01
Step 1
Gather monthly orders and refunds for the requested window with the currency rules, the store timezone and the comparison periods the merchant expects to see.
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Spark playbook scenarios · Spark: AI Store Assistant
Produce a comparable monthly sales report
Months are compared side by side even though they contain different numbers of selling days. In this case, the immediate task is to produce a comparable monthly sales report. The workflow should produce a sales report with definitions and reconciliation notes, using the merchant's monthly orders, refunds and currency rules.
Topic
Sales and Store Analytics
Target keyword
shopify monthly sales 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 produce a comparable monthly sales report.
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: [monthly orders and refunds for the requested window with the currency rules, the store timezone, and the comparison periods the merchant expects to see]. Mark each month as complete or partial using the timezone boundary, count the trading days in each and compare periods on a daily rate where the day counts differ, and exclude from the trend comparison any month distorted by a launch, a store closure or a reporting change.
Return a sales report with definitions and reconciliation notes 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
Mark each month as complete or partial using the timezone boundary, count the trading days in each and compare periods on a daily rate where the day counts differ, and exclude from the trend comparison any month distorted by a launch, a store closure or a reporting change.
03
Step 3
Deliver the monthly report with the definition block, the completeness and trading-day notes, the comparison basis used and a reconciliation against the order export, then verify that the sum of the months equals the window total and that no partial month entered a trend line.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Should months be compared on raw totals?
Only when length and trading conditions match. Where selling days differ, the report compares daily rates and shows the day count beside the total, because a shorter month otherwise reads as a decline that never happened.
How is the closing month handled?
It is labelled partial with the number of days it covers and kept out of trend comparisons and rankings, because including it makes the current month look weaker than it is and repeats the same distortion every period the report is produced early.
Is the same month last year a fair benchmark?
It is the most common benchmark, but only after confirming promotions, trading days and assortment were comparable. Where last year carried a campaign this year did not, the report says so beside the comparison rather than presenting the gap as performance.
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