Case study · Product Design

Improving Candidate Experience in a Predictive Assessment Platform

Improving candidate experience while increasing completion rates and perceived fairness.

A redesign of the candidate-facing testing interface at BlueOptima, focused on reducing friction, improving usability and driving measurable improvements in completion and perception.

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Predictive Assessment interface redesign hero visual

Project snapshot

At a glance

Role
Lead Product Designer
Company / Product
BlueOptima · Predictive Assessment
Timeline
Discovery → Delivery
Team
PM · Engineering · Growth · Design
Users
Candidates · Recruiters · Hiring Managers
Responsibilities
Research, UX flows, UI design, prototyping, usability testing, A/B testing
Tools
Figma · Miro · Analytics
Outcome
Increased completion, reduced drop-off, improved perceived fairness

Impact at a glance

Completion rate ↑
55% → 78%
After redesign
Drop-off ↓
40% → <20%
Across key steps
Perceived fairness ↑
4/5 → 5/5
Candidate sentiment
Usability score
SUS 97.5
Usability testing

Context

The problem

BlueOptima’s Predictive Assessment helps teams evaluate engineering talent. The existing testing interface created friction for candidates, impacting completion and trust in the process.

  • High drop-off at critical steps (instructions / transitions / submission).
  • Unclear system feedback created uncertainty and perceived unfairness.
  • Inconsistent UI patterns increased cognitive load and slowed progress.
Before redesign screenshot
Before: friction points and unclear states.

Research

Understanding users and constraints

I combined qualitative feedback with funnel insights to understand where candidates struggled, why they dropped off, and what influenced perceptions of fairness.

Methods
  • User interviews
  • Usability testing
  • Journey mapping
  • Analytics review
Key insights
  • Uncertainty drives abandonment.
  • Progress visibility increases trust.
  • Consistency reduces cognitive load.
Personas overview
Personas: candidates, recruiters, hiring managers.

Mapping

Journey & friction points

A simplified journey map helped align stakeholders around the biggest drop-off moments and the emotions behind them, prioritizing changes with the highest impact on completion and perception.

Candidate journey map highlight
Journey highlights: confusion, lack of feedback, unclear next steps.

Product thinking

Hypotheses & success metrics

Hypothesis
Expected outcome
Metric
Clear progress + system status reduces uncertainty.
Higher completion and fewer abandonments.
Completion rate, drop-off rate
Consistent UI patterns reduce cognitive load.
Faster flow, fewer errors.
Time on task, error rate
Better guidance builds trust in the assessment.
Higher perceived fairness.
Perception score (survey)

Design

Exploration, decisions, iteration

I explored multiple interaction patterns to reduce friction and make progress, rules and outcomes clearer. Key decisions were validated through usability testing and A/B experiments.

Key decision example

Simplifying candidate actions from multiple buttons to a clearer primary action improved confidence and reduced hesitation.

  • Reduced ambiguous actions at critical steps.
  • Improved visibility of “what happens next”.
  • Added system feedback and confirmations.
Design option A
Option A — more choices, higher cognitive load.
Design option B
Option B — clearer hierarchy, simpler decision.

Outcomes

Validation & results

We validated the redesign through usability testing and A/B experimentation, tracking key funnel and perception metrics.

Completion rate ↑
55% → 78%
Improved end-to-end flow
Perceived fairness ↑
4/5 → 5/5
Clearer guidance + feedback
Usability score
SUS 97.5
Excellent usability
Drop-off ↓
<20%
Across key steps
Final redesigned interface
Final UI — clearer hierarchy, stronger feedback, smoother flow.

Collaboration & handoff

I worked closely with engineering throughout the process to ensure feasibility, define states, and deliver dev-ready specs.

  • Shared interactive prototypes and annotations.
  • Defined states, edge cases and success criteria.
  • Iterated post-launch based on feedback and data.

Key learnings

  • Clarity at transitions prevents most drop-offs.
  • Perceived fairness improves with transparent rules and feedback.
  • Small UI decisions can drive measurable funnel impact.
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Pedro Mugartegui

[email protected]

Based in Paris, France 🇫🇷
Open to work worldwide.

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