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Step 1
Start with ticket timestamps, statuses, categories, channel, staffing context and satisfaction responses.
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Spark playbook scenarios · Spark: AI Store Assistant
Analyze support performance and recurring topics
A manager needs to understand support efficiency and recurring problems without relying on incomplete averages. 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, using the merchant's actual records and constraints.
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: [provide ticket timestamps, statuses, categories, channel, staffing context and satisfaction responses]. Define metrics, separate first response from resolution and inspect distributions and recurring topics.
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.Scenario & problem
When does this scenario usually come up?
Start with the business context, the trigger, and the expected output before moving into the execution steps.
A manager needs to understand support efficiency and recurring problems without relying on incomplete averages. 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, using the merchant's actual records and constraints.
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 metrics, separate first response from resolution and inspect distributions and recurring topics.
03
Step 3
Deliver a service performance report and improvement priorities; verify the result against the supplied records and identify unresolved inputs.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Why distinguish response time from resolution time?
A fast initial reply does not mean the customer's problem was solved quickly.
Can satisfaction averages be biased?
Yes. Respondents may not represent every customer, and response volume matters when interpreting changes.
What information is needed to investigate this use case?
Start with ticket timestamps, statuses, categories, channel, staffing context and satisfaction responses. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
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Next step
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