Refund rate by product
Shows which products get refunded most often and which ones are getting worse, over the last 90 days.
Authored by Sid Chaudhary
Founder & CEO at Intempt
Agentic workflow
Coming soon1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.
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Find the most refunded products
Coming soonBlu builds a step
About this recipe
When to use it
A blended refund rate hides the one SKU quietly causing most of it. This breaks refund rate down by individual product and category, compared to the previous period, and flags products with a rising rate as a likely quality issue.
What it delivers
Tracks refund rate broken down by individual product and category from real order line items, compared against the previous period and flagged when a product's rate is rising.
How you prompt this
Describe the outcome in your own words and Blu Agent maps it to this recipe, or run it by name. It drafts a plan first and nothing runs until you approve it.
Tell Blu Agent what you want
“Shows which products get refunded most often and which ones are getting worse, over the last 90 days.”
recipe.md preview
Built from the published catalog. Blu Agent's full step prompts load in your workspace when you run it.
Inputs & outputs
What the recipe needs before it runs, and what it leaves in your workspace.
Inputs
- Nothing declared beyond an Intempt workspace. Blu asks for anything else it needs as it goes.
Outputs
How the recipe runs
Blu follows the steps in order and shows you each one before anything goes live.
- 01Find the most refunded productsCreate an Insights report called "Refund Rate by Product". Series A: Event "Order refunded", aggregation: Count Series B: Event "Placed order", aggregation: Count Formula: (A / B) × 100, unit: %, label: "Refund Rate" Breakdown: By product_id (extracted from Placed order.items, which flattens product_id, title, quantity, price, sku): top 20 products by absolute refund count Time range: Last 90 days Compare: Previous period (prior 90 days) Chart type: Bar chart sorted by refund rate descending, secondary view grouping the same products into product-category buckets if the Products object has category metadata available Annotations: - Flag any product whose refund rate exceeds 8% (typical apparel/consumer-goods quality threshold). - Flag any item whose refund rate increased by more than 3 percentage points vs. the prior period. - Highlight items with both rising rate AND rising volume: the highest-priority quality issues. Taxonomy notes: - Placed order.items contains product_id, title, quantity, price, sku per line item. Resolve product to category by joining product_id against the Products record-object (Products has 27 attributes including category metadata). - Order refunded carries order_id (relation to Orders), refund_amount, reason, and Order total. Reason text is unstructured; not used for grouping here.
Install Intempt and run it
- 1
Sign up
Create a free Intempt workspace.
- 2
Connect your data
Add the sources this recipe reads from.
- 3
Ask Blu
Type /refund-rate-by-product-and-category in Blu once this recipe is live there.
- 4
Review and approve
Blu builds each step and waits for your OK before anything goes live.
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