Specific buyer questions bring the most qualified visitors, yet the content plan still covers only broad head terms. In this case, the immediate task is to identify specific long-tail search questions. The workflow should produce a keyword-to-page map and research priorities checked against the merchant's own catalog.
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 identify specific long-tail search questions.
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 catalog with attributes and variants, the support questions and customer language on record, the audience and market, and any keyword evidence available]. Keep phrases that name a concrete need, product, attribute or situation and can be answered by a specific catalog item or existing page, separate them from broad category words, and mark as unmeasured demand any specific phrase the merchant's records do not yet show being searched.
Return a keyword-to-page map and research priorities, checking each long-tail term 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.
Gather the catalog with its attributes and variants, the support questions and customer language the merchant has on record, the audience and market, and any keyword evidence available, keeping every candidate phrase with the record it was drawn from.
Keep phrases that name a concrete need, product, attribute or situation and can be answered by a specific catalog item or existing page, separate them from broad category words, and mark as unmeasured demand any specific phrase the merchant's records do not yet show being searched.
Deliver a keyword-to-page map with each specific phrase's matched item or page, the question it answers and its measured or unmeasured status, then verify that every retained phrase traces to a catalog item or a recorded customer question and that no broad head term was left in the list.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
It has to name something a buyer would actually ask about: a product, an attribute, a use case or a situation; a single category word stays on the head-term side, and a phrase that only adds a modifier without changing the answer is treated as a variant of the head term.
From the catalog attributes, support tickets, review language and the audience notes the merchant already holds, because those show how buyers describe the need; invented phrasings are excluded, or labelled as hypotheses when the merchant wants them tested.
Only when no existing page answers it well and the catalog can carry the topic on its own; where several phrases share one intent they extend a current page, and a phrase needing a page the merchant will not build is recorded as a content request rather than a live priority.
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.