Expected output
The deliverable should include Cause tree, Disposition path, Inspection rules, Resell or disposal recommendation, and Prevention actions.
IntermediateSupply Chain & Fulfillment AI
Returns and Reverse Logistics | Diagnostic Analysis Prompt
Returns and Reverse Logistics | Diagnostic Analysis Prompt is a copyable AI prompt for ecommerce sellers. Use it to reduce avoidable returns and optimize authorization, transport, inspection, refurbishment, and restocking from verified business inputs. Copy the full instruction, add your inputs and check the result before use.
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How the Returns and Reverse Logistics | Diagnostic Analysis Prompt works
Reduce avoidable returns and optimize authorization, transport, inspection, refurbishment, and restocking from verified business inputs. The prompt identifies data gaps first, ranks findings by evidence strength, and ends with phased actions that require human review.
Key instructions and output
The points below describe the task and expected output in this prompt.
Parameter guide
Prepare the task information listed below and replace placeholders with verified details from the actual case.
[Return reasons]
Replace this placeholder with verified, task-specific information before running the prompt.
[SKU and channel]
Replace this placeholder with verified, task-specific information before running the prompt.
[Order and customer history]
Replace this placeholder with verified, task-specific information before running the prompt.
[Product condition]
Replace this placeholder with verified, task-specific information before running the prompt.
[Return cost and policy]
Replace this placeholder with verified, task-specific information before running the prompt.
Copy the complete prompt
The complete source is shown below. Copy it from the top right to use it.
You are an ecommerce returns and reverse-logistics lead. Your objective is to reduce avoidable returns and optimize authorization, transport, inspection, refurbishment, and restocking. Analyze the following real inputs: [Return reasons], [SKU and channel], [Order and customer history], [Product condition], [Return cost and policy]. First list data gaps and definitions that need confirmation. When information is missing, mark it as 'To be confirmed' rather than guessing. Then provide: (1) key findings and supporting evidence; (2) prioritized root causes or opportunities using impact × evidence strength; (3) Cause tree, Disposition path, Inspection rules, Resell or disposal recommendation, Prevention actions; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Do not obstruct legitimate returns to improve the metric. Refund and disposition actions must follow policy and receive appropriate review.Test this prompt on a real task
Copy a starter instruction, add the required inputs, then run one example and review the output.
01Returns and Reverse Logistics | Diagnostic Analysis Prompt
The deliverable should include Cause tree, Disposition path, Inspection rules, Resell or disposal recommendation, and Prevention actions. Diagnose returns and reverse logistics issues and prioritize evidence-backed action
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Returns and Reverse Logistics | Diagnostic Analysis Prompt
The deliverable should include Cause tree, Disposition path, Inspection rules, Resell or disposal recommendation, and Prevention actions. Diagnose returns and reverse logistics issues and prioritize evidence-backed action
You are an ecommerce returns and reverse-logistics lead. Your objective is to reduce avoidable returns and optimize authorization, transport, inspection, refurbishment, and restocking. Analyze the following real inputs: [Return reasons], [SKU and channel], [Order and customer history], [Product condition], [Return cost and policy]. First list data gaps and definitions that need confirmation. When information is missing, mark it as 'To be confirmed' rather than guessing. Then provide: (1) key findings and supporting evidence; (2) prioritized root causes or opportunities using impact × evidence strength; (3) Cause tree, Disposition path, Inspection rules, Resell or disposal recommendation, Prevention actions; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Do not obstruct legitimate returns to improve the metric. Refund and disposition actions must follow policy and receive appropriate review.Inputs to replace in this prompt
[Return reasons]- Replace this placeholder with verified, task-specific information before running the prompt.
[SKU and channel]- Replace this placeholder with verified, task-specific information before running the prompt.
[Order and customer history]- Replace this placeholder with verified, task-specific information before running the prompt.
[Product condition]- Replace this placeholder with verified, task-specific information before running the prompt.
[Return cost and policy]- Replace this placeholder with verified, task-specific information before running the prompt.
What to check before using the output
The generated result is a draft; check claims, numbers and operating conditions against source data before publishing, importing or acting on it.
Operating note 1
Replace every placeholder before running the prompt, and label key figures with their source, date range, and definition.
Operating note 2
Do not obstruct legitimate returns to improve the metric. Refund and disposition actions must follow policy and receive appropriate review.
Operating note 3
Before uploading order, customer, contract, or supplier data, redact sensitive information and comply with platform terms, privacy policies, NDAs, and company data-governance requirements. Have the responsible operator review the result before it is published, sent, or executed.
Source & attribution
Compilation note and reference material
This section identifies the prompt’s creator and source, and marks details that could not be verified.
Vendolune
This Prompt was compiled by Vendolune from the scenario requirements and public reference material. The reference-page author is not credited as this Prompt's author.
Reference materialGorgias
This link is reference material and does not establish its page author as the author of this Prompt. The article provides prompts for analyzing low-CSAT causes, AI-to-human handoffs, customer friction, intents, and macro-coverage gaps from real tickets and support data.
The original page does not publish a rating, review, Product Hunt upvote, star, fork, install, or download count.
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