Does it produce reports for non-technical stakeholders?

Absolutely. It includes a dedicated Stakeholder Analysis Output Format that summarizes findings in plain English with clear executive summaries and recommended actions.

What is the 4-Question Method used in this skill?

It is a framework ensuring every analysis answers: What changed? Why did it change? So what is the impact? Now what is the recommended action?

How does this skill help with unexpected metric drops?

The skill provides a Metric Triage template that guides you through segmenting data by platform, user cohort, and acquisition channel to isolate the root cause.

Can this skill perform cohort and funnel analysis?

Yes, it includes specific structures for defining cohort windows and retention metrics, as well as identifying the 'biggest drop-off' points in conversion funnels.

Data Analysis Standard

Name: Data Analysis Standard
Author: mohitagw15856

bymohitagw15856

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295

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分析与监控

Transforms raw product metrics into actionable insights using structured frameworks for root cause analysis and stakeholder reporting.

The Data Analysis Standard skill empowers Product Managers and developers to move beyond simple data reporting into rigorous product decision-making. By enforcing the '4-Question Method'—What, Why, So What, and Now What—this skill ensures that every metric investigation identifies a clear root cause, assesses business impact, and provides a concrete recommendation. Whether you are performing a funnel deep-dive, a cohort retention study, or triaging an unexpected drop in conversion, this skill provides the templates and quality checks needed to produce high-confidence, stakeholder-ready analysis.

主要功能

01Stakeholder-ready reporting formats including confidence levels

02295 GitHub stars

034-Question Method for structured narrative and decision-making

04Standardized funnel and cohort analysis frameworks

05Metric triage templates for platform, cohort, and channel segmentation

06Built-in quality checks for data limitations and correlation vs. causation

使用场景

01Investigating a sudden drop in conversion rates or user engagement

02Identifying friction points in a multi-step user onboarding funnel

03Explaining complex metric movements to executive stakeholders

主要功能

01Stakeholder-ready reporting formats including confidence levels

02295 GitHub stars

034-Question Method for structured narrative and decision-making

04Standardized funnel and cohort analysis frameworks

05Metric triage templates for platform, cohort, and channel segmentation

06Built-in quality checks for data limitations and correlation vs. causation

使用场景

01Investigating a sudden drop in conversion rates or user engagement

02Identifying friction points in a multi-step user onboarding funnel

03Explaining complex metric movements to executive stakeholders