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Spark playbook scenarios · Spark: AI Store Assistant

Monitor comparable market-price movements

Price observations arrive from scattered listings and supplier notes, and nobody can tell which ones are truly comparable. In this case, the immediate task is to monitor comparable market-price movements. The workflow should produce a dated price movement log with the provenance of each change and a list of the items that need a follow-up decision.

Topic
Market and Supply Chain Alerts
Target keyword
ecommerce market price alerts
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 monitor comparable market-price movements.
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: [monitored sources, the comparable items with their specification, unit and market, and the date each price observation was taken].

Compare only items that match on specification, unit and market, express every movement in the merchant's reporting currency with the observation date attached, and mark as an exception any change whose provenance cannot be traced to a named source or a dated listing.
Return the price movement log and impact review list 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

Name the monitored sources, the comparable items with the specification, unit and market that define comparability, and the date each price observation was taken.

02

Step 2

Compare only items that match on specification, unit and market, express every movement in the merchant's reporting currency with the observation date attached, and mark as an exception any change whose provenance cannot be traced to a named source or a dated listing.

03

Step 3

Deliver a dated price movement log with the provenance of each change and a follow-up list of items that affect active sourcing decisions, then reconcile the log against the supplied records and note every entry whose source stays unconfirmed.

FAQ

Common questions when running this scenario

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

Which items count as comparable?

Those matching on specification, unit and market at the same observation date. A loose match can show a movement that never happened, so record the basis beside each comparison instead of assuming the listings are interchangeable.

Should every recorded change trigger action?

No, log it first and act only where the item has an open sourcing decision. Keeping the full movement history is what later lets you tell a real trend from a single listing that simply changed its own price.

How should an unnamed price tip be handled?

Store it apart from sourced observations and keep it out of the movement log until a named source confirms it. Treating an unverified tip as a data point biases every comparison built on top of it.

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Next step

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