Testing AEO involves running controlled experiments in a 'Query Lab' to measure changes in visibility metrics like PAWC and Subjective Impression. This data-driven process validates which content edits improve performance in answer engines.
Core Stats
"Without data, you're just another person with an opinion."
- W. Edwards Deming
How to Test AEO with a Query Lab
Effective Answer Engine Optimization is scientific. Instead of guessing, you can run controlled tests to measure exactly how your content edits impact visibility. This guide walks you through the process using a Query Lab.
The 5 Steps of an AEO Test
- Ingest URLs: Upload a sitemap or paste a list of URLs into the Query Lab to define your test group.
- Select Query Set: Choose a pre-built 250-query set relevant to your domain and user intent (e.g., 'SaaS - High Intent').
- Run Baseline Test: Execute the test to capture initial metrics, including PAWC, citation rate, and Subjective Impression (SI).
- Apply Content Edits: Implement AEO changes on your staging server, such as adding citations, quotes, or tightening the lead paragraph.
- Re-test & Compare: Run the test again on the edited content and compare the delta in PAWC and SI to measure the impact of your changes.
Interpreting the Results
The goal is to see a positive change (a 'delta' or Δ) in your key metrics. A successful test will show:
- ΔPAWC: An increase in your Position-Adjusted Word Count, meaning more of your content is appearing in answers.
- ΔSI: An increase in your Subjective Impression score, meaning the perceived quality and authority of your content has improved.
Frequently Asked Questions
What is a Query Lab?
A Query Lab is a controlled environment for testing how content changes affect visibility in answer engines across a standardized set of queries.
Can I use my own queries?
Yes, custom query sets can be uploaded, but we recommend starting with our benchmark sets for standardized comparisons.
How often should I run tests?
We recommend running tests after any significant content update or at least quarterly to track baseline visibility changes as answer engines evolve.