Acquisition reporting counts everyone appearing in the current period as new, including people who ordered long before the window was set. In this case, the immediate task is to define a segment for newly acquired customers. The workflow should produce a customer segment table and interpretation guide built on the merchant's own first-purchase records.
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.
Act as a Shopify operations specialist.
Help me define a segment for newly acquired customers.
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: [the order history with customer identifiers and order dates reaching back before the current window, the merchant's stated definition of a new customer, its treatment of refunded and cancelled first orders, and the window the segment covers]. Place a customer in the segment only when their earliest eligible order anywhere in the history falls inside the window, so anyone with an earlier order is excluded however active they are in the current period, and record the earliest-order date used for each decision.
Return a customer segment table and interpretation guide 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.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Collect the order history with customer identifiers and order dates reaching back before the current window, the merchant's stated definition of a new customer, its treatment of refunded and cancelled first orders, and the window the segment will cover.
Place a customer in the segment only when their earliest eligible order anywhere in the history falls inside the window, so anyone with an earlier order is excluded however active they are in the current period, and record the earliest-order date used for each decision.
Deliver a customer segment table and an interpretation guide stating the definition and window applied, verify the segment count against a recount of distinct customers whose earliest order falls inside the window, and list customers excluded because their history predates it.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Because a customer is new only if no earlier order exists, and a history starting at the window boundary makes everyone look new; the check runs against the full record, and anyone whose earliest order predates the window is listed as excluded rather than dropped silently.
Only where the merchant's definition says so, applied the same way to every customer, because counting refunded first orders admits people who never completed a purchase while excluding them removes customers who did buy again later; the definition is recorded and both counts are shown when the rule is ambiguous.
In line with the merchant's repurchase cycle so a customer is not still called new after they would normally have ordered again, and held fixed across reports once chosen; changing the window restates history, so the report names the window used and flags segments built on a different one.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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