Bridging Contradictions in Measurement Data: MTA vs. MMM vs. Incrementality Testing

Over the next few weeks we’re going to go deeper into the role of validation and calibration in marketing measurement. This is the fourth post in a series of posts derived from our recent webinar. Catch the full event on demand here, also read  first, second  and third posts.

Contradictions in data from different measurement methodologies—whether Marketing Mix Modeling (MMM), incrementality testing, or attribution—are not just common but inevitable. Far from being a setback, these discrepancies can be pivotal moments for marketers to uncover hidden insights and refine their strategies.

“Contradictions are inevitable and should be viewed as opportunities,” emphasized Will Burghes, Head of Professional Services at Rockerbox. “They signal where testing should be prioritized to reconcile and validate results.”

Why Contradictions Arise

Each measurement methodology operates with distinct assumptions, data sources, and timeframes, which naturally lead to variances in the outputs they produce:

• MMM relies on long-term, aggregated data, making it effective for evaluating broad trends but less responsive to short-term changes.
• Incrementality testing isolates specific channels or tactics to determine causal relationships, offering granularity but limited by the scope and timing of the test.
• Attribution models provide detailed, near-real-time insights but are prone to biases, particularly with lower-funnel over-attribution.

These foundational differences often lead to conflicting interpretations of channel performance, especially when evaluating high-impact channels like paid search or upper-funnel tactics like display and video.

Navigating the Discrepancies

1. Prioritize Testing for High-Discrepancy Channels
   When significant gaps appear between methodologies, it’s a clear signal to focus on those areas in future testing. “For example,” explained Burghes, “if MMM indicates a channel is underperforming, but attribution shows it as a top performer, that’s an ideal candidate for incrementality testing to determine the true impact.”
   High-spend or strategically critical channels should take precedence, as resolving discrepancies in these areas can deliver the most immediate and meaningful returns.
2. Leverage Triangulation for a Holistic View
   Hile recommended using triangulation—the practice of combining insights from multiple methodologies to form a more nuanced perspective.
   “Each methodology has strengths and blind spots,” Eli Hile, Senior Director of Data and Analytics at Tombras noted. “Triangulation allows marketers to use the strengths of one approach to compensate for the weaknesses of another, creating a blended perspective that’s more reliable than any single source.”
   For instance, attribution can provide day-to-day performance data, while MMM offers long-term context, and incrementality testing validates specific assumptions. Together, they ensure that marketers aren’t overly reliant on a single, potentially flawed, data source.
3. Adopt Calibration Techniques
   Validation efforts, such as applying multipliers derived from testing or MMM outputs, can reconcile discrepancies. For example, if MMM indicates that brand search is less incremental than attribution suggests, applying a calibration factor to attribution data can bring the two into closer alignment, enabling better decision-making.
4. Understand the Context of Each Methodology
   Contradictions often arise when methodologies are applied outside their ideal scope. MMM is designed for evaluating long-term media mix strategies, while incrementality testing excels at pinpointing channel-specific impacts. Recognizing the strengths and limitations of each approach ensures they are used appropriately.

Embracing Contradictions as Growth Opportunities

Rather than viewing contradictions as obstacles, marketers should embrace them as signals for deeper exploration. These variances often reveal areas where a channel’s performance is misunderstood or where a measurement methodology might need adjustment.

“Ultimately, the business of media optimization is about making decisions in uncertain environments,” Burghes explained. “Contradictions push us to dig deeper, refine our methods, and align our strategies more closely with real-world dynamics.”

Hile concurred, adding, “These moments of divergence are where the most significant breakthroughs happen. By leaning into the data, questioning assumptions, and triangulating insights, marketers can turn ambiguity into actionable intelligence.”