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#23. Automating Incrementality Testing with BigQuery & Looker Studio

Incrementality testing is one of the most effective ways to measure true marketing impact. But running these tests manually can be time-consuming, especially when analyzing geo-based lift or PSA tests across multiple campaigns. By automating incrementality testing with BigQuery and Looker Studio, marketers can streamline experimentation and deliver insights faster.

This guide covers how to build an automated, scalable incrementality testing pipeline using BigQuery for data modeling and Looker Studio for visualization.

Why Automate Incrementality Testing?

Traditional incrementality testing methods:

  • Require manual CSV exports and pivot tables.
  • Take days to analyze post-campaign.
  • Struggle to scale across regions, audiences, and creative.

Automation enables:

  • Real-time or near-real-time lift analysis.
  • Consistent methodology across experiments.
  • Scalable reporting and stakeholder sharing.

Step 1: Design Your Test Framework

Incrementality tests typically use one of two designs:

1. Geo Lift Testing

  • Randomly assign regions into test and control groups.
  • Run ads in the test group only.
  • Measure lift in conversions or revenue.

2. PSA Testing

  • Show brand-neutral ads (e.g., charity PSAs) to the control group.
  • Run real campaign ads to the test group.
  • Compare results across groups.

In both cases, clearly define:

  • Test/control regions or audiences.
  • Start/end dates.
  • Primary KPIs (conversions, ROAS, revenue).

Step 2: Ingest and Structure Data in BigQuery

Data Sources to Include:

  • Google Ads, Meta Ads, LinkedIn: Spend, impressions, conversions by geo or audience.
  • CRM/GA4: Sales or lead data segmented by test/control groups.
  • Region mapping or audience ID table.

Schema Structure Example:

dategeogroupspendimpressionsconversionsrevenue
2024-05-01TXTest100050,000120$10,000
2024-05-01CAControl0075$7,500

Load your data into BigQuery tables and refresh them daily via API or ETL tools.

Step 3: Build Lift Calculation Queries

Use SQL to calculate incremental lift metrics.

Sample Query: Conversion Lift

SELECT
  group,
  SUM(conversions) AS total_conversions,
  SUM(revenue) AS total_revenue
FROM `project.dataset.incrementality_data`
WHERE date BETWEEN '2024-05-01' AND '2024-05-14'
GROUP BY group

Lift Formula:

Lift (%) = ((Test - Control) / Control) * 100

Repeat for impressions, spend, ROAS, etc.

Use scheduled queries in BigQuery to refresh calculations automatically.

Step 4: Visualize Results in Looker Studio

Connect your BigQuery data to Looker Studio and build dashboards that include:

  • Total lift in conversions, revenue, or ROAS.
  • Trend charts over the test period.
  • Geo heatmaps showing regional performance.
  • Filter controls for brand, campaign, or product lines.

Use color-coded metrics (green = positive lift, red = negative) to make insights easy to interpret.

Step 5: Scale Testing Across Campaigns

Automation Ideas:

  • Create dynamic test/control group tables based on geo or audience logic.
  • Schedule test periods with start/end logic embedded in your pipeline.
  • Use Dataform or dbt to modularize SQL for each campaign.
  • Trigger email or Slack alerts when significant lift is detected.

This approach enables you to manage multiple incrementality tests across brands, regions, and formats with minimal manual effort.

Final Thoughts

Automating incrementality testing with BigQuery and Looker Studio empowers marketers to measure campaign effectiveness at scale—without waiting for weeks of manual analysis. By integrating real-time data, consistent testing frameworks, and automated dashboards, your team can confidently prove ROI and optimize faster.

Next Steps

In upcoming articles, we’ll explore:

  • Combining Incrementality Testing with Multi-Touch Attribution Models
  • Building Geo Lift Models Using BigQuery ML
  • Automated Experimentation Pipelines for Always-On Testing

Stay tuned for more data-driven experimentation strategies!

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