Expected output
The deliverable should include Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, and Actions.
IntermediateData, Profit & Decision AI
Customer Analytics | Comparative Decision Prompt
Customer Analytics | Comparative Decision Prompt is a copyable AI prompt for ecommerce sellers. Use it to compare candidate options against one consistent decision framework. Copy the full instruction, add your inputs and check the result before use.
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How the Customer Analytics | Comparative Decision Prompt works
Compare candidate options against one consistent decision framework. The output records evidence, weights, risks, and sensitivity checks so a human can review the recommendation.
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.
[Option A]
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[Option B]
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[Option C]
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[Customer orders]
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[First-touch acquisition channel]
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[Engagement]
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[Returns and support]
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[Privacy consent]
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Copy the complete prompt
The complete source is shown below. Copy it from the top right to use it.
You are an ecommerce customer-value and behavior analyst. Compare [Option A], [Option B], and [Option C] for the objective: analyze acquisition cohorts, repeat purchase, retention, LTV, preferences, and service experience. Inputs: [Customer orders], [First-touch acquisition channel], [Engagement], [Returns and support], [Privacy consent]. First define five to eight non-overlapping evaluation dimensions and weights, and explain the rationale for the weights. Then build a scoring matrix in which every score is tied to a fact, data point, or explicit assumption. Provide the best choice, conditions under which it is best, irreversible risks, the lowest-cost validation method, and Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, Actions. Do not invent missing information to force a conclusion. Special requirement: Do not use future information to predict the past. Define observation windows, censoring, and identity-resolution rules.Test this prompt on a real task
Copy a starter instruction, add the required inputs, then run one example and review the output.
01Customer Analytics | Comparative Decision Prompt
The deliverable should include Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, and Actions. Compare options and make a reviewable customer analytics decision
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Customer Analytics | Comparative Decision Prompt
The deliverable should include Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, and Actions. Compare options and make a reviewable customer analytics decision
You are an ecommerce customer-value and behavior analyst. Compare [Option A], [Option B], and [Option C] for the objective: analyze acquisition cohorts, repeat purchase, retention, LTV, preferences, and service experience. Inputs: [Customer orders], [First-touch acquisition channel], [Engagement], [Returns and support], [Privacy consent]. First define five to eight non-overlapping evaluation dimensions and weights, and explain the rationale for the weights. Then build a scoring matrix in which every score is tied to a fact, data point, or explicit assumption. Provide the best choice, conditions under which it is best, irreversible risks, the lowest-cost validation method, and Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, Actions. Do not invent missing information to force a conclusion. Special requirement: Do not use future information to predict the past. Define observation windows, censoring, and identity-resolution rules.Inputs to replace in this prompt
[Option A]- Replace this placeholder with verified, task-specific information before running the prompt.
[Option B]- Replace this placeholder with verified, task-specific information before running the prompt.
[Option C]- Replace this placeholder with verified, task-specific information before running the prompt.
[Customer orders]- Replace this placeholder with verified, task-specific information before running the prompt.
[First-touch acquisition channel]- Replace this placeholder with verified, task-specific information before running the prompt.
[Engagement]- Replace this placeholder with verified, task-specific information before running the prompt.
[Returns and support]- Replace this placeholder with verified, task-specific information before running the prompt.
[Privacy consent]- 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 use future information to predict the past. Define observation windows, censoring, and identity-resolution rules.
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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