The merchant knows several parts of the store underperform but cannot decide what to fix first. In this case, the immediate task is to recommend store improvements tied to observed evidence. The workflow should produce recommendations ordered by expected impact, each tied to the gap it addresses and to a check that would prove it worked.
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 recommend store improvements tied to observed evidence.
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 observed gaps with their page, template or product, the metric, window and segment showing each gap, the merchant's stated constraints including theme limits, staffing and channel mix, and any agreed target for those pages]. Tie each recommendation to the specific evidence behind it, rank them by expected impact on the merchant's stated objective, describe effort only in relative terms drawn from the merchant's own context, and set aside every page whose records support nothing stronger than a hypothesis.
Return the recommendations ordered by expected impact, with the check that would prove each one worked 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.
Collect the observed gaps with the page, template or product each sits on, the metric, window and segment showing it, the merchant's stated constraints such as theme limits, staffing and channel mix, and any agreed target for the pages involved.
Tie each recommendation to the specific evidence behind it, rank them by expected impact on the merchant's stated objective, describe effort only in relative terms drawn from the merchant's own context, and set aside every page whose records support nothing stronger than a hypothesis.
Deliver the ordered recommendation list naming per item the gap addressed, the evidence quoted, the relative effort and the check that would prove it worked, including the window and comparison record that make the result legible.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
No. Work concentrates where the evidence shows a gap, such as one template or product set, and the rest is left untouched until those results hold, otherwise the later check cannot attribute the recovery to any single change.
In relative terms the merchant already recognises, tied to constraints it stated such as theme access, staffing or app dependencies; hours, costs and uplift figures are never invented, because fabricated precision invites false confidence.
It becomes a hypothesis with the missing record named and is collected before any ranking, because acting on sparse data risks crediting a normal fluctuation to a change that made no measurable difference.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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