5 AI workflows under the "Refund Analysis and Alerts" topic. Each includes a copyable prompt and FAQs. Paste them into your preferred AI assistant or Spark agent and run it.
Refunds are logged as single events with no link back to the order or the reason, so trends cannot be read. In this case, the immediate task is to analyze refund patterns and affected products. The workflow should produce a breakdown separating refund counts from refunded value and matching each refund to its order date to surface recurring clusters.
Products with very different order volumes are being compared on raw refund totals, which makes the ranking unreliable. In this case, the immediate task is to compare refund rates by product with consistent denominators. The workflow should produce a refund breakdown and evidence-based action list, using the merchant's units, orders and refund dates.
Refund reason codes and notes are scattered across individual orders, so repeated causes never surface as a pattern. In this case, the immediate task is to summarize recurring refund reasons. The workflow should produce a refund breakdown and evidence-based action list, grouped from the merchant's own refund records with their products and dates.
Refund volume is rising overall, but nobody can say whether it is spread evenly or driven by a few items. In this case, the immediate task is to identify products with concentrated refund activity. The workflow should produce a breakdown that weighs each product's refund share against its order share and turns the outliers into an action list.
Refunds tend to arrive in bursts, and the team cannot tell a real shift from a reporting delay. In this case, the immediate task is to detect unusual concentrations of refund activity. The workflow should produce a refund breakdown and evidence-based action list, tied to the merchant's dated refund events and their agreed thresholds and owners.
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