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#25. Blending Incrementality Testing with Multi-Touch Attribution

Marketing measurement doesn’t have to be a choice between incrementality testing and multi-touch attribution (MTA). In fact, combining both approaches leads to more accurate insights, smarter optimization, and greater clarity on what’s truly driving revenue.

This guide explores how to blend incrementality testing with MTA models to capture both the causal impact of campaigns and the full customer journey.

Why Combine Incrementality and Attribution?

While multi-touch attribution shows who touched what and when, incrementality testing tells you what actually caused a conversion.

MethodStrengthLimitation
Incrementality TestingMeasures causal lift, ideal for proving ROIDoesn’t provide touchpoint-level insight
Multi-Touch AttributionOffers granular path analysisMay over-attribute conversions without lift context

Blending the two gives you both path analysis and lift validation.

Step 1: Use Incrementality to Calibrate MTA Models

Before deploying attribution across your stack:

  • Run geo-lift or PSA tests on key paid channels.
  • Measure true incremental conversions or revenue.
  • Use the lift data to adjust weightings in your attribution model.

Example:

If a channel is heavily present in customer journeys but shows no lift in geo tests, you can reduce its attributed value in your MTA model.

Step 2: Map Conversions Across Touchpoints and Test Groups

Use your data warehouse (e.g., BigQuery) to join:

  • Campaign and click data from ad platforms.
  • Conversion events from GA4 or your CRM.
  • Test/control flags from incrementality studies.

Query Structure Example:

SELECT
  user_id,
  test_group,
  touchpoint_sequence,
  conversion,
  campaign_name
FROM `project.dataset.joined_data`

This lets you compare attribution paths across test and control groups.

Step 3: Apply Attribution Models with Incrementality Adjustments

Once you’ve validated lift:

  • Run your MTA model (Markov, Shapley, or algorithmic).
  • Adjust attribution scores by lift coefficients.
  • Recalculate channel contribution to reflect both influence and causality.

Example Adjustment:

If your Shapley model gives Facebook 20% credit, but your geo test shows 50% of that is non-incremental, reduce its final attribution to 10%.

Step 4: Visualize Combined Insights in Looker Studio

Build dashboards that show:

  • Conversion paths with adjusted attribution values.
  • Channels with high presence but low lift.
  • Incremental ROAS vs. attributed ROAS.
  • Lift-adjusted campaign rankings.

Add filters for:

  • Region
  • Funnel stage
  • Campaign type

Step 5: Optimize Strategy Using Both Models

With both attribution and lift data in hand:

  • Prioritize budget to channels with high incremental value and cross-path influence.
  • Deprioritize channels with low lift despite high attribution.
  • Align creative and messaging strategies to channels with proven conversion impact.

Final Thoughts

MTA tells you the story. Incrementality tells you the truth. By blending both, you get the best of both worlds—full-funnel visibility and statistically grounded ROI. This hybrid approach empowers marketing teams to allocate spend with confidence and optimize every dollar.

Next Steps

In upcoming articles, we’ll explore:

  • Building Hybrid Attribution Models in BigQuery
  • Comparing Lift-Based Attribution vs. Algorithmic Models
  • How to Scale Incremental-Lift Attribution Across Global Campaigns

Stay tuned for smarter attribution strategies!

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