What are friction signals in user journey tracking?

Friction signals are behavioral indicators that a user is struggling with the UI, such as rage clicks (3+ clicks on an element), form thrashing, or back loops without progress.

How does this skill improve funnel analysis?

It adds intent context to every event, allowing you to correlate specific steps to a user's ultimate goal and identify exactly where and why abandonment occurs.

What is 'job context' in event instrumentation?

Job context includes the specific user goal (job_name), their current location in the flow (job_step), and their overall progress, making data easier to analyze than generic event logs.

Can I use this with frameworks like React or Vue?

Yes, the skill provides patterns and implementation guidance compatible with modern web frameworks including React, Vue, and Svelte.

User Journey Tracking

Name: User Journey Tracking
Author: nexus-labs-automation

bynexus-labs-automation

分析と監視

Instruments web applications to track user intent, funnel progress, and friction signals for deeper behavioral observability.

概要

This skill enables developers to implement sophisticated user journey tracking by correlating events with specific user goals via the Jobs-to-be-Done (JTBD) framework. It focuses on detecting critical friction signals—such as rage clicks, back loops, and retry exhaustion—allowing teams to understand the qualitative behavior behind quantitative drop-offs. By standardizing event schemas for multi-step flows, it helps identify why users fail to complete tasks and where UI unresponsiveness or confusion is damaging the user experience.

主な機能

Contextual intent tracking using job-based metadata (goal, step, progress)
Standardized event schema for journey starts, completions, and abandonments
Automated friction signal detection for rage clicks and form thrashing
Integration with user-focused observability patterns to avoid high-cardinality anti-patterns
Advanced drop-off analysis to identify behavioral patterns behind failures
0 GitHub stars

ユースケース

Analyzing user onboarding flows to improve conversion rates through behavioral insights
Instrumenting multi-step checkout funnels to identify specific points of user frustration
Debugging silent failures by tracking 'success with friction' signals in complex web apps

概要

主な機能

Contextual intent tracking using job-based metadata (goal, step, progress)
Standardized event schema for journey starts, completions, and abandonments
Automated friction signal detection for rage clicks and form thrashing
Integration with user-focused observability patterns to avoid high-cardinality anti-patterns
Advanced drop-off analysis to identify behavioral patterns behind failures
0 GitHub stars

ユースケース

Analyzing user onboarding flows to improve conversion rates through behavioral insights
Instrumenting multi-step checkout funnels to identify specific points of user frustration
Debugging silent failures by tracking 'success with friction' signals in complex web apps