Support tickets and reviews describe the same fault in different words, so a real pattern can look like scattered one-off complaints. In this case, the immediate task is to detect recurring product quality concerns. The workflow should produce alert conditions with the evidence behind each, checked against the dated review and complaint entries 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 detect recurring product quality concerns.
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 review, ticket and complaint entries with fault descriptions, product and variant identifiers, order references, per-product sales volume, the merchant's stated alert threshold, and the fixed fault-attribute list]. Normalize fault descriptions onto a fixed attribute list and cluster entries that mention the same attribute on the same product, treating a pattern as recurring only when independent buyers report it inside the window and routing single-report products to a watch list instead of an alert.
Return alert conditions with the evidence behind each, with flagged products rechecked against the merchant's 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.
Collect every review, support ticket and complaint entry for the review window with its date, order reference, product and variant identifiers, and the customer's own words describing the fault, plus the merchant's stated alert threshold in reports per product and each product's sales volume so complaint counts can be read against exposure.
Normalize fault descriptions onto a fixed attribute list and cluster entries that mention the same attribute on the same product, treating a pattern as recurring only when independent buyers report it inside the window and routing single-report products to a watch list instead of an alert.
Deliver an alert sheet per flagged product listing the attribute, the count of independent reporters, the earliest and latest report dates, the matched entry identifiers and the exposure-adjusted rate, then reconcile every quoted entry back to the dated review and complaint records before publishing.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Entries are mapped onto a fixed attribute list so a seam splitting and stitching coming apart all resolve to the same fault; the rule keeps near-identical complaints from hiding as one-offs, and an entry whose wording matches no attribute stays unclustered until a human names it.
Only independent buyers count toward the threshold, so repeat contacts from the same buyer about one order collapse into a single report; the boundary case is a buyer reporting the same fault across multiple orders, which counts once per order and is noted on the alert sheet.
Every alert lists the identifiers of the entries behind it, and the checker confirms each one exists in the dated records with the quoted fault and date; an alert whose citations cannot be matched is withdrawn and logged rather than left standing.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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