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Step 1
Start with dated sales, stock availability, promotions, seasonality, lead times and forecast horizon.
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Spark playbook scenarios · Spark: AI Store Assistant
Identify seasonal patterns in the sales history
A planner needs future demand estimates to guide purchasing but sales history is affected by promotions or stockouts. In this case, the immediate task is to identify seasonal patterns in the sales history. The workflow should produce a demand forecast with assumptions, ranges and review dates, using the merchant's actual records and constraints.
Topic
Sales and Demand Forecasting
Target keyword
shopify seasonal sales 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 seasonal patterns in the sales history.
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 dated sales, stock availability, promotions, seasonality, lead times and forecast horizon]. Prepare a baseline, identify distorted history, produce ranges rather than false precision and explain how forecast error will be evaluated.
Separate recurring seasonal patterns from one-off spikes and account for purchasing lead time.
Return a demand forecast with assumptions, ranges and review dates 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 planner needs future demand estimates to guide purchasing but sales history is affected by promotions or stockouts. In this case, the immediate task is to identify seasonal patterns in the sales history. The workflow should produce a demand forecast with assumptions, ranges and review dates, 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
Prepare a baseline, identify distorted history, produce ranges rather than false precision and explain how forecast error will be evaluated.
03
Step 3
Separate recurring seasonal patterns from one-off spikes and account for purchasing lead time.
04
Step 4
Deliver a demand forecast with assumptions, ranges and review dates; 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.
Are historical sales always equal to demand?
No. Stockouts can suppress recorded sales, while promotions can temporarily inflate them.
Why use forecast ranges?
Demand is uncertain. Ranges make planning tradeoffs visible and avoid treating a single estimate as guaranteed.
Why does timing matter for seasonal products?
Demand may peak before stock can arrive. Buying decisions need to account for sourcing and delivery lead time as well as the shopping season.
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Next step
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