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Spark playbook scenarios · Spark: AI Store Assistant

Translate social media trend signals into product research leads

Captured posts show what draws attention, but attention figures do not show whether anyone is buying. In this case, the immediate task is to translate social media trend signals into product research leads. The workflow should produce a shortlist ranked by verifiable demand evidence, flagging the weaker signals and the observation date of each.

Topic
Product Research and Market Opportunities
Target keyword
social media product trend tracker
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 translate social media trend signals into product research leads.
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: [captured posts with publication and capture dates, platform, reach and engagement figures, referenced products, and any existing dated demand evidence for those products]. Convert each post into a research lead only when it points at a product the store could actually stock, record the observation date of every signal, and mark trends whose engagement has already visibly peaked in the capture sequence.
Return a shortlist ranking trends by verifiable demand evidence with weaker signals flagged 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.
Use in Spark
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.

01

Step 1

Gather the captured posts with their publication dates, capture dates, platform, reach and engagement figures, and the specific products each references, plus any dated demand evidence that already exists for those products.

02

Step 2

Convert each post into a research lead only when it points at a product the store could actually stock, record the observation date of every signal, and mark trends whose engagement has already visibly peaked in the capture sequence.

03

Step 3

Deliver a shortlist ranked by verifiable demand evidence rather than engagement, with weaker signals flagged and each entry carrying its observation date and platform, then verify the quoted figures back to the captured posts.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

Can engagement figures stand in for sales evidence?

No, because likes and views measure attention while the shortlist ranks commercial interest; a post only lifts a product's rank when dated demand evidence backs it, otherwise it stays on the list as a flagged lead awaiting that evidence.

Which mentions become research leads?

Only those referencing products the store could stock and reach with its current audience, since taking every mention would swamp the shortlist with unvalidatable entries; the screening rule is stated so a filtered-out mention can be revisited if the store's scope changes.

Why store both publication and capture dates?

Because a post can circulate long after publication and a trend may have peaked between the two dates; the pair shows how fresh the signal is, and entries whose capture date lags far behind are marked as possibly stale.

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Next step

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