Bring these inputs
Consent-based audience rules, product-care instructions, current policies and anonymized tickets.
Use approved, minimized data. Replace personal details with anonymous IDs.
Work through the lesson
- Map the customer’s next need after purchase: setup, care, delivery help or a complementary purchase. Avoid making every message a discount.
- Ask AI to draft three messages with trigger, audience, exclusions, purpose and a single CTA. Keep marketing consent and service communications distinct.
- Build a support knowledge set from approved policies. Require the assistant to cite the policy and route missing or exceptional cases to a person.
- Test with edge cases: delayed shipment, return-window exception, damaged item and wrong product. Do not let a draft assistant promise refunds or delivery dates it cannot verify.
- Measure repeat purchase, unsubscribe rate, complaints, resolution quality and margin. Review failures weekly before increasing automation.
Try this prompt
Replace the bracketed fields with your own evidence.
Draft a post-purchase journey for [product] from [approved policies]. For each message provide trigger, exclusions, helpful purpose and CTA. Then draft support replies to [anonymized tickets], citing the policy used. Flag exceptions for a person. Do not send, promise compensation or invent tracking details.
Worked example
A care email helps a customer use the product successfully before an upsell. A late-delivery reply should quote verified tracking status and explain the real next step.
Before you use the result
- Audience exclusions prevent contradictory messages.
- Policy versions are current.
- A person handles exceptions.
Complete the exercise
Create the deliverable described at the top of this lesson. Save the input evidence, your edited output, unresolved questions and the name of the reviewer in your workbook.