Trust & Reviews · Amazon

Customer Reviews

Reimagining the reviews and ratings experience on Amazon.com — surfacing trust signals that help millions make confident purchase decisions. Design leadership focused on clarifying review quality, author credibility, and product sentiment at the moments that matter most in the shopping journey.

Role: Lead designer
Surface: Amazon.com
Reach: Millions of shoppers
Org: Amazon Retail

01 · Problem

A reviews experience that shapes millions of purchase decisions.

Amazon customers rely on reviews to evaluate products quickly and with confidence. The experience needed to surface trustworthy signals, reduce cognitive load, and help shoppers distinguish helpful feedback from noise — without adding friction to the purchase journey.

02 · Solution

Trust signals designed for confident decisions.

Design work focused on the review surfaces customers use most — making quality indicators visible, reviewer credibility clear, and product sentiment scannable at the moments that mattered most for purchase confidence.

Review quality indicators optimized for quick evaluation
Reviewer credibility and verified-purchase signals
Design decisions shaped by direct customer feedback

03 · Process

Design informed by customer voice.

Iteration was driven by customer reviews and shopping behavior data, working tightly with the ratings and reviews team to refine how trust signals are presented across product detail pages and browse surfaces.

Review experience grounded in customer obsession
Tight collaboration with ratings, search, and browse teams
Continuous refinement informed by customer feedback signal

04 · Context

Amazon-scale trust and commerce work.

The work shipped on a site used by millions worldwide — a context where small trust-signal decisions compound into very large customer and business outcomes.

Results

Outcomes.

Reach

Millions

Daily product used by millions of shoppers worldwide.

Collaboration

Cross-team

Tight integration with ratings, search, and browse teams.

Practice

Customer-obsessed

Iteration informed by direct customer feedback and review signal.

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