01
Step 1
Gather the holiday calendar with each shopping window's opening and closing dates, prior-year sales by period from the merchant's records, the candidate products with supplier lead times, and the store's margin rules for seasonal buys.
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Spark playbook scenarios · Spark: AI Store Assistant
Identify relevant product opportunities for upcoming holidays
A holiday shopping window closes on a fixed date, so a good product that lands late contributes nothing. In this case, the immediate task is to identify relevant product opportunities for upcoming holidays. The workflow should produce a holiday shortlist with timing risks noted, verified against the merchant's past seasonal records.
Topic
Product Research and Market Opportunities
Target keyword
holiday product trends
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 relevant product opportunities for upcoming holidays.
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: [holiday calendar with window opening and closing dates, prior-year sales by period, candidate products with supplier lead times, and margin rules for seasonal buys]. Separate demand that recurs across prior holiday periods from one-off spikes, and for each candidate compute the latest workable order date by working the supplier lead time back from the window's closing date, marking any candidate whose sourcing cannot land in time.
Return a holiday shortlist with timing risks noted, verified against the merchant's past seasonal records 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
Separate demand that recurs across prior holiday periods from one-off spikes, and for each candidate compute the latest workable order date by working the supplier lead time back from the window's closing date, marking any candidate whose sourcing cannot land in time.
03
Step 3
Deliver a holiday shortlist with each candidate's recurring-demand evidence, its latest order date, its timing risk and a stated leftover-stock disposition, then verify the demand claims against the prior-year records before any purchase order is raised.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
How late can a holiday order still be placed?
No later than the supplier lead time plus inbound transit allows while still landing before the window closes, so each candidate carries a latest order date computed from the calendar; a product that cannot land in time is dropped regardless of its demand evidence.
How is last year's holiday data adjusted before use?
It is treated as a reference and corrected for changes in assortment, pricing and the calendar itself, such as a holiday falling on a different weekday; the adjustments made are listed with the plan so the reasoning survives review.
What happens to unsold holiday stock after the window?
Its disposition is chosen before buying, whether markdown, storage for next season or retirement, and the expected leftover quantity with its holding cost is counted into the buy decision; a plan without a leftover path treats an avoidable cost as a surprise.
Related scenarios
Continue with adjacent or same-topic scenarios
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Identify emerging product trends before expanding the catalog
Identify product trends worth validating before launch
Plan an assortment around seasonal demand
Shortlist products with evidence of demand and viable margins
Compare demand and competition across product categories
Translate social media trend signals into product research leads
Next step
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