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 | Diagnostic Analysis Prompt
Customer Analytics | Diagnostic Analysis Prompt is a copyable AI prompt for ecommerce sellers. Use it to analyze acquisition cohorts, repeat purchase, retention, LTV, preferences, and service experience from verified business inputs. Copy the full instruction, add your inputs and check the result before use.
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How the Customer Analytics | Diagnostic Analysis Prompt works
Analyze acquisition cohorts, repeat purchase, retention, LTV, preferences, and service experience 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.
[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.
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. Your objective is to analyze acquisition cohorts, repeat purchase, retention, LTV, preferences, and service experience. Analyze the following real inputs: [Customer orders], [First-touch acquisition channel], [Engagement], [Returns and support], [Privacy consent]. 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) Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, 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 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 | Diagnostic Analysis Prompt
The deliverable should include Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, and Actions. Diagnose customer analytics issues and prioritize evidence-backed action
Show prompt and variablesHide prompt and variables
Customer Analytics | Diagnostic Analysis Prompt
The deliverable should include Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, and Actions. Diagnose customer analytics issues and prioritize evidence-backed action
You are an ecommerce customer-value and behavior analyst. Your objective is to analyze acquisition cohorts, repeat purchase, retention, LTV, preferences, and service experience. Analyze the following real inputs: [Customer orders], [First-touch acquisition channel], [Engagement], [Returns and support], [Privacy consent]. 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) Cohort analysis, LTV and payback period, Repeat-purchase path, High-value characteristics, 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 use future information to predict the past. Define observation windows, censoring, and identity-resolution rules.Inputs to replace in this 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 materialKlaviyo
This link is reference material and does not establish its page author as the author of this Prompt. The author shares frameworks his team uses to analyze reviews at scale, build customer profiles, generate ideas from source material, audit email programs, and examine retention from an executive perspective.
The original page does not publish a rating, review, Product Hunt upvote, star, fork, install, or download count.
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