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Analyze order issue rates across support & fulfillment

Tie support tickets back to orders, SKUs, carriers, and channels. Surface the issue rate that actually explains where your fulfillment is breaking.

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The prompt

I want to analyze order issue rates across our support and fulfillment data. Can you build me a flow that pulls tickets from Zendesk and order data from ShipHero and Shopify, tags each ticket by issue type, calculates issue rates by SKU, carrier, and channel, and surfaces the top problem areas?

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

What Parabola builds

A workflow with seven steps you can edit:

1. Pull tickets from Zendesk. Open and recently closed tickets, with subject, body, tags, requester, and the order number the agent attached.

2. Pull orders from Shopify and shipments from ShipHero. Order line items, SKU, carrier, ship date, delivery date, channel.

3. Match tickets to orders. Join by order number first, by email and date as a fallback. Tickets without a matchable order land on a review pile.

4. Tag each ticket by issue type. Late delivery, missing item, damaged, wrong SKU, address change, return request, billing. The classifier reads subject and body and assigns the type.

5. Calculate issue rates. Tickets per 100 orders, sliced by SKU, carrier, channel, warehouse, ship method. The denominator is shipments in the same period.

6. Surface the top problems. Rank by rate and by absolute volume. A 12% issue rate on a SKU you ship 30 times a month matters less than a 2% rate on a SKU you ship 5,000 times.

7. Output the report. Top problem SKUs, top problem carriers, top problem lanes, and a trend line so the team can see whether last week's fix moved the number.

Why teams stop doing this manually

Every ops team can tell you which SKU has been a headache lately. What they cannot tell you, without a week of work, is the actual rate. Tickets sit in Zendesk. Shipments sit in ShipHero. Orders sit in Shopify. The numerator and the denominator live in three places and never line up cleanly.

The manual version is one person pulling exports on a Friday afternoon, joining them in a spreadsheet, and emailing a deck on Monday. It works once. It does not work as a weekly cadence. By the time the deck circulates, the carrier is already missing the next week's deliveries and the wrong SKU is already shipping from the wrong warehouse.

When the rate is visible by SKU, by carrier, and by channel, the team can act. They pull the bad SKU from the bad warehouse. They route the bad lane to a different carrier. They flag the channel feeding back the most damage tickets. The work shifts from arguing about anecdotes to fixing the top three offenders.

How it works

Step 1. Paste the prompt.

Open Parabola, paste the prompt in section 2, and let it ask follow-up questions about your Zendesk tagging, your ShipHero account structure, and how you slice channels in Shopify.

Step 2. Connect your data.

API connections to Zendesk, ShipHero, and Shopify. Optional: your carrier portal, your 3PL exports, and a returns platform if returns drive your top ticket type.

Step 3. Run it on a schedule.

Daily, weekly, or every Monday morning before standup. The flow regenerates the full ticket-to-order join, recalculates the rates, and posts the top problem list.

Related prompts

FAQ

What if a ticket has no order number on it?

The flow attempts a fallback match on email plus date plus product, and routes anything still unmatched to a review pile. Most teams find this pile is small and the lessons from it improve the agent's tagging habits.

How does the classifier know which issue type to tag?

It reads the ticket subject, body, and existing tags. You can also feed it your Zendesk macro library so the model learns your team's specific labels. The classifier output is stored as a column you can edit before it rolls up.

Does this work with Gorgias or Kustomer instead of Zendesk?

Yes. The ticket source step changes, the rest of the flow does not. Same join logic, same classifier, same output.

How is this different from a Zendesk dashboard?

A Zendesk dashboard shows ticket volume. This flow shows ticket rate per shipment, sliced by the dimensions ops can act on. That difference is the difference between "we had a rough week" and "the issue is this carrier on this lane."

Can the flow alert when an issue rate spikes?

Yes. Set a threshold per slice. The flow posts a Slack alert when a SKU, carrier, or channel rate crosses it.
See the issue rate where it actually breaks.
Paste the prompt, connect Zendesk and your order systems, and let the rate analysis run on its own.
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