Tracking > Surveys

When two measurement types contradict each other, always default to the higher-authority method:

  1. Business (P&L, revenue trends) → Most reliable source. If revenue is declining despite good attribution results, something is off.
  2. Experiments (incrementality tests, control groups) → When done correctly, these provide the most scientifically rigorous insights.
  3. Analyses (MMM, run rates, statistical models) → Strong, but needs to be validated by experiments or business trends.
  4. Tracking (UTMs, platform attribution, pixels) → Helpful for directional insights but should never be used for major budget decisions.
  5. Surveys (brand lift, “How did you hear about us?”) → Useful for brand perception but should not override financial or experimental data.

How This Works in Practice:

Example 1: Your MMM says Meta ads aren’t incremental, but an incrementality experiment says they are.

 

  • Trust the experiment over the model and adjust accordingly.

 

Example 2: Your incrementality test says your media strategy is strong, but revenue is declining and MER is worsening.

 

  • Trust the business metrics—something is off, and the experiment might be missing the full picture.

Example 3: Your platform attribution says retargeting has a high ROAS, but MMM suggests it’s unprofitable.

 

  • Trust the MMM, apply in-platform data corrections, and adjust budgets accordingly.

Key takeaway: Science beats math. Math beats technology. Prioritize methodologies that align with long-term, high-confidence decision-making.

Conclusion: Avoiding Analysis Paralysis

Marketing teams often fall into analysis paralysis, faced with too many conflicting metrics, they default to what’s easiest or most familiar.

The solution? Use a priority framework to guide decision-making:


 ✔ Choose the right measurement method for the decision at hand.
 ✔ Trust the higher-authority measurement source when conflicts arise.
 ✔ Align your measurement approach with your company’s size and maturity.

By applying these principles, you can eliminate confusion, make data-driven marketing decisions with confidence, and maximize your ROI.

Want to make smarter marketing decisions?

At fusepoint, we help brands implement structured, reliable measurement strategies that drive real business impact. Need help simplifying your marketing measurement? Get in touch with us today.