Imagine three investigators arriving at the same crime scene. We analyze the fingerprints. Another reviews surveillance footage. The third interviews witnesses. Everyone leaves with a unique interpretation of what happened.
The lead detective’s job isn’t to decide which evidence matters most. It’s about piecing together all the evidence to come to a more complete understanding of the truth. Today, the measure requires you to do the same.
A marketing team meets to review quarterly performance. MMM analysis shows that video has driven business growth. The performance team highlights attribution data crediting paid social conversions. At the same time, an incrementality study suggests that some conversions would have occurred without any media exposure.
Three methodologies. Three competing interpretations of the same business question: What really drives business outcomes?
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Measuring systems are not designed to agree
For years, the industry has sought a single source of truth regarding MMM, multi-touch attribution (MTA), and incrementality, often treating them as competing approaches rather than complementary forms of evidence. But these systems were never designed to do the same thing.
MMM evaluates the company’s overall contribution and media mix allocation. Attribution focuses on user-level interactions and conversion paths. Incrementality measures whether marketing exposure actually influenced consumer behavior.
Each methodology captures different dimensions of marketing performance, and conflicting results often reflect multiple dimensions of consumer behavior rather than flaws in the methodology itself. Yet many organizations still struggle to effectively operationalize these approaches.
Companies often prioritize one framework as the primary decision-making model, while others are treated as directional or complementary rather than integrated into strategy, optimization and investment planning.
Interpretation of conflicting measurement signals
Measurement systems are often contradictory because they rely on distinct data sets, assumptions, analytical models and definitions. MMM, attribution, and incrementality can each evaluate the same CTV campaign differently depending on their methodologies, data structures, and underlying assumptions.
At the same time, systems can evaluate campaign performance using entirely separate signals. One model might rely on completed video views, another on website visits or QR scans, while an incrementality study compares exposed and unexposed households to determine whether media exposure generated incremental sales.
All three evaluate the same campaign through distinct analytical lenses. Conflicting results often reflect different dimensions of consumer behavior rather than flaws in the methodology itself.
Measurement is no longer about choosing between MMM and attribution, or replacing attribution with incrementality. Each methodology captures different dimensions of marketing performance and consumer behavior, and you must interpret these signals collectively.
Taken together, these findings can help you understand how channels influence awareness, consideration, conversion, and long-term growth throughout the customer journey. Operational change now moves from consolidation of measures to coordination of measures.
Triangulation must become standard practice
The real power of measurement is not choosing between MMM, attribution, and incrementality. It is a triangulation between the three.
By triangulating MMM, attribution, and incrementality, you can compare signals across systems, reconcile discrepancies, and build trust through convergence rather than relying on a single outcome.
If MMM shows a strong contribution from CTV, attribution shows paid conversions on social and incrementality confirms an increase on both channels, the broader interpretation may be that CTV creates awareness and demand at the household level, while paid social captures and converts this demand later in the funnel.
For example, a consumer may first view a streaming television advertisement for a new athletic shoe brand while watching a live sporting event. A few days later, that same consumer encounters a paid social ad offering a limited-time promotion and clicks to buy.
Attribution can credit paid social networks for the conversion because it captured the final interaction before purchase. MMM could show that CTV generated greater sales growth in regions where the campaign was widely distributed. Incrementality tests could then confirm that consumers exposed to both channels were significantly more likely to purchase than unexposed audiences.
Overall, you might decide to maintain or increase your investments in CTV to continue to build awareness and demand, while optimizing paid social to audiences already exposed to the streaming campaign.
Rather than shifting budget entirely to the channel receiving conversion credit, triangulated insights help you balance upper and lower funnel investments, improve sequencing across channels, and allocate spend based on how each tactic contributes to broader business outcomes.
Which triangulated measure should evaluate
When triangulating metrics, evaluate:
- Which channels and tactics appear to influence awareness, conversion, or downstream business outcomes at different stages of the customer journey.
- Where measurement signals align, diverge or reinforce each other depending on the methodologies.
- What role does each channel play in the broader media strategy and how these roles should influence optimization and budget allocation.
- Which measurement results are most actionable for in-flight optimization, long-term planning, or validation of incremental impact.
The goal is not to force full alignment of MMM, attribution, and incrementality. These systems were designed to evaluate performance from distinct analytical perspectives.
Better measurement decisions come from connecting fragmented signals, without forcing complete alignment between systems. However, not all organizations have the luxury of fully operationalizing MMM, attribution, and incrementality simultaneously, especially as the industry continues to grapple with data loss, inconsistent data, increasing costs, operational complexity, and increasing pressure to make faster, AI-driven decisions.
Even so, the strongest conclusions rarely come from a single source. They come from connecting the evidence.
