A diagnostic workflow, prompts, scorecards and funnel board for finding friction from an approved creator clip to a first deposit. This is a human-reviewed operating kit, not software that connects to accounts or changes deposit flows automatically.
Map each touchpoint, separate measurement gaps from real friction, and assign the next diagnostic action.
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Use this only for a lawfully operated, appropriately licensed adult-facing product and permitted audience. This kit does not determine legal eligibility or replace local compliance review. Never target minors, self-excluded people or people showing vulnerability. Do not promise winnings, recover losses, disguise conditions or remove protective checks to improve conversion.
The objective is to identify technical friction and mismatched expectations—not pressure people to deposit. Retain age, identity, security and responsible-gambling controls. Use anonymized aggregate data; never upload IDs, payment details or customer credentials to an AI assistant.
Use a single cohort, market, campaign and observation window. Define FTD as the first successfully completed deposit for an eligible new customer under your reporting rules; do not count failed attempts or returning deposits as new FTDs.
Collect clip transcripts, approved offers, landing screenshots, event definitions, aggregate stage counts and payment error categories.
| Stage | Entry event | Exit event | Diagnostic question |
|---|---|---|---|
| Creative | View under stated platform definition | Tracked landing visit | Does the clip promise what the page provides? |
| Landing | Eligible landing session | Registration started | Are conditions and next steps clear? |
| Registration | Started | Completed | Is a technical field or unclear instruction blocking progress? |
| Verification | Required check started | Check completed | Are legitimate users told what is needed? |
| Deposit | Eligible deposit attempt | Successful first deposit | Is there a provider error or misunderstood condition? |
Track eligibility exclusions separately. Never divide events from different populations without acknowledging the mismatch.
Use an approved transcription tool, or provide a verified transcript. The AI can analyze supplied audio only if your tool supports it; otherwise attach text. Keep uncertain words marked and have a person verify offer amounts, conditions and subtitles.
Analyze the supplied clip transcript and scene notes. Return timestamp, spoken_claim, visual_claim, offer_condition, destination_expectation and ambiguity. Quote exact lines. Mark inaudible material instead of guessing. Compare each promise with the approved offer sheet. Flag misleading certainty, missing material conditions or a mismatch with the landing page. Do not create gambling inducements or profit guarantees.
OUTPUT: a time-coded creative-to-page mismatch list. Do not diagnose conversion from a clip alone.
For each comparable stage, conversion=exits/entries and drop-off=1−conversion. A missing count is unknown, not zero. If entries=0, report not applicable. Include the data window and deduplication rule.
Illustrative cohort: 1,000 eligible landing sessions → 200 registration starts → 160 completions → 120 verified customers → 60 deposit attempts → 48 successful first deposits. Stage conversions: 20%, 80%, 75%, 50%, 80%; landing-to-FTD=4.8%. These are invented teaching numbers, not benchmarks.
Counts can reveal where to investigate; they do not reveal why people chose not to proceed.
Using the supplied aggregate cohort counts and event definitions, calculate stage conversion and drop-off with explicit numerators and denominators. Check that stages refer to the same cohort and window. Flag returning users, delayed events, duplicate events and missing data. Rank investigation areas by observed loss and evidence quality. Label suspected causes as hypotheses. Return the calculation and the additional evidence needed for each hypothesis.