Ecommerce Customer Support Macro Quality and Consistency Review
Review support macros for accuracy, empathy, consistency, actionability, and safe escalation across common ecommerce situations.
PROMPT GUIDE / ECOMMERCE & RETAIL
Review support macros for accuracy, empathy, consistency, actionability, and safe escalation across common ecommerce situations.
READY-TO-USE PROMPT
Ecommerce Customer Support Macro Quality and Consistency Review
COPY THIS PROMPT
Act as a customer experience quality lead and policy editor. GOAL Audit and improve [SUPPORT MACRO SET] for [STORE, MARKET, AND CHANNEL]. INPUTS - Current macros and associated ticket reasons - Policies, shipping promises, refund rules, warranties, and escalation limits - Brand voice, localization, accessibility, and personalization rules - Quality data, repeat contacts, complaints, handle time, and agent feedback WORKFLOW 1. Check every factual statement against the controlling policy 2. Find ambiguity, missing next steps, overpromising, blame, and robotic language 3. Separate reusable policy content from fields that require personalization 4. Define exceptions and mandatory human escalation 5. Rewrite macros and create a reviewer scorecard REQUIREMENTS - Separate confirmed facts, assumptions, and missing information. - Do not invent measurements, specifications, prices, policies, or results. - Make every recommendation specific, testable, and prioritized. - Identify risks, dependencies, edge cases, and a practical fallback. - Ask focused questions when a missing input would materially change the answer. DELIVERABLE Return an issue inventory, risk-ranked revisions, improved macros with placeholders, escalation map, and quality sampling checklist.
What this prompt helps you produce
- Accurate answers customers can act on
- Consistent tone without sounding copied or evasive
- High-risk situations routed to trained people
How to use it
- Replace every bracketed input with real project information.
- Attach representative examples and constraints when available.
- Review assumptions before acting on recommendations.
- Run the proposed checks on a small sample, record results, then revise.
Frequently Asked Questions
Can I use this with incomplete information?
Yes. Leave unknown fields clearly marked. The response should turn them into targeted questions instead of silently filling gaps.
Should I accept the first result?
No. Verify it against real examples, measurements, source material, or stakeholder requirements, then rerun the prompt with corrections.
Which AI model should I use?
Use a model that can follow long structured instructions and provide the context it needs. The quality of the inputs and verification process matters more than a model label.