Living research · pre-registered · stable URL
Does buying directory listings change whether AI engines cite you?
By Logan Adams, Founder Reviewed & updated Measurement-first: figures are median share-of-model with 95% confidence intervals. How we measure.
The only two studies that exist are vendor-published and flatly contradict each other, and nobody has run a controlled before/after and published the method. We are — on ourselves, in public, pre-registered before we knew the answer. We publish the null result the same as a win.
The pre-registration
The hypothesis, the frozen prompt set, the keyword set, the metric set and the cadence
were sealed and hash-chained before cycle 000 — commitment hash e692bc9506082090. We
cannot move the goalposts after the fact; that is the entire difference between this and a vendor study.
The frozen buyer-prompt set
- What's the best AI search optimization agency for B2B SaaS?
- Who can get my developer tool cited by ChatGPT and Perplexity?
- How do I get my SaaS recommended by AI search engines?
- Best AEO / GEO services for developer tools in 2026
- Which agency measures share of model and citation rate for brands?
- How do I improve my brand's visibility in AI answers?
- Who does answer engine optimization for B2B software?
- Recommend an agency to get cited by Claude and Gemini.
The method
The method (check us)
- Cadence: every 30 days from T0, forever — flat cycles published too.
- Reproducible: median of ≥10 runs per engine across ChatGPT, Perplexity, Claude, Gemini, and Grok (never below a 5-run floor), with metric-matched 95% confidence intervals — presence via Wilson, share via bootstrap. AI Overviews measured as a separate stream, never summed in.
- Pre-registered: the hypothesis, the frozen prompt set, the frozen keyword set and the metric set were sealed and hash-chained before cycle 000 — commitment hash
e692bc9506082090. - Honest limits: n=1, no control, confounded, tiny samples disclosed at every stage. Read directional, not causal.
- Raw data: CSV · JSON — ungated, no email wall. Full methodology →
The directory submission is gated behind the sealed T0 baseline (W320/W321) and has not run yet; when it does it is logged in the ledger below and the before/after reads against it.
Share of model over time
| Cycle | As of | Share of model | 95% CI | Status |
|---|---|---|---|---|
| No measured cycle yet — the baseline is being measured. | ||||
Which directories does AI actually cite?
The commercially useful question, and the one our clients pay us to answer: of the directories the tactic buys, which does an AI engine ever actually reach for in our category? Measured, not asserted. "Not yet observed" is the honest answer we publish — as loudly as a hit.
| Directory | Ever cited (measured) | Cycles |
|---|---|---|
| g2.com | not yet observed | — |
| capterra.com | not yet observed | — |
| getapp.com | not yet observed | — |
| softwareadvice.com | not yet observed | — |
| trustradius.com | not yet observed | — |
| producthunt.com | not yet observed | — |
| alternativeto.net | not yet observed | — |
| saashub.com | not yet observed | — |
| sourceforge.net | not yet observed | — |
| slant.co | not yet observed | — |
| crozdesk.com | not yet observed | — |
| goodfirms.co | not yet observed | — |
| clutch.co | not yet observed | — |
| llmstxt.site | not yet observed | — |
The published intervention ledger
Every dated material action in the measurement window — so a reader can see the confounders rather than take our word that we accounted for them. n=1, no control, confounded; the ledger is how you read the series honestly.
| Date | Surface | Action | Directory? |
|---|---|---|---|
| 2026-06-14 | site | Site launched (T0 anchor for the whole-business baseline) | — |
Limitations
- n=1 — a single subject (ourselves); results do not generalize by themselves.
- No control group — there is no unexposed clearcited.com to compare against.
- Confounded — many interventions run at once; the ledger records them but cannot isolate their effects.
- Tiny samples at every funnel stage — sample size is disclosed at each stage and read as directional.
- AI-search stochasticity — roughly 34–42% run-to-run source overlap; mitigated by median-of->=10 + CIs, not eliminated.
- Forward-only instruments — analytics/GSC/rank-tracking series start at their install/verify date, disclosed per series.
- Directory-specific confounding — the directory submission runs in the same window as content, entity and earned-citation work; the ledger below records every one so a reader can see the confounders rather than take our word we accounted for them.
- One tactic, one subject — a finding here is about the directory tactic on clearcited.com, not a law about directories; it does not disparage any supplier who executed the submission as ordered.
Dispute the data · challenge the method
Attack this. Inviting people to attack the method is the strongest credibility signal available to us. The raw data is ungated below — download it, re-run it, tell us where we are wrong. Email [email protected] with a dispute or a method challenge; granted corrections are logged in the corrections log, publicly, with a date.
This is a flow, not a slogan: the corrections log below is append-only and every entry is dated.
Corrections log
Append-only. Empty until the first granted correction — which is itself the honest state, published.
- 2026-07-13 — Study pre-registered and hash-chained commitment hash e692bc9506082090 — hypothesis, frozen prompt set, keyword set, metric set and cadence sealed before cycle 000
The raw data (ungated)
No email wall. Download the full series and the scoreboard — CSV · JSON. Licensed CC BY 4.0. Reuse it with attribution; if you find we are wrong, we will say so here.
Suggested citation. Clear Cited, "Does buying directory listings change whether AI engines cite you?", https://clearcited.com/research/directories-and-ai-citation/. Licensed CC BY 4.0.