One-tap Apply Flow
One conditional skip lifted view→apply conversion from 12% to 16%.
Application
Company
Remember
Timing
H1 2026
Role
Senior Product Designer
Team
PM, Design, Data, Engineering
Contribution
Owned design end-to-end
Improved stage
Exposure
→
View
→
Apply
→
Hire
Overview
Every user passed a final review screen before applying. This case covers how I identified, from behavior data, who that screen actually served, and designed a conditional skip for everyone it didn't.
Problem
View→apply conversion had plateaued. Behavior data and interviews showed only ~5% of applying users actually changed their resume at the review step; for repeat applicants it was a checkpoint clicked through: hesitation without value.
Hypothesis
For users with prior applications and an unchanged resume, the review screen was friction rather than value. Letting them skip it would raise view-to-apply conversion without degrading downstream quality: applications per user and the rate at which applicants passed employers' resume screening.
My Role
Problem framing with PM
Hypothesis and Guardrails with Data
Flow & edge-case design
Implementation review with Engineering
The PM set the KPI and release scope; Data co-designed the experiment; Engineering owned implementation. Problem framing, the flow itself, and the guardrail definition were mine.
Design Decision
Not removal — a conditional skip, applied only where the data said the screen added nothing. The full review flow stays one tap away.
Considered and rejected: removing the step outright. ~5% of applicants genuinely used it. The conditional skip kept the screen for them while clearing the path for everyone else.
Before Flow
After Flow
Job posting detail
Full review flow
Confirm
Apply
What this shows: the shortened path and the visible return entry to the full flow.
Result
+4pp
A/B-validated, View → apply conversion
~2,900
additional applications per month
Flat
Downstream guardrails flat
applications per user and employers resume-screening pass rate held
For the first three days, applications dipped as users met the unfamiliar pattern. We read it as adaptation rather than failure and held instead of rolling back; volume recovered, overtook the control, and the lift repeated at 50:50.
Measured
A/B test of the old flow vs. the conditional skip, 80:20 traffic for two weeks, then widened to 50:50 with the same lift before full rollout. View = job posting detail views · Apply = completed applications. ~2,900/month is the test lift projected onto monthly traffic.
Trade-offs & Guardrails
The risk was clear: an easier apply could push lower-intent applications to employers.
Metric Guardrail
Watched applications per user and the employers' resume-screening pass rate, where unintended applications would surface first. They held.
Design Guardrail
Skip scoped to the data-supported segment; the full review flow stayed one tap away.
What This Shows
This project started from data, qualitative and quantitative, and ended as a UX outcome. The starting point was behavior I saw in user interviews; I confirmed it with quantitative data before making the call. What I built was a UX that lowered the psychological load of applying, so users could reach their goal with less friction.
