Product Consolidation | MVP to Market | Build Customer Trust | Beta iOS Mobile Application

Case Study: Build Customer Trust

The Challenge

• Despite robust data science behind Amazon's book recommendations, users often perceived them as impersonal or irrelevant—leading to lower engagement and missed conversion opportunities.

• The challenge was to earn trust by humanizing the book recommendations so they did not feel disconnected.

• Design lightweight but meaningful UX interventions that make the UX/UI smarter, more transparent, and trustworthy.

My Role

I was the Lead Product Designer embedded on the Trusted Voices Team, where I:

• Conducted UX audits of the Amazon Book Review, book detail page, and Goodreads to help personalize content without disrupting the shopping experience.

• Worked cross-functionally with UX research, Amazon Editors, Goodreads Team, Product, and Dev.

• Designed and tested micro interactions and content layouts that helped users understand an actual human was recommending this book.

Discovery & Insights

• I worked with UX research to review customer feedback to identify where the lack of trust around recommendations is coming from.

• Partnering with the Goodreads UX team to compare how they present similar content, and digest how their users perceive trust.

• A key insight: users didn’t trust recommendations when there was no visible photo of a person or the context sounded like a machine.

Design Execution

• Introduced an editorial blurb component to make book recommendations feel personal and trusted by including a photo and quotations.

• Accessed content from the Amazon Book Review to populate the book recommendation blurbs.

• Created a north star design pattern to include other human centered voices like authors, Kindle highlights, Goodreads, and more.

Outcomes

• Supported long-term efforts to bridge Amazon’s e-commerce experience with Goodreads insights.

• Leveraged Goodreads book recommendation reputation by adding Goodreads star ratings to book detail page to build Amazon book recommendation trust.

• Created design road map for integrating other human voices into the book recommendation engine.

If I Had More Time...

I would have liked to see where the blurb card fit into the search, detail page, and thank you page, resulting in the growth of trust of human centered book recommendation.

Key Takeaway

Small UX interventions—when backed by user insight and executed thoughtfully—can have an large impact on how customers interact with your product.

Design system & components

The blurb card is designed on the thinking that a human book recommendation has a visually prominent quote mark. The card contains real human reviews of the book, their name, photo (optional), and a follow button (optional). These examples show the design system for the blurb component.

OUTCOME

Using this system of card types allows the blurb cards to have customization and flexibility of use.

mirco UI interactions

Book customers often value accolades such as awards and bestseller status as indicators of a book's quality and relevance, helping them make informed choices in their reading selections. These accolades provide customers with a sense of assurance that they are investing their time and money in a worthwhile literary experience.

Currently, customers are presented with several types of accolades throughout the book's detail page, both above (ATF) and below the fold (BTF).

In the glance evaluation section (ATF) customers see a single ranked badge. We estimate ~30% of our top titles will have more than one badge at a given time.

In the editorial reviews section (BTF) customers see an array of trusted voices and media sources. We hypothesize these accolades are being missed by the customer due to their location on the detail page.

In Q4 2022 we ran an email campaign with multiple badges on a single title. This experiment resulted in a statistically significant increase of click through rate (CTR) by 6.25%.

How might we create a combined multi-badge and editorial customer experience ATF to help customers simplify their evaluation of titles and increase their purchase confidence?

Project overview

The Amazon Book Review (ABR) is filled with thousands of book reviews from the Amazon Editors and most customers don’t know there are real humans reading and recommending books on a monthly basis. The ABR enhances customer's shopping experience by providing credibility, personalization, guidance, and a sense of community. It helps customers make better-informed decisions, save time, and build lasting relationships with the editorial staff, authors and genres that align with their reading taste.

Our hypothesis is using editorial bios as a mechanism to personalize book recommendations in a bookstore offers a powerful way to connect readers with books that resonate with their interests and preferences. By including personalized biographical information about the editorial team, readers can relate to the tastes and literary preferences of the curators. This fosters a sense of trust, as customers are more likely to explore books recommended by individuals with similar reading sensibilities.

How might we create a discovery pathway to the Amazon Editors’ book reviews throughout customer’s shopping experience?

COMPONENT SYSTEM

Many book customers struggle to find curated book recommendations from trusted sources, leading to a frustrating experience of sifting through vast amounts of irrelevant or low-quality titles. As a result, customers do not discover books that align with their interests, preferences, and reading habits.

Currently, a majority of customers come to the bookstore with a book title already in mind due to low prices, large selection, and ease of consumption. The bookstore lacks discoverability mechanisms throughout the customer's shopping journey.

The most simplified customer's shopping journey is through browsing the book store navigation or from search/results. Customers select a title, and after purchase, see a thank you page.

The Trusted Voices team has an opportunity to present customers with curated book recommendations throughout their entire shopping journey.

Our hypothesis is by creating a UX solution that provides customers with personalized and reliable book recommendations throughout their shopping journey, it will help customers simplify their evaluation of titles and increase their purchase confