Reviewing complaints one channel at a time hides a fulfilment fault that arrives through both email and chat, leaving the root cause unowned. In this case, the immediate task is to organize customer complaints by issue and ownership. The workflow should produce a grouped complaint view inside a support operating workflow based on issue type and handler.
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 organize customer complaints by issue and ownership.
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: [complaint cases with arrival date, channel, order or product reference, recorded issue type, status and handler, plus the merchant's follow-up interval]. Group cases by underlying cause rather than by channel, name one accountable owner per group alongside the team that must act, and mark cases carrying no issue type or sitting unchanged past the merchant's stated follow-up interval.
Return a grouped complaint view inside a support operating workflow 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 complaint with its arrival date, the channel it came through, the order or product it references, its recorded issue type, its current status and the person handling it, collapsing repeat contacts from the same customer about the same order into one case.
Group cases by underlying cause rather than by channel, name one accountable owner per group alongside the team that must act, and mark cases carrying no issue type or sitting unchanged past the merchant's stated follow-up interval.
Deliver the grouped view inside the support workflow with a status column per case and a count per group, then reconcile the case count against the complaint export and confirm no group is left without a named owner.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
They become one case with every channel and date recorded on it, so the count reflects distinct problems rather than persistence; a genuinely different issue from the same customer stays a separate case linked to the first, and the link is kept so the history is not lost.
The team able to change the cause owns it, and the other team is named as a contributor with the fact it must supply; splitting ownership between two teams leaves the case unowned in practice, which is exactly what the grouping is meant to prevent.
The current state, the date it last changed, and the next action with its owner, so a case that has stopped moving is visible without reading the thread; cases older than the merchant's stated follow-up interval are flagged, not closed automatically.
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.