Step 1
Gather every dated demand signal available with its source, what it actually measures, the audience and geography it covers, and the merchant's target market definition the estimate must serve.
Ordering decisions rest on partial signals, and it is unclear which figures were measured and which were inferred. In this case, the immediate task is to estimate product demand from available market signals. The workflow should produce a demand estimate with its confidence limits, flagging the inputs missing from the supplied records.
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 estimate product demand from available market signals.
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: [dated demand signals with sources and coverage notes, the target market definition, and the observed-versus-inferred status of each input component]. Convert the signals into a stated demand estimate that labels each component observed or inferred, keeps conflicting signals reported side by side with their sources instead of averaged, and carries a confidence range reflecting the coverage of the inputs.
Return a demand estimate with its confidence limits and the missing inputs flagged 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.
Gather every dated demand signal available with its source, what it actually measures, the audience and geography it covers, and the merchant's target market definition the estimate must serve.
Convert the signals into a stated demand estimate that labels each component observed or inferred, keeps conflicting signals reported side by side with their sources instead of averaged, and carries a confidence range reflecting the coverage of the inputs.
Deliver the demand estimate with its confidence limits and a list of the inputs missing from the supplied records, then verify that every observed component traces back to a dated source and every inferred component names the assumption behind it.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Signals measure interest with partial coverage, so a single figure implies precision the inputs do not have; the range reflects how much of the estimate rests on observation versus assumption, and it widens as the share of inferred components grows.
Both are reported with their sources, periods and coverage, and the disagreement is explained rather than averaged away, because an average of conflicting inputs hides the very uncertainty the estimate exists to expose; the range accounts for the spread between them.
Those that would change the estimate if supplied, such as conversion behavior or coverage outside the observed audience; they are listed with what each would resolve, so the merchant can see which data gap is worth filling before the next ordering decision.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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