Direct answer: write one retention experiment as: for a defined subscriber cohort, change one creator-controlled part of the experience, keep the comparison conditions visible, observe one primary retention signal over a fixed window, and apply a decision rule written before results appear. Record execution gaps and confounders. The result can support repeat, revise, stop or inconclusive; it cannot guarantee retention.
This brief tests one experience change. It is not a list of generic retention tactics, a churn diagnosis, predictive scoring model or promise that subscribers will renew. Start from a documented problem, not from a tactic someone claimed worked universally.
If the problem is still unclear, use the subscriber churn diagnosis worksheet first. An experiment should test a plausible creator-controlled cause, not compensate for missing baseline evidence.
Write the observed problem without inventing intent
Describe what was observed, where, when and for whom. “Renewal is low” is incomplete. Record the cohort definition, subscription-start window, eligible count, observed end state and data source. Keep inferred motivations separate from measured events.
Add an evidence grade: direct when the record supports the event and cohort, directional when several causes remain possible, or unavailable when fields do not align. A directional problem can still produce a learning experiment, but the brief should not present it as a confirmed diagnosis.
Use the churn reason tagging template to preserve voluntary subscriber feedback without treating every reason as representative.
Choose one creator-controlled change
Name the exact experience element: welcome orientation, content navigation, expectation reminder, feedback prompt, release preview, service response or another bounded step. Write its current state and proposed state. If the change includes several new messages, a schedule shift and a different offer, it is not one variable.
Confirm the change can be delivered consistently for the eligible cohort. A clever intervention that is too labour-intensive to execute produces an implementation test rather than useful subscriber evidence.
Record what will remain stable: page promise, cohort rule, observation window, content cadence and any relevant access conditions. Stability does not eliminate every outside influence, but it makes interpretation less ambiguous.
Turn the idea into a falsifiable hypothesis
Use this structure: “For [cohort], changing [current experience] to [new experience] will move [primary signal] in [direction] during [window], because [observed mechanism].” Then write the null or challenging outcome that would make you reject or revise the idea.
Avoid outcome claims inside the hypothesis. “Will improve retention” is a test statement only when the measure, denominator and decision threshold are supplied. The threshold is creator-selected from operational context; this brief supplies no universal good rate.
Add one learning question even if the primary outcome is flat. For example: can the change be delivered at the planned workload, and do subscribers reach the intended experience state?
Define eligibility and comparison conditions
Write inclusion and exclusion rules before assignment. Include dates, subscription state, prior exposure, language or experience constraints only when relevant to the hypothesis. Do not remove inconvenient cases after seeing their outcome.
Choose a comparison that is practical and transparent: a prior matched period, a concurrent holdout where appropriate, or a baseline series. Note material differences. Small creator datasets often cannot isolate every cause, so use careful directional language.
Count each eligible subscriber once in the primary denominator. Record duplicates, late entrants, missing status and interrupted delivery separately rather than silently cleaning them away.
Select one primary signal and supporting checks
The primary signal should match the hypothesis and be observable, such as eligible subscribers reaching the next renewal state within the defined window. Show numerator, denominator and observation date beside every rate. Do not compare totals when cohort sizes differ.
Supporting checks can include delivery completion, relevant message response, content-view state or feedback completion. These explain execution and mechanism; they do not replace the primary signal after an unfavourable result.
Specify the source location and extraction rule. OnlyFans maintains current platform terms at OnlyFans Terms; use the live platform source for current feature and account boundaries rather than relying on a copied third-party description.
Fix the window and stopping conditions
Set assignment start, assignment end, minimum observation date and final review date. The window must allow the defined outcome state to occur. Do not stop early simply because the first few records look favourable.
Write operational stop conditions: failed delivery, material change to the page promise, unavailable source data, unexpected workload or another event that makes the comparison invalid. A stopped experiment is not a failed idea; it is an execution result.
Record changes with timestamps. If the intervention itself changes, close the experiment and issue a new version rather than blending two treatments under one ID.
