Testing and Experiment Design for GEO and AEO
How growth teams run valid experiments on AI search visibility and turn “best practices” into repeatable tactics
AI summaries and answer engines make learning noisy. You can publish strong content and follow the usual playbook, yet visibility in generative answers still swings by engine, query wording, domain, and which sources the system prefers. That’s why GEO and AEO need experiments, not folklore.
Two (2) research anchors frame our problem:
This GEO: Generative Engine Optimization paper shows specific tactics can increase visibility inside generative responses, and results vary by domain; and thus, you need systematic tests. (Source: https://arxiv.org/abs/2311.09735) (arXiv)
A newer large-scale comparative study finds AI search systems differ a lot from each other and are sensitive to phrasing, freshness, and source-type bias; and thus, you should test per answer engine and not assume one rule fits all. (Source: https://arxiv.org/abs/2509.08919) (arXiv)
This post covers how to design GEO/AEO tests that produce usable answers from clear hypotheses, clean controls, realistic timeframes, and metrics tied to growth outcomes.
Why GEO needs experiments and not only best practices
Best practices are a good starting point. Experiments tell you what holds for your category, your website, and the AI answer engines you care about.
Variability across engines and categories
Three (3) kinds of variance break one-size GEO playbooks:
1) Engine differences
AI search engines differ in domain diversity, freshness behavior, cross-language stability, and phrasing sensitivity. A tactic that increases citations in one engine can do nothing in another.
2) Source-mix bias
That same study reports a strong bias toward earned media over brand-owned content in several AI search systems. If your plan relies mostly on owned content, testing can quantify the gap and tell you what mix you actually need to grow your brand.
3) Citation noise and accuracy issues
Even when engines cite sources, the citations can be inconsistent or wrong. The Tow Center’s evaluation of multiple AI search tools documents citation problems in news contexts, which is a reminder to measure “quality of inclusion,” not just “presence.” (Source: https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php) (Columbia Journalism Review)
Bottom line: GEO experiments reduce uncertainty. They turn black-box behavior into tactics you can repeat.
Designing GEO experiments
You want validity without over-engineering. GEO tests run in messy conditions like algorithm changes, seasonality, content launches, and multi-surface journeys. You can still run clean tests by controlling what you own.
Hypothesis, treatment, control, and timeframes
1) Start with a narrow, testable hypothesis
A useful GEO hypothesis includes the following
Lever: what changes (structure, entities, schema, coverage)
Surface: where you expect the change (AI features, assistant referrals, citations)
Metric: what you’ll track (presence, quality, downstream outcomes)
Some Examples:
“Adding an answer-first block + FAQ section increases AI-feature inclusion for long-tail ‘how’ queries in our category hub.”
“Clarifying entity definitions + the internal link graph increases citation frequency for product comparisons across assistants.”
2) Pick an experiment unit you can control
Most teams succeed with one of these
Page-set test: pick 20–100 similar pages. Apply treatment to half, keep half as control.
Template test: apply treatment at the template level for a class of pages (docs, product pages, use-case pages).
Hub test: treat one entity hub (product line) and keep another hub as control (closest match only).
Don’t mix page types in one test. A product page and a blog post behave differently under generative summaries.
3) Define treatment and control precisely
Write down what changed so you can repeat it.
Example “Answer block” treatment:
add a 2–4 sentence direct answer at the top
add 3–5 bullet summary points
add 3–6 question-style H2s with concise answers
add proof (citations, standards, examples)
Example “Entity clarity” treatment:
add or tighten definitions for primary entities
standardize naming across headings, body, and internal links
add structured data where appropriate and compliant
Structured data isn’t guaranteed to trigger visible features, so keep expectations grounded. (Source: https://developers.google.com/search/docs/appearance/structured-data/faqpage) (Google for Developers)
4) Match the timeframe to the mechanism
Rule of thumb
Structure/content changes: 4–8 weeks minimum
Internal link graph and entity consolidation: 8–12+ weeks
Earned media + citation ecosystem effects: 12–24+ weeks
Keep pre and post windows stable. If you can, run a two-week baseline pre-period.
5) Protect Google Search performance during tests
Google outlines how to minimize risk when testing variations (consistent access for crawlers, avoid indexing issues, etc.). Use those rules as guardrails when you run URL or content variations. (Source: https://developers.google.com/search/docs/crawling-indexing/website-testing) (Google for Developers)
6) Instrument your outcomes before producing treatment
If you can’t observe the change, don’t implement it.
Minimum setup:
Search Console deltas (query sets tied to tested pages)
assistant referral sessions (channel grouping)
on-page engagement and conversion events (proof consumption, CTA clicks)
a manual or semi-automated “AI presence check” on a fixed query sample
Ethics and compliance
Treat GEO/AEO testing like classic search work with the same constraints, same need for clean behavior.
Avoid deceptive tactics. Google’s spam policies cover practices that mislead users or manipulate systems. (Source: https://developers.google.com/search/docs/essentials/spam-policies) (Google for Developers)
Markup must match visible content. Google’s structured data policies restrict eligibility when markup is misleading. (Source: https://developers.google.com/search/docs/appearance/structured-data/sd-policies) (Google for Developers)
Protect privacy in tracking. If you track copy behavior or snippet use, log metadata (block type, length bucket), not copied text.
Don’t chase misleading appearances. If an AI answer engine cites inconsistently, prioritize accuracy and durable user value.
Example Experiment Types
Structure, entities/schema, and coverage shape patterns map to common GEO/AEO levers.
