How does the Conversion Diagnostic Kit identify funnel issues?

It uses a 'Signal Stack' approach, analyzing data from analytics, heatmaps, session replays, and performance metrics to pinpoint where users are dropping off.

Does it support automated reporting?

The kit is designed to be used alongside the 'diagnose-conversion-drop' command for automated reporting and faster insight generation.

What kind of templates are included in this kit?

The kit provides conversion diagnostic workbooks, experiment brief templates with KPIs, and agendas for cross-functional conversion war rooms.

Can this skill help prioritize engineering tasks?

Yes, it includes a prioritization model that scores issues based on impact, confidence, effort, and guardrail risk to align development resources effectively.

Is this skill suitable for high-velocity marketing campaigns?

Yes, it is designed for lightweight diagnostics with a target 48-hour turnaround to support fast-moving e-commerce environments.

Conversion Diagnostic Kit

Name: Conversion Diagnostic Kit
Author: gtmagents

bygtmagents

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E-commerce Solutions

Diagnoses e-commerce funnel drop-offs and identifies optimization opportunities through structured data analysis and issue categorization.

About

The Conversion Diagnostic Kit is a specialized tool designed to help developers and growth teams systematically investigate performance issues within e-commerce funnels. Whether dealing with sudden drops in checkout completion or preparing an experiment backlog, this skill provides a rigorous framework for evaluating signal stacks, segmenting user data, and categorizing friction points across UX, technical performance, and merchandising. By aligning engineering, UX, and marketing teams through standardized prioritization models and action trackers, it enables high-velocity remediation and data-driven decision-making for revenue-critical workflows.

Key Features

29 GitHub stars
Multi-signal diagnostic framework integrating analytics, heatmaps, and performance metrics
Comprehensive issue taxonomy for identifying UX friction and technical bottlenecks
Detailed segmentation grid covering devices, channels, geographies, and customer types
Impact-based prioritization model to rank remediation efforts by confidence and effort
Ready-to-use templates for conversion workbooks and cross-functional war room agendas

Use Cases

Investigating sudden drops in conversion rates, AOV, or checkout completion
Aligning cross-functional stakeholders on technical and marketing remediation priorities
Building and prioritizing experiment backlogs based on validated funnel pain points

About

Key Features

29 GitHub stars
Multi-signal diagnostic framework integrating analytics, heatmaps, and performance metrics
Comprehensive issue taxonomy for identifying UX friction and technical bottlenecks
Detailed segmentation grid covering devices, channels, geographies, and customer types
Impact-based prioritization model to rank remediation efforts by confidence and effort
Ready-to-use templates for conversion workbooks and cross-functional war room agendas

Use Cases

Investigating sudden drops in conversion rates, AOV, or checkout completion
Aligning cross-functional stakeholders on technical and marketing remediation priorities
Building and prioritizing experiment backlogs based on validated funnel pain points