01
Step 1
Export every ticket in the period with its created, first-agent-reply and resolved timestamps, its status history, the category and channel recorded at creation, plus the agent roster and working hours actually in force in each week.
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Spark playbook scenarios · Spark: AI Store Assistant
Analyze support performance and recurring topics
The monthly summary reports a single handling average, concealing both tickets that sit untouched for days and themes repeating week after week. In this case, the immediate task is to analyze support performance and recurring topics. The workflow should produce a service performance report and improvement priorities built from ticket-level timestamps.
Topic
Customer Service Analytics
Target keyword
shopify customer service analytics
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 analyze support performance and recurring topics.
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-level created, first-reply and resolved timestamps with status history, ticket category and channel, weekly staffing rosters, and working hours]. Report first response and resolution as distributions with the median, the share of tickets past the merchant's stated response target and the oldest still-open ticket, and group recurring topics by the category recorded at creation so a theme counts once per ticket rather than once per message.
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
Report first response and resolution as distributions with the median, the share of tickets past the merchant's stated response target and the oldest still-open ticket, and group recurring topics by the category recorded at creation so a theme counts once per ticket rather than once per message.
03
Step 3
Deliver the service performance report with improvement priorities naming the topic, the channel where it concentrates and the owner, then reconcile the report's ticket count against the export and list tickets with no category or no resolution timestamp.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
How is a recurring topic counted?
Once per ticket, using the category recorded when the ticket was created, so a long thread cannot inflate a theme; tickets reclassified during handling count under their final category with the change noted, and uncategorised tickets sit on a separate line.
Are unresolved tickets part of the report?
They count toward volume and backlog but not toward the resolution distribution, since they have no resolution time yet; their age is reported separately, and leaving them out of the backlog would understate the queue the team is carrying.
How do staffing changes affect period comparison?
Periods are normalised by agent-hours available, so a week with half the roster is not read as a fall in performance; weeks where coverage fell below the merchant's stated minimum are flagged and excluded from the trend rather than compared.
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