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Step 1
Start with consistent sales and order data, discounts, product mix and comparison periods.
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Spark playbook scenarios · Spark: AI Store Assistant
Identify the segments driving changes in AOV
A merchant sees changes in average order value but does not know which products or customer groups explain them. In this case, the immediate task is to identify the segments driving changes in AOV. The workflow should produce an AOV analysis and targeted improvement hypotheses, using the merchant's actual records and constraints.
Topic
Average Order Value Analysis
Target keyword
shopify aov analysis
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 identify the segments driving changes in AOV.
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 consistent sales and order data, discounts, product mix and comparison periods]. Define the numerator and denominator, calculate AOV by relevant segment and separate mix shifts from within-segment changes.
Return an AOV analysis and targeted improvement hypotheses 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 merchant sees changes in average order value but does not know which products or customer groups explain them. In this case, the immediate task is to identify the segments driving changes in AOV. The workflow should produce an AOV analysis and targeted improvement hypotheses, 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 the numerator and denominator, calculate AOV by relevant segment and separate mix shifts from within-segment changes.
03
Step 3
Deliver an AOV analysis and targeted improvement hypotheses; 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.
What does average order value measure?
It divides a defined sales amount by the corresponding number of orders. The sales definition and order exclusions must be stated.
Can a higher AOV hide a problem?
Yes. AOV can rise while order count falls or discounts reduce margin. Review it alongside other business measures.
What information is needed to investigate this use case?
Start with consistent sales and order data, discounts, product mix and comparison periods. 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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