Test what moves AI visibility
Run controlled experiments on your GEO strategy. Siftly splits your tracked topics into a test group and a control group, then tracks how visibility and citation metrics diverge over time — so you see which changes actually move the needle.
Content
Generate, optimize, and publish AI-optimized content
Typically takes 3-5 minutes · Elapsed: 1m 23s
Turn GEO from guesswork into a measurable channel
Divide topics into balanced groups
Siftly clusters your tracked topics by citation behavior and splits them into a test set and a control set. Balanced splits mean differences in outcome reflect your intervention — not uneven groups.
Citations
Internal links
Word count
Watch the two groups diverge
Compare visibility % and citation counts for test vs control over time. A widening gap after you ship a content change is the signal that the change is working.
Typically takes 3-5 minutes · Elapsed: 1m 23s
Scale what works, skip what doesn't
A library of every change you've tested
Every experiment — winners and losers — becomes part of your team's playbook. Reuse winning patterns across pages; retire tactics that didn't move the needle.
You call the winner, not a black box
Siftly surfaces the raw comparison: test vs control, visibility, citations, the trend. When the gap is clear, you mark the winning variant — no opaque verdicts from a model you can't inspect.
Citations
Internal links
Word count
Why Experimentation Is the Missing Piece in AI Visibility
AI visibility experimentation is the practice of running controlled tests — splitting your tracked topics into a test group and a control group, making a content change that affects only the test group, and comparing how visibility and citation metrics move between the two groups over time. Without a control group, any change you see could be noise — AI model updates, competitor launches, general topic volatility. With one, you see what would have happened anyway and isolate the effect of your change.
Most AI visibility tools stop at monitoring: they tell you where you stand, but not what to do about it. Siftly's experimentation feature adds the before/after structure that turns monitoring into a system for improvement.
How Test & Control Splits Work
- 1Cluster your topicsSiftly analyzes citation patterns across your tracked topics and uses hierarchical clustering to group similar topics together based on which sources AI engines cite for each one.
- 2Balance the splitTopics are divided into a test set and a control set so that both groups have comparable baseline visibility, citation counts, and topic coverage. Split-quality scores tell you how well-matched the two groups are before you start.
- 3Freeze the controlLeave the content supporting your control topics unchanged. Control is your "what would have happened" baseline — it captures background movement you didn't cause.
- 4Ship the change on testImplement your intervention — new content, schema additions, restructuring, freshness updates — only for pages that answer test-group topics.
- 5Track the divergenceSiftly records visibility % and citation counts for both groups daily and renders them side-by-side so you can see the gap grow — or not.
- 6Call the winnerWhen the trend between test and control is clear and holds over a multi-week window, mark the winning variant. The experiment stays in your library for reference and reuse.
Experiment Types That Drive Results
| Experiment Type | What You Change | Typical Impact | Time to Clear Signal |
|---|---|---|---|
| Data enrichment | Add original statistics, benchmarks, or survey results | High — unique data is the strongest citation driver | 2-3 weeks |
| Structural optimization | Reformat with GEO patterns (definitions, tables, ordered lists) | Medium — improves AI parseability | 2-4 weeks |
| Schema addition | Add FAQ, HowTo, Speakable, or Article schema | Medium — explicit signals for AI crawlers | 3-4 weeks |
| Content expansion | Add new sections covering subtopics competitors miss | Medium-high — improves topical authority | 3-4 weeks |
| Freshness update | Replace outdated stats with current data, update date | Medium — AI prefers fresh content | 1-2 weeks (for real-time platforms) |
| New page creation | Publish entirely new content targeting a visibility gap | Variable — depends on topic competition | 4-6 weeks |
Reading Experiment Results
Every experiment surfaces the same core numbers side-by-side for test vs control:
The signal you're looking for isn't a single verdict — it's a widening, directional gap between test and control that holds across a multi-week window. When the two lines clearly diverge and the gap is stable, the change worked. When they move together, it didn't.
How it works
Time to value, not time to configure
Set up the split
Pick the topics you're testing. Siftly clusters them into balanced test and control groups using citation-based similarity so the two groups start from comparable baselines.
Ship the change
Implement your content change — new pages, schema, restructuring, freshness updates — on the pages that answer your test-group topics. Leave control-group topics untouched.
Read the gap
Watch visibility and citation metrics for test vs control diverge. When the gap is clear and stable, mark the winning variant and roll the change out to other topics.
FAQ
Frequently asked questions
How do AI visibility experiments work?
You split your tracked topics into a test group and a control group. You leave the control group unchanged and ship a specific content change that affects only the test group. Siftly tracks visibility % and citation counts for both groups over time, showing you whether the change actually moved the needle relative to what would have happened anyway.
How long does an experiment need to run?
Most experiments need 2-4 weeks before the gap between test and control is clear enough to act on. The more sensitive the topics (frequent queries, fast-changing AI models), the sooner you'll see a trend. Siftly shows the daily and weekly trajectory so you can judge when the signal is strong and stable.
What kinds of changes can I test?
Common experiments include: adding original data or statistics to a page, restructuring content with GEO formatting (tables, definitions, ordered lists), adding or updating schema markup, rewriting page titles or meta descriptions, publishing new competitor comparison pages, and refreshing outdated statistics with current data.
How is this different from website A/B testing?
In website A/B testing, you show different page versions to different users at the same time. In AI visibility experiments, you change content permanently for one set of topics (the test group) and leave other topics untouched (the control group). Then you compare how visibility and citation metrics move for the two groups over time.
What makes this feature unique to Siftly?
Most AI visibility tools only monitor — they show you where you stand but not what works. Siftly adds a before/after structure: balanced test and control groups built from citation-based clustering, consistent daily measurement across both, and a historical library of every change you've tested and whether it moved the metric.
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