Structured FAQs and Answer Blocks
When to use
pages get impressions or rank but convert poorly
queries are question-shaped (“how,” “what,” “best,” “vs,” “cost”)
users need quick clarity, then depth
Treatment
add an answer-first block at the top
add 3–6 question headings with concise answers
add FAQ structured data only when the page truly contains FAQs
Google’s FAQPage docs note structured data can help discovery for rich results, and also supports that there’s no guarantee features will appear. (Source: https://developers.google.com/search/docs/appearance/structured-data/faqpage) (Google for Developers)
Control
comparable pages with no new answer block and no FAQ section
Primary metrics
presence: AI feature inclusion for the target query set
quality: accuracy of extracted answers in sampled summaries
outcomes: conversion rate and proof-page clicks from those landers
Risk
repeating the same FAQ pattern everywhere creates redundancy. Keep FAQs tied to real buyer questions.
Schema and Entity Clarity
This targets how systems connect concepts, not only what they display.
When to use
your product terminology is ambiguous
teams describe the same feature in different ways
you sell into multiple industries with different vocabulary
Treatment
standardize entity names and definitions across the site
tighten internal links so entities connect consistently
add structured data that matches what’s visible and true
Treat structured data rules as constraints, not suggestions. (Source: https://developers.google.com/search/docs/appearance/structured-data/sd-policies) (Google for Developers)
Control
similar pages without entity standardization
Primary metrics
presence: citation frequency for entity-led queries (sampled checks)
quality: whether engines name the right product and use cases
outcomes: higher opportunity rate from evaluation pages tied to treated entities
AI Answer Engine note:
If you run citation checks, expect different systems to cite different source types. The 2025 comparative analysis reports earned-media bias in several AI search systems. (Source: https://arxiv.org/abs/2509.08919) (arXiv) That means entity clarity on owned content can still help, while earned validation changes inclusion odds.
Expand or Consolidate Topic Coverage
Coverage shape is a common lever for improving fewer, stronger pages versus many thin pages. Generative summaries tend to reward clarity, cohesion, and proof.
When to use
duplicated posts target adjacent queries
internal linking is fragmented
category hubs lack depth
Treatment options
Consolidate: merge 3–5 thin posts into one authoritative reference page
Expand: build a hub-and-spoke cluster around one entity with consistent definitions, examples, and proof
Control
keep comparable topic sets unchanged
Primary metrics
presence: inclusion across paraphrases and long-tail variants
quality: whether summaries reflect your intended framing
outcomes: conversion efficiency on fewer clicks
Measuring Outcomes Across AI Answer Engines
You need more than “did we show up.” Use three layers of presence, quality, and outcomes.
Presence, quality, and outcome metrics
1) Presence metrics
Use a fixed query set and check on a consistent cadence (weekly or biweekly):
AI presence rate: % of queries where your domain appears as a cited source
Citation share: share of citations owned by your domain among sampled answers
Landing-page alignment: % of citations that point to the intended canonical page
Support for citations varies by tool. OpenAI says ChatGPT search provides links to relevant web sources. (Source: https://help.openai.com/en/articles/9237897-chatgpt-search) (OpenAI Help Center)
2) Quality metrics
Presence without quality can hurt if systems misstate claims or cite the wrong section.
Use a simple rubric (0–3 per query):
0: not present
1: present but inaccurate or weak relevance
2: present and accurate, missing key nuance
3: present, accurate, includes core entities and your framing
Tow Center testing reinforces why you need a quality rubric instead of counting citations. (Source: https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php) (Columbia Journalism Review)
3) Outcome metrics
Tie tests to outcomes you already trust:
AI referral sessions (channel group)
conversion rate from treated pages (lead, demo, signup)
proof consumption rate (security page, implementation guide, case study views)
opportunity rate and cycle time for leads touched by treated assets
Outcome metrics prevent a common failure mode of chasing “AI presence” that never turns into revenue.
Data, Examples, and Frameworks You can Use
A sample GEO experiment backlog
Score each idea on Impact, Confidence, Effort, Risk, then sort.
| Priority | Experiment | Lever | Impact | Confidence | Effort | Risk |
|---|---|---|---|---|---|---|
| 1 | Answer-first blocks on top 30 non-brand landers | Structure | High | Med | Low | Low |
| 2 | Entity definition standardization across one product line | Entities | High | Med | Med | Med |
| 3 | Consolidate thin cluster into one reference hub | Coverage | High | Med | High | Med |
| 4 | FAQ + schema on top comparison pages | Structure + schema | Med | Med | Low | Low |
| 5 | Earned-media “proof pack” + PR placements for category terms | Source mix | High | Low | High | Med |
The “source mix” item exists because comparative research shows earned-media bias across AI search engines.
Case Patterns to Test
Use these patterns below to shape hypotheses.
Case Pattern 1: Answer block + proof links improves quality scores
Treatment is top-of-page answer block, key takeaways, proof links (security, implementation, case study)
Expected outcome is higher rubric scores for accuracy and relevance, higher proof consumption
Case Pattern 2: Entity hub + internal links improves citation alignment
Treatment is one canonical hub per entity, consistent internal linking from spokes
Expected outcome is more citations pointing to canonical hubs rather than random blog posts
GEO-bench results suggest these kinds of tactics can increase visibility in generative responses and that results vary by domain, which supports testing patterns instead of copying them. (Source: https://arxiv.org/abs/2311.09735) (arXiv)
Your Next Steps
Create a GEO experiment backlog and commit to two (2) controlled tests per quarter:
pick one lever (structure, entities/schema, coverage)
define treatment and control at the page-set level
lock a query sample and a quality rubric
run a 6–8 week window with a stable baseline
decide: scale, iterate, or kill
That cadence makes AI engine behavior less mysterious and turns GEO/AEO from reactive tweaks into a consistent routine for generating outcomes and learnings for brand growth.
Last updated 01-13-2026
