The store has never been walked end to end as a shopper would, so friction is suspected in several places and confirmed in none. In this case, the immediate task is to audit the shopping journey for conversion barriers. The workflow should produce a conversion diagnosis and experiment backlog, separating supported barriers from unproven assumptions.
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
Act as a Shopify operations specialist.
Help me audit the shopping journey for conversion barriers.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [the ordered journey steps from entry to order confirmation, sessions entering and leaving each step for the defined period, the conditions each step presents including required fields and the shipping and payment options shown, device splits, and any checkout or error records for the same window]. Judge each suspected barrier against the step counts for that period and against a comparable earlier window the merchant names, calling it supported only where the records show the loss or the condition, and listing anything else as a hypothesis together with the record that would settle it.
Return a conversion diagnosis and experiment backlog with each finding checked against the supplied records for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Define the journey as the ordered steps a shopper takes from entry to order confirmation, recording for one merchant-defined period the sessions entering and leaving each step, the conditions each step presents such as the fields required, the shipping and payment options shown and the device, plus any checkout or error records the merchant holds for the same window.
Judge each suspected barrier against the step counts for that period and against a comparable earlier window the merchant names, calling it supported only where the records show the loss or the condition, and listing anything else as a hypothesis together with the record that would settle it.
Deliver a conversion diagnosis and an experiment backlog listing supported barriers and hypotheses in separate sections, each with the step, the evidence, the check that would confirm it and a proposed test, then reconcile the entry count of the first step with the merchant's session record and confirm every supported barrier names the record it rests on.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The journey is divided wherever the shopper must act to continue, such as selecting a variant, entering an address or choosing a payment method, and every step is written down before any counts are pulled. Fixed boundaries keep the audit repeatable; steps with no recorded event are marked as unmeasured instead of being filled with an estimate.
A friction point is confirmed only when a record supports it, meaning the step counts for the period show the loss, or a condition such as an error is documented. Confirmation is about evidence, not severity; a step that feels slow but shows no loss is reported as a hypothesis with the record that would test it.
Hypotheses are listed in their own section with the observation behind them and the record that would confirm or drop them, never mixed into the supported findings. Keeping them apart protects the ranked list from opinion; once the merchant adds the missing tracking for that step, a hypothesis can be retested and moved across.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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