Copy the retention experiment brief workflow
Artifact: copy these fields: Experiment ID | owner | problem statement | evidence grade | cohort | eligibility | exclusions | baseline window | proposed change | stable conditions | hypothesis | challenging outcome | primary signal | numerator | denominator | supporting checks | assignment start | assignment end | review date | stop conditions | workload ceiling | data source | decision rule | confounders | result | next action.
Pre-write four outcomes. Repeat when the stated rule is met and execution is valid. Revise when the mechanism or delivery needs one bounded change. Stop when evidence challenges the hypothesis or workload is unacceptable. Inconclusive when data, window or execution cannot support a decision.
For a fictional worked record, use 42 eligible new subscribers and a comparison group of 39, not percentages alone. Suppose delivery reaches only 24 of 42. Even if the observed renewal fraction appears higher, the review must flag execution coverage before attributing movement to the change.
Review experience evidence alongside the number
Read direct subscriber feedback linked to the intervention and preserve contrary responses. Use the active subscriber feedback loop to collect, tag and close feedback without turning anecdotes into a cohort rate.
Compare the final cohort conditions with the written brief. Note other content releases, pricing changes, platform interruptions, acquisition-source shifts and unusually incomplete records. These are interpretation limits, not excuses to hide the result.
Document what was learned about delivery, signal quality and subscriber experience. A flat primary result can still prevent a larger rollout of a costly idea.
Prepare the record before launch
Run a dry review with fictional records before assigning real subscribers. Confirm that eligibility can be determined, the intervention can be delivered, the primary event can be read from the named source and the review date lands after the required observation window. A brief that cannot survive this rehearsal is not ready.
Create a versioned copy of the intervention material and record its approval state. The experiment record should reference that version rather than storing sensitive conversation history inside the brief. If a version changes after launch, timestamp the change and treat affected records separately.
Write the owner for assignment, delivery, data extraction and final review. One person can hold several roles, but the work still needs named responsibilities. A missing review owner often turns a fixed window into an open-ended project.
Interpret small and incomplete cohorts cautiously
Show counts before percentages. A change from three renewals among ten eligible subscribers to five among eleven can look dramatic as a rate while remaining highly sensitive to a few people. Describe the observed difference and its uncertainty; do not label it a stable effect.
Keep missing outcomes visible. Separate not yet observable, no longer eligible, delivery failed and source unavailable. Removing every incomplete record after results appear can distort the denominator in whichever direction favours the hypothesis.
If cohorts differ materially in acquisition source, promise exposure or starting date, document the mismatch. The result may still guide a cleaner next test, but it should not be reported as a controlled comparison.
Close the experiment without outcome shopping
At the review meeting, read the original hypothesis, primary signal and decision rule before viewing supporting checks. Apply the pre-written outcome first. Then use supporting evidence to explain or refine, not to replace an unfavourable primary result.
Publish an internal closeout with eligible counts, execution coverage, observed result, confidence, confounders, workload and next action. Keep the null or challenging evidence. A record that documents why an idea stopped can be more useful than a weak positive story.
For a follow-up, change one element justified by the result and issue a new Experiment ID. Never merge consecutive tests into one larger “successful initiative” when their hypotheses, cohorts or interventions differ.
Check contamination before closing. Record whether comparison subscribers encountered the change through shared posts, routine replies or a parallel campaign, and whether experiment subscribers missed it. Report the affected counts instead of assuming assignment equals exposure. When exposure cannot be reconstructed, downgrade confidence and use the result to improve the next delivery record.
Keep a short decision log for excluded analyses. If someone proposes a new segment, later cutoff or different outcome after seeing results, record the question and schedule it as exploratory work. This preserves useful curiosity without allowing a post-result metric to replace the experiment's primary commitment.
Limitations and source notes
Limitations: small cohorts, changing acquisition mix, subscriber choice and incomplete platform data can make causal conclusions weak. This brief tests a local creator-controlled change; it does not establish a universal retention tactic or predict an individual subscriber's behaviour.
The pre-written hypothesis and review structure adapts the UK Government Service Manual guidance to plan research around clear questions. The copyable brief, evidence grades and decision outcomes are original SirenCY editorial tools.