Customers receive one follow-up message whether their order is being packed, shipped or already delivered. In this case, the immediate task is to define useful post-purchase follow-up messages. The workflow should produce an email automation map and test scenarios, assigning onboarding, delivery and retention messages to the recorded order stage.
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 useful post-purchase follow-up messages.
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: [recorded customer events, order stages shown in the fulfillment records, product context per line item, and the merchant's stated eligibility and stop conditions]. Assign each message to the stage it serves, sending onboarding while the item is still in transit, delivery follow-up once the carrier records handover, and retention contact only after the stated post-delivery window, and treat any order whose stage cannot be resolved from the records as ineligible rather than guessing.
Return an email automation map and test scenarios 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, for one merchant-defined catalogue slice, the recorded customer events, the order stages shown in the fulfillment records, the product context attached to each line item, and the eligibility and stop conditions the merchant has stated for post-purchase contact.
Assign each message to the stage it serves, sending onboarding while the item is still in transit, delivery follow-up once the carrier records handover, and retention contact only after the stated post-delivery window, and treat any order whose stage cannot be resolved from the records as ineligible rather than guessing.
Deliver an email automation map and test scenarios naming the stage, trigger, timing and stop rule for every message, then reconcile the message count against the order stage export and list each order left unassigned with the field that blocked it.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Follow the line items rather than the order header, so guidance references the goods the customer actually received; where items ship separately, each dispatch earns its own delivery message, while the shared onboarding message goes out once.
Stop once the order reaches the merchant's stated end state or the customer replies, because continued contact after a resolved purchase reads as unrelated promotion; record the stop reason so a later campaign cannot silently re-enter the same contact.
Run one order per observed stage through the full timing sequence and compare the sends against the stage export, since a message keyed to a missing timestamp fires either immediately or never; log both outcomes as defects rather than adjusting timing to fit.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.