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What is incrementality compared to attribution?

Attribution models and incrementality testing both aim to measure marketing impact, but they do so in fundamentally different ways.

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Attribution: Assigning credit to touchpoints

Attribution models (such as last-click, first-click, or data-driven attribution) attempt to allocate credit for conversions to different marketing touchpoints. These models help marketers understand which channels or campaigns are involved in the customer journey.
Example: A customer clicks on a Google ad before purchasing a product. Attribution models may assign credit to the ad, but the customer might have purchased the product anyway—even if they had never seen the ad.

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Limitations of attribution models

  • They assume correlation equals causation. Just because a user clicks on an ad doesn’t mean the ad caused the purchase.
  • They don’t account for organic behavior. Some customers would have converted without any marketing exposure.
  • They struggle with cross-device tracking and walled gardens. As users move between devices and platforms, attribution models often lose visibility.
Key question: Would this sale have happened without the ad?Unlike attribution, which assigns credit based on observed behavior, incrementality testing proves whether marketing is driving additional revenue or profit beyond what would have happened organically.

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Where does MMM fit in?

Marketing mix modeling (MMM) is another approach used to measure marketing impact, particularly at a broader level. It uses statistical models to analyze historical data and estimate how different marketing channels contribute to overall sales.

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Strengths of MMM

  • Provides a holistic view across all channels, including online and offline marketing.
  • Doesn’t rely on user-level tracking, making it privacy-friendly.
  • Useful for long-term budgeting and strategic decisions.

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Limitations of MMM

  • Requires large amounts of historical data and works best with longer timeframes.
  • Less precise for tactical optimizations, since results are often aggregated and not at the individual campaign level.
  • Difficult to measure rapid changes, as updates require recalibrating the model.

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How incrementality testing enhances MMM

While MMM provides a big-picture view, incrementality testing offers a high-precision measurement of specific campaigns or channels. Running both in parallel allows marketers to:
  • Validate and calibrate MMM results with real-world experiments.
  • Use incrementality testing for tactical decisions (e.g., testing a new channel or campaign change) and MMM for strategic planning.
  • Ensure that media mix decisions are based on causal impact rather than just historical correlations.

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Summary

  • Attribution models track and assign credit but don’t prove causality.
  • MMM estimates overall channel impact but works best at a high level.
  • Incrementality testing directly measures the true lift from marketing spend through controlled experiments.
By combining these approaches, marketers can get both high-level strategic insights and precise causal measurement of their campaigns.