Behavioral analysis: Funnel Analysis

Development / LaunchquantitativeIntermediateAsynchronous

TL;DR

Study of user behavior through key steps in a process (registration, payment, etc.) to detect drop-offs.

Detailed description

Funnel Analysis allows studying user behavior through key steps in a process, such as registration or payment, to detect abandonment points and optimize conversion. It is essential for prioritizing business and experience improvements.

Main objective

Study user behavior through key steps to detect abandonment points and optimize conversion.

Use cases

E-commerceSaaS onboardingConversion appsRegistration processes

When to use it

In digital products with multi-step processes to identify bottlenecks.

Effort level

Medium

Recommended number of users

Thousands of users (analytics data)

Advantages

✓Measures where conversions are lost
✓Provides business and experience data

Disadvantages

✗Doesn't detect cause of abandonment
✗Can be misleading without qualitative context

When to use

•In multi-stage processes like checkout, onboarding, etc.
•To prioritize optimizations

Metrics

📊Conversion rate per step
📊Drop-off rate per step
📊Average time per stage
📊Number of users per cohort

Execution mode

unmoderated, asynchronous

How results are presented

Analytics dashboards, conversion graphs, behavior funnels, and key event reports. Usually includes cohort visualizations and temporal comparisons.

Practical example

Analyze e-commerce purchase funnel: visitors → cart → checkout → payment → confirmation, identifying highest drop-off.