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Track average days to return delivery

Measure return transit time by carrier and reason. See the gap between RA opened and unit received, and hold reverse logistics SLAs accountable.

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The prompt

I want to analyze return delivery performance across our carrier network. Can you build me a flow that pulls return initiation and delivery timestamps from Frate Returns, calculates average transit days segmented by carrier and return reason, and visualizes SLA performance in Parabola?

Just copy and paste the prompt into a new Parabola flow to get started.

What Parabola builds

A workflow with six steps you can edit:

1. Pull return events from your returns platform. Frate Returns, Loop, Happy Returns, or a 3PL portal. Pull return authorization opened, label scanned at origin, delivered at the receiving warehouse.

2. Pull carrier scans. Optional but useful. UPS, USPS, FedEx, or DHL scans fill in the in-transit milestones between origin and receive.

3. Calculate transit days. Days between RA opened and label scanned. Days between scanned and delivered. Days between delivered and receipt confirmation if your WMS records it separately.

4. Segment the result. By carrier, return reason, originating region, item category, and ship method. The slices are how you find the broken lane.

5. Compare against SLA. Carrier promised five days. Actual was eight. Flag every lane that drifted, and surface the longest tails so finance can argue the credit.

6. Output the dashboard. Average and median days by carrier, by reason, by region. A trend line for the last 12 weeks. A flagged list of returns still in transit past SLA.

Why teams stop doing this manually

A return looks closed the moment a customer hits "start a return." It is not. The unit is somewhere in the carrier network, the credit has not been issued, and the inventory will not show up in receivable stock for days. The lag is real money. It is also invisible until somebody pulls the numbers.

The manual version is one person exporting from the returns platform, joining a carrier export, and aggregating by reason. They do it once for a quarterly review, hand the deck around, and the lanes that need fixing are already six weeks old by the time anyone sees the chart. Reverse logistics rarely gets the same operational rigor as outbound, so the bad lanes persist.

When the metric is live, the conversation changes. Finance can size the dollar exposure of inventory in transit. CX can give customers an accurate expected refund date. Operations can call the carrier with a specific lane and a specific number, not a complaint.

How it works

Step 1. Paste the prompt.

Open Parabola, paste the prompt in section 2, and let it ask follow-up questions about which platform records your RA timestamps and which carriers handle your reverse legs.

Step 2. Connect your data.

API connections to your returns platform and, if you have them, your carrier portals. The flow joins return events to carrier scans automatically.

Step 3. Run it weekly.

The flow recalculates transit time, updates the segment cuts, and refreshes the dashboard. Add a new carrier or a new return reason and the report includes it on the next run.

Related prompts

FAQ

Does this work without a dedicated returns platform?

Yes. The minimum input is two timestamps per return: when it was authorized and when it was received. Those can come from Shopify, a 3PL portal, or a spreadsheet your CX team maintains. Frate Returns is the canonical source if you have it.

How does the flow handle returns that never arrive?

They sit on a flagged list with the days-in-transit count. Most teams set a threshold, after which the unit is presumed lost and the value is written off. The flow surfaces them so finance can size the exposure.

Can the flow split by item category or SKU?

Yes. Add the category or SKU column at the join step. The dashboard segments automatically.

Why segment by carrier rather than reason?

Both segments are useful for different decisions. Carrier explains transit performance and lets ops have a specific conversation with the partner. Reason explains customer-side patterns and informs product or sizing changes.

How is this different from a returns platform's built-in report?

The built-in report covers what the platform sees. This flow joins platform events to carrier scans, WMS receipt records, and your finance write-off thresholds. That gives finance and ops one number they can both reference instead of three reports that disagree.
Know how long a return actually takes.
Paste the prompt, connect your returns platform, and let the transit-time analysis run on its own.
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