Return Reason Analysis — AI workflow for Malaysian SMEs
Return Reason Analysis turns return data into root-cause themes. AI writes the first draft. You check it before it goes out.
Ready-to-use prompts for Return Reason Analysis
Create prompt
Create prompt
Create a first-pass deliverable
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
The task is return reason analysis. Input: return data. Output I expect: root-cause themes. Make it work for online sellers on marketplaces, webstores and social commerce. Short sections only. Wrap up with one next action and a version ready to paste into the workflow.
Replace “return data” with your real data.
Analyse prompt
Analyse prompt
Analyse the input and identify the highest-impact insights
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Analyse return reason analysis for me. Working from return data, list the five findings that matter, ranked by impact. Tie each to a business consequence. Then build root-cause themes. Sign off with three moves to push return rate up.
Replace “return data” with your real data.
Optimise prompt
Optimise prompt
Improve an existing version for better business results
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Review and upgrade my return reason analysis. Input: return data, plus what we currently produce. Tell me where it loses accuracy, clarity or conversion. Remove work that adds nothing. Hand back a better root-cause themes, with each improvement mapped as before-issue, change, expected return rate movement.
Replace “return data” with your real data.
Automate prompt
Automate prompt
Turn the task into a repeatable AI-assisted workflow
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Design a repeatable automation for return reason analysis. Trigger: return data. Required output: root-cause themes. Systems on hand: E-commerce platform, marketplace, CRM, inventory, analytics. Define the trigger, required fields, the AI instruction, decision rules, human approval points, fallback handling, what data to store, and how to track return rate. Keep it simple enough for a small Malaysian team to maintain without a developer.
Replace “return data” and the listed systems with what you actually run.
New here? How the four prompts work together
They are one sequence, not four options. Run them in order on the same piece of work, feeding each answer into the next.
Create
Paste your real data. You get a first draft. Most people never need to go further than this.
Analyse
Paste that draft back in. It tells you what is weak, missing or wrong before a customer sees it.
Optimise
Paste the draft again with what Analyse found. You get a stronger version.
Automate
Only once the output has been good five times running. This is the one people reach for too early.
Nothing forces you through all four. A good Create draft that you edit yourself is a finished job. The longer version.
Specification
- You give it
- Return data
- You get back
- Root-cause themes
- Moves
- Return rate
- Run it
- Daily/Weekly
- Priority
- P3
- Works with
- E-commerce platform, marketplace, CRM, inventory, analytics
Is this for you?
- Pick it for
- Online sellers on marketplaces, webstores and social commerce. No setup needed. If you can copy and paste, you can run it.
- What you get
- Drafted in minutes, not built from scratch. It aims at growth. Watch one number: return rate.
- Reach for it when
- Most SMEs run it daily to weekly. If you are doing it by hand more often, automate it first.
How to run it
- 01Collect the input: return data.
- 02Copy the Create prompt and paste it into ChatGPT, Claude or Gemini.
- 03Replace “return data” in the prompt with your real data, then run it.
- 04Read the output against what you already know and correct anything the model guessed at.
- 05Track return rate. Only wire up the Automate prompt after five useful runs in a row.
The tools you will need
The prompts run in any chat assistant — ChatGPT, Claude or Gemini. These are what you need around them to get the input in and the output somewhere useful.
- E-commerce platform
- Any tool you already use is fine.
- marketplace
- Any tool you already use is fine.
- inventory
- inFlow Inventory
- KatanaAimed at makers and light manufacturing.
- SortlyFree tier for a small catalogue.
- Zoho Inventory
- analytics
- Google Analytics
- UmamiPrivacy-first, self-hostable.
Check the rules yourself
This workflow touches rules set by someone else. Check the current requirements yourself before you act on the output.
- LHDN
- e-Invoice arrived in phases set by turnover. LHDN's published timeline puts Phase 4 at businesses up to RM5 million from 1 January 2026, and exempts those under RM1,000,000 a year. Check which phase you are in before you change how you invoice. Read from LHDN in August 2026; confirm it is still current.
- RMCD
- Cross the registration threshold and you must charge SST and file returns on time. Whether a service is taxable or exempt is decided here, and getting it wrong is expensive to unwind later.
- JPDP
- Pasting customer or staff records into an AI tool is a transfer of personal data. Consent, notice, retention and cross-border rules still apply when the tool is not yours.
Links go to each authority’s own site. See all Malaysian authorities.
Data & privacy
This workflow involves buyer names, addresses and order history. Under the PDPA your business stays responsible for it, and pasting it into an AI tool may be a disclosure to a third party. Replace names and numbers with initials or references first — the prompt works just as well on “Customer A”.
Using customer data with AI tools in Malaysia covers what to check with any provider before you paste.
For this website, it works differently.
Nothing you type here is sent to us or to an AI model. The prompts are static content, and the copy button works directly inside your browser.
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Maintained by Naven Pillai