The percentage of users who abandon a flow at a specific step without completing the intended goal. High drop-off at a particular screen typically indicates friction — whether through confusing copy, a demanding form, an unexpected requirement, or a missing affordance — and is the starting point for targeted design improvements.
Common contexts
- Analyzing a signup funnel to identify at which step new user acquisition is leaking most significantly
- Prioritizing which screen in a checkout flow to redesign first based on step-by-step abandonment data
- Presenting data to stakeholders to justify investing in a friction-reduction redesign with clear numeric evidence
Use when
When you have sufficient traffic volume to produce statistically meaningful rates, and you need to prioritize where to focus design effort across a multi-step flow based on evidence rather than intuition.
Avoid when
Don't rely on drop-off rate alone to diagnose why users leave — the number tells you where the problem is, but not what caused it; pairing it with session recordings or usability testing is required to design an effective fix.
A high drop-off rate at step three doesn't mean step three is broken — it often means step one or two created wrong expectations that catch up with the user later, so always trace back upstream before redesigning the exit point.
Real-world examples
- Amazon closely monitors drop-off rates at each step of their checkout funnel, which led to the invention of 1-Click purchasing to eliminate the primary drop-off point in e-commerce.
- Duolingo analyzes drop-off rates in their onboarding flow to identify which steps cause users to abandon setup, using this data to continuously simplify the first-run experience.
- Booking.com uses drop-off rate analysis across their reservation funnel to run hundreds of A/B tests simultaneously, optimizing conversion at each micro-step of the booking process.