The merchant must choose which part of a broad assortment deserves the next sourcing budget. In this case, the immediate task is to compare demand and competition across product categories. The workflow should produce a category comparison with the reasoning for each ranking, drawn from the dated demand, competitor and cost records supplied.
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 compare demand and competition across product categories.
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: [category definitions with product boundaries, dated demand evidence from a consistent window, competitor listing density per category, and the stated cost and delivery constraints].
Compare the categories on demand signal, seller density and margin potential using the same evidence window and source for all of them, separating observed figures from estimates and marking any category whose evidence base is thinner than the others.
Return a category comparison with the reasoning for each ranking, marking where the evidence is thin 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 category definitions with their product boundaries, dated demand evidence per category from a consistent window and source, competitor listing density per category, and the merchant's stated cost and delivery constraints for the next sourcing budget.
Compare the categories on demand signal, seller density and margin potential using the same evidence window and source for all of them, separating observed figures from estimates and marking any category whose evidence base is thinner than the others.
Deliver a category comparison with the reasoning for each ranking shown dimension by dimension, then verify that every figure quoted traces to the supplied records and that the evidence window is identical across all ranked categories.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Because the comparison decides where the next sourcing budget goes, and category-level demand and seller density show structure that single-product checks miss; product selection then happens inside the winning category instead of scattering budget across the assortment.
Mixing evidence windows or sources between categories, which makes the rankings an artifact of the data rather than the market; the check before delivery confirms one window and one source across every category, and any exception is either corrected or the category is unranked.
They are ranked with a thin-evidence mark naming the missing data, or left unranked when the gap is too wide; the rule keeps a weakly evidenced category from occupying a confident position that would direct budget on unexamined grounds.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.