IntermediateStore Experience & Conversion AI

Shopping Journey Optimization | Diagnostic Analysis Prompt

Shopping Journey Optimization | Diagnostic Analysis Prompt is a copyable AI prompt for ecommerce sellers. Use it to reduce friction from browsing and add-to-cart through checkout, payment, and confirmation from verified business inputs. Copy the full instruction, add your inputs and check the result before use.

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Supported Platforms: Platform agnostic
Works with: chatgpt · claude · gemini

Prompt Template

01

How the Shopping Journey Optimization | Diagnostic Analysis Prompt works

Reduce friction from browsing and add-to-cart through checkout, payment, and confirmation from verified business inputs. The prompt identifies data gaps first, ranks findings by evidence strength, and ends with phased actions that require human review.

02

Key instructions and output

The points below describe the task and expected output in this prompt.

01

Expected output

The deliverable should include Drop-off points, Root-cause hypotheses, Fix priorities, Journey redesign, and Guardrail metrics.

03

Parameter guide

Prepare the task information listed below and replace placeholders with verified details from the actual case.

[Funnel data]

Replace this placeholder with verified, task-specific information before running the prompt.

[Device and channel]

Replace this placeholder with verified, task-specific information before running the prompt.

[Error logs]

Replace this placeholder with verified, task-specific information before running the prompt.

[User feedback]

Replace this placeholder with verified, task-specific information before running the prompt.

[Shipping, payment, and return policies]

Replace this placeholder with verified, task-specific information before running the prompt.

04

Copy the complete prompt

The complete source is shown below. Copy it from the top right to use it.

You are an ecommerce checkout and shopping-journey advisor. Your objective is to reduce friction from browsing and add-to-cart through checkout, payment, and confirmation. Analyze the following real inputs: [Funnel data], [Device and channel], [Error logs], [User feedback], [Shipping, payment, and return policies]. 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) Drop-off points, Root-cause hypotheses, Fix priorities, Journey redesign, Guardrail metrics; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Separate technical errors, surprise costs, trust issues, and process complexity. Do not sacrifice transparency for short-term conversion.

05

Test this prompt on a real task

Copy a starter instruction, add the required inputs, then run one example and review the output.

01

Shopping Journey Optimization | Diagnostic Analysis Prompt

The deliverable should include Drop-off points, Root-cause hypotheses, Fix priorities, Journey redesign, and Guardrail metrics. Diagnose shopping journey optimization issues and prioritize evidence-backed action

You are an ecommerce checkout and shopping-journey advisor. Your objective is to reduce friction from browsing and add-to-cart through checkout, payment, and confirmation. Analyze the following real inputs: [Funnel data], [Device and channel], [Error logs], [User feedback], [Shipping, payment, and return policies]. 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) Drop-off points, Root-cause hypotheses, Fix priorities, Journey redesign, Guardrail metrics; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Separate technical errors, surprise costs, trust issues, and process complexity. Do not sacrifice transparency for short-term conversion.

Inputs to replace in this prompt

[Funnel data]: Replace this placeholder with verified, task-specific information before running the prompt.
[Device and channel]: Replace this placeholder with verified, task-specific information before running the prompt.
[Error logs]: Replace this placeholder with verified, task-specific information before running the prompt.
[User feedback]: Replace this placeholder with verified, task-specific information before running the prompt.
[Shipping, payment, and return policies]: Replace this placeholder with verified, task-specific information before running the prompt.

06

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

Separate technical errors, surprise costs, trust issues, and process complexity. Do not sacrifice transparency for short-term conversion.

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.

Community rating results

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Source & attribution

Compilation note and reference material

This section identifies the prompt’s creator and source, and marks details that could not be verified.

Compiled by

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 materialContentsquare

This link is reference material and does not establish its page author as the author of this Prompt. The article uses drivers, barriers, and hooks to understand behavior and recommends finding missing information and friction before designing conversion improvements.

The original page does not publish a rating, review, Product Hunt upvote, star, fork, install, or download count.

Content checked: 2026-07-21