You will get Ecommerce Returns & Refund Root-Cause Analysis | Product Issue Report
Project details
Hi, I’m Kashan. I will turn your ecommerce returns, refunds, RMA records and support-reason data into a clear, traceable product-issue report in Excel or Google Sheets.
I clean and connect the supplied files, standardize inconsistent reason codes and comments, link refund value to products and SKUs, and rank recurring issues by frequency, rate, value and evidence strength.
You can receive:
Source control and reconciliation summary
Cleaned and classified return detail
Product and SKU issue ranking
Return and refund trend summary
Evidence-linked examples
Human-reviewed exception report
Approved mappings are applied first. AI is used only to propose categories for unresolved free text; uncertain records are manually reviewed and marked Review Required instead of guessed. I also sample passed classifications and reconcile the supplied records and relevant refund totals.
A usable return reason, comment, tag or issue field is required. This service reports data-supported patterns and hypotheses, not proven causes. It does not process refunds, contact customers, replace native platform reporting, or guarantee reduced returns.
I clean and connect the supplied files, standardize inconsistent reason codes and comments, link refund value to products and SKUs, and rank recurring issues by frequency, rate, value and evidence strength.
You can receive:
Source control and reconciliation summary
Cleaned and classified return detail
Product and SKU issue ranking
Return and refund trend summary
Evidence-linked examples
Human-reviewed exception report
Approved mappings are applied first. AI is used only to propose categories for unresolved free text; uncertain records are manually reviewed and marked Review Required instead of guessed. I also sample passed classifications and reconcile the supplied records and relevant refund totals.
A usable return reason, comment, tag or issue field is required. This service reports data-supported patterns and hypotheses, not proven causes. It does not process refunds, contact customers, replace native platform reporting, or guarantee reduced returns.
Data Tool
Microsoft ExcelWhat's included
| Service Tiers |
Starter
$49
|
Standard
$119
|
Advanced
$229
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $50
Additional Revision
+$20Frequently asked questions
About Kashan
Business Research & Data Validation | Data Analysis & Reconciliation
Oyama, Japan - 7:18 am local time
That practical experience is what led me into broader business research and data work.
I help clients with research, data validation, comparison, analysis, and reconciliation. Sometimes that means finding and organizing supplier or market information. Other times it means cleaning a spreadsheet, comparing two files, checking why records don’t match, or finding missing, duplicate, and inconsistent data.
I work mainly with Excel, CSV files, business records, supplier data, pricing information, and structured exports.
My approach is simple: I keep the original data intact, avoid guessing when something is unclear, and flag anything that needs attention. For larger datasets, I use tools to handle the repetitive work efficiently, then review the exceptions and spot-check the results before delivery.
The goal is not to give you another complicated spreadsheet. It’s to give you a clear result showing what was checked, what matches, what doesn’t, and what needs your attention.
If you have business data, research, spreadsheets, or records that need to be checked, compared, cleaned up, or organized, feel free to send them over.
Steps for completing your project
After purchasing the project, send requirements so Kashan can start the project.
Delivery time starts when Kashan receives requirements from you.
Kashan works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm that the data supports the report
I review the sample fields, sources, date coverage and package limits. If there is no usable reason, comment, tag or issue field, I recommend a smaller valid scope rather than infer unsupported causes.
Preserve, normalize and connect the records
I retain the original files, log source counts and totals, standardize IDs, SKUs, products, dates, channels, reasons and refund values, then match records using deterministic keys first.