The editorial and operations contributors behind SirenCY’s public guides.

Why this page exists

The creator-economy content space has been overrun in the last eighteen months by AI-generated comparison pages, affiliate listicles dressed up as journalism, and ranking sites whose only criterion is which agency paid the highest referral fee. We publish an authors page because the alternative — anonymously published guides on an agency-owned domain — is exactly the format readers should distrust most. An operating agency that compares itself to competitors has obvious commercial incentives, and the only way to be useful to readers under that constraint is to be loudly transparent about who writes the content, what data they have access to, and how that data is sanity-checked before it lands on a public URL. That is what this page is for. It names the internal teams responsible for each content category, lists what they are qualified to speak about, and documents the standards we expect our own pages to meet. Readers can then weight our content accordingly, and they have a concrete address to email when we fall short of those standards.

When you read a comparison page on this site — SirenCY versus another agency, one funnel structure versus another, one traffic platform versus another — you should expect a few things consistently. Commercial context is disclosed in the opening paragraphs, not buried in a footer. Comparison criteria are stated explicitly rather than implied through selective benchmarks. Numbers carry sources, and where exact public figures do not exist, ranges are used and labeled directional rather than dressed up as precise statistics. Case studies cite real internal payout records or platform analytics, anonymized when the creator has not opted into public attribution, and never extrapolated from a single quarter into a misleading annual figure. We try to write the comparison page we would want to read as a creator evaluating agencies for the first time, which means assuming the reader is skeptical of an agency-owned source by default. That skepticism is healthy, and this page is the structural answer to it.

How content is sourced and verified

Statistical claims on this site fall into three buckets, each with a different sourcing standard. Platform-level data — OnlyFans commission rates, payout schedules, content policy changes — comes from OnlyFans' own documentation, support center articles, and creator agreement updates, with the page or document URL preserved internally so we can re-verify when the platform changes wording. Industry-level data — creator counts, average earnings distributions, geographic spread, demographic breakdowns — comes from public surveys conducted by adult-industry research organizations, academic papers, government tax-authority disclosures, and reputable industry trade publications. Internal-aggregate data — chatter performance metrics, funnel unlock rates, post-onboarding revenue curves, retention figures — comes from our own anonymized operational records, never from a single creator's account, and is presented as ranges rather than point estimates when the sample size is small enough that a single outlier would distort the picture. Every statistical sentence on a public page is expected to map back to one of those three buckets, and our internal review process flags claims that do not.

The harder problem with comparison content is the biased-benchmark trap. It is trivially easy for an agency to construct a comparison page where the criteria selected just happen to be the criteria the agency wins on, while the criteria where competitors are stronger are quietly omitted from the table. We work to avoid that pattern by drafting comparison criteria before scoring any agency on them, including categories where we know we score lower than at least one competitor, and stating the weight a reader should assign to each criterion based on their own creator profile rather than presenting a single composite score as if it were objective. When a comparison page describes a competitor, we link to the competitor's own public website rather than paraphrasing their value proposition through our lens. When we cannot verify a competitor claim, we say so on the page rather than guessing. None of this makes the page neutral — it remains commercial content on an agency-owned domain — but it changes what kind of commercial content it is.

Case-study earnings figures are the highest-risk category we publish, because the temptation to round upward is constant and the verification overhead is real. The standard we hold ourselves to is that every dollar figure in a case study must trace to a specific underlying record — a payout statement, a platform analytics export, a signed earnings disclosure — before it appears in copy. Where a creator has consented to public attribution, the case study runs under their working name with the period covered stated explicitly. Where attribution is not consented, the case study runs anonymized with the same documentation standard internally, and the page makes the anonymization visible rather than pretending the figure is from a named source. We do not compress multi-year trajectories into single annualized figures, we do not present peak months as if they were averages, and we do not republish historical case studies without re-checking whether the original numbers still represent today's economics. When the math no longer holds, the case study is either updated or retired.

When pages get reviewed

Evergreen pillar pages — the long-form guides on funnels, pricing, traffic strategy, country-level market context, and agency comparison content — are on a quarterly review cadence. Each quarter the editorial team works through the pillar inventory, re-verifies the platform-level facts, re-checks any cited external sources for link rot or substantive changes, and updates ranges or benchmarks where the underlying internal data has shifted by more than a trivial amount. Quarterly reviews are scheduled rather than reactive, so a page that has not changed in three months has still been looked at by a human within that window, and the last-modified date reflects that.

Outside of the quarterly cycle, a defined list of triggers forces an immediate update on any affected page. OnlyFans changes to commission structure, payout schedule, content policy, or creator agreement terms are the most common trigger and typically require same-week updates across dozens of pages. A major competing agency rebranding, merging, splitting, or shutting down forces updates to any comparison content referencing the previous entity. Tax-law changes in jurisdictions we cover — most often the United States, United Kingdom, Australia, Canada, and the EU — trigger updates to country guides and earnings explainers. Significant shifts in platform-level data published by OnlyFans itself, such as updated creator-count or payout figures, propagate through pillar pages within the same quarter.

Last-modified dates on this site reflect substantive content changes rather than cosmetic edits. A typo fix, a styling tweak, a navigation update, or a schema-markup adjustment does not advance the last-modified date — those are tracked separately in version control. A figure update, a paragraph rewrite, a new section, a criterion change in a comparison table, or a refreshed case study does advance it. The intent is that a reader who returns to a page months later can tell at a glance whether anything material has changed since their last visit, and the date is the load-bearing signal for that. Pages that have not been substantively touched since their original publication date carry that original date honestly rather than being date-stamped forward to appear fresh.