Discount impact on order value
Tracks orders that applied a discount code and their basket totals, code by code.
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.
- 1
Test whether discounts grow baskets
Coming soonBlu builds a step
About this recipe
When to use it
A discount that raises AOV isn't automatically a win if it's just moving purchases you'd have gotten anyway. This shows whether discount usage actually grows basket size or just shifts existing demand to the discount window.
What it delivers
Compares AOV and margin for discounted vs. non-discounted orders, surfacing whether promotions genuinely grow the basket or just redistribute demand to discounted periods.
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
“Tracks orders that applied a discount code and their basket totals, code by code.”
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.
- 01Test whether discounts grow basketsCreate an Insights report called "Discount Impact on AOV". Series A: Event "Placed order" filtered to orders with a linked discount_applied event (orders with discount), aggregation: Average of total_price, label: "AOV with Discount" Series B: Event "Placed order" filtered to orders with NO linked discount_applied event, aggregation: Average of total_price, label: "AOV without Discount" Series C: Computed: Series A − Series B, unit: $, label: "AOV Lift from Discount" Series D: Computed: Sum of discount_applied.amount across all discounted orders, unit: $, label: "Total Discount Cost" Series E: Computed: (Sum of total_price for discounted orders − Series D) − (count of discounted orders × Series B), label: "Net Revenue Impact": answers: did discounts grow the pie or just shift it? Time granularity: Weekly Time range: Last 12 weeks Breakdown: By "code" property on discount_applied (top 10 codes by usage volume) Compare: Previous period (prior 12 weeks) Chart type: Dual-axis: left axis AOV ($) showing Series A and Series B side-by-side bars per discount code, right axis Series E (net revenue impact) as a line Annotations: - Flag any discount code where AOV with discount is LOWER than AOV without (the discount is being used by lower-intent buyers: pure margin loss with no basket-growth benefit). - Flag any discount code with negative Series E (Net Revenue Impact): this code is cannibalizing full-price demand more than it's creating new orders. - Highlight discount codes where AOV with discount > AOV without (the discount actually grows the basket: keep these and scale). - Surface the trailing-12-week total discount cost as a % of total revenue (industry benchmark: <15% for healthy DTC; >20% indicates over-reliance on promotions). Use case: the cannibalization question is the most important and most-avoided ecommerce analysis. Most brands track discount usage but never measure whether discounts CREATE orders that wouldn't otherwise happen, vs. just SHIFTING demand to discounted moments. This recipe makes the distinction visible. Taxonomy notes: - discount_applied has amount, code, currency, customer_id, order_id, type (and amount_off / percent_off depending on source). - Placed order has total_price (Shopify) and items. - Linking an order to a discount uses discount_applied.order_id.
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 /discount-impact-on-aov-and-margin 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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