01
Step 1
Collect the ticket timestamps per channel, the agent and automation events on each thread, the business-hours calendar in force, the satisfaction responses linked to each ticket, and the merchant's stated reopen window.
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Spark playbook scenarios · Spark: AI Store Assistant
Measure support response times consistently
Agents argue about whether targets were met because one dashboard counts the automated acknowledgement and another counts the final reply. In this case, the immediate task is to measure support response times consistently. The workflow should produce a service performance report and improvement priorities using one documented definition of each measure.
Topic
Customer Service Analytics
Target keyword
shopify support response time report
Copyable AI prompt
A prompt you can drop directly into Spark
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
ai-prompt.txt
Act as a Shopify operations specialist.
Help me measure support response times consistently.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [ticket timestamps by channel, agent and automation events on each thread, the business-hours calendar, satisfaction responses, and the merchant's reopen window]. Define first response as the first public reply written by an agent, excluding automated acknowledgements and internal notes, and resolution as the first move into a resolved status that is not reopened inside that window, then compute both in business hours and in elapsed hours so the two sit side by side.
Return a service performance report and improvement priorities for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.How to solve
A recommended resolution workflow
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
02
Step 2
Define first response as the first public reply written by an agent, excluding automated acknowledgements and internal notes, and resolution as the first move into a resolved status that is not reopened inside that window, then compute both in business hours and in elapsed hours so the two sit side by side.
03
Step 3
Deliver the service performance report with the definition sheet attached, then hand-recompute a sample of tickets from the raw timestamps and confirm the report totals match the ticket export before circulating it.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Does an automated acknowledgement count as first response?
No, only a public reply written by an agent counts, because the acknowledgement tells the customer nothing about their case; when an automated flow resolves a ticket with no agent involved, that ticket leaves the first-response measure and is reported separately with its resolution time.
How are tickets reopened later treated?
Every reopen is flagged, and a reopen inside the stated window restarts the resolution clock while one outside it starts a new measurement attached to the original; whichever rule is chosen, one rule applies to all tickets so the totals stay additive.
Which timezone governs business hours?
The support team's own calendar timezone applies to every channel, so a ticket from another region is measured against the hours the team was actually staffed; regional expectations are noted separately rather than changing the clock per customer.
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