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Order status flow

Shows daily order volume alongside how many get fulfilled, refunded or cancelled, and the rate for each.

Authored by Intempt Technologies

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1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.

  1. 1

    Track orders through their states

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    Blu builds a step

About this recipe

When to use it

A spike in refunds or cancellations is easy to miss order by order but obvious as a trend line. This tracks the distribution of every order across created, fulfilled, refunded, and cancelled states over time.

What it delivers

Tracks the distribution of orders across created, fulfilled, refunded, and cancelled states over time, giving one operational view of order flow health.

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 daily order volume alongside how many get fulfilled, refunded or cancelled, and the rate for each.”

recipe.md preview

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Inputs & outputs

What the recipe needs before it runs, and what it leaves in your workspace.

Inputs

  1. 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.

    1. 01Track orders through their statesCreate an Insights report called "Order Status Flow". Series A: Event "Placed order", aggregation: Count, label: "Created" Series B: Event "Order fulfilled", aggregation: Count, label: "Fulfilled" Series C: Event "Order refunded", aggregation: Count, label: "Refunded" Series D: Event "Order cancelled", aggregation: Count, label: "Cancelled" Series E: Computed: Series B / Series A × 100, unit: %, label: "Fulfillment Rate" Series F: Computed: Series C / Series A × 100, unit: %, label: "Refund Rate" Series G: Computed: Series D / Series A × 100, unit: %, label: "Cancellation Rate" Time granularity: Daily (with weekly rollup option) Time range: Last 30 days Breakdown for Series A: By "fulfillment_status" property on Order fulfilled where present (fulfilled, partial, etc.) Compare: Previous period (prior 30 days) Chart type: Stacked column chart for absolute counts (Series A: D) over time, with Series E/F/G as overlay lines on a secondary axis showing the rates Annotations: - Add benchmarks: healthy fulfillment rate is 95%+; 90: 95% indicates a backlog or capacity constraint; <90% suggests systemic operational issue. - Add benchmarks: refund rate <3% is healthy DTC; 3: 8% varies by category (apparel typically higher); >10% suggests product-quality or fit-prediction issues. - Add benchmarks: cancellation rate <2% is healthy; >5% suggests checkout-conversion issues (customers regret the purchase quickly) or fraud-detection cancellations. - Flag any day where Series E (fulfillment rate) dropped >5 points vs. trailing-7-day average (operational issue requiring same-day investigation). - Highlight the gap between Created and Fulfilled in the most recent days: this is the in-flight backlog. Growing gap = capacity constraint. - Surface absolute volumes (orders today, fulfilled today, refunded today, cancelled today) as headline callouts. Use case: the operational pulse for ecommerce ops/fulfillment teams. Surfaces both the volume picture (am I getting more orders?) and the quality picture (am I delivering them?) on a single canvas. Taxonomy notes: - All four order events are canonical: Placed order, Order fulfilled, Order refunded, Order cancelled. - Order fulfilled.fulfillment_status is a real property (values vary by integration source: typically "fulfilled", "partial", "pending"). - For accurate rate computation, Series E/F/G should match against orders that had time to fulfill (e.g., exclude orders <48h old when computing fulfillment rate, since they may still be legitimately in pre-fulfillment).

    Install Intempt and run it

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      Sign up

      Create a free Intempt workspace.

    2. 2

      Connect your data

      Add the sources this recipe reads from.

    3. 3

      Ask Blu

      Type /order-status-flow in Blu once this recipe is live there.

    4. 4

      Review and approve

      Blu builds each step and waits for your OK before anything goes live.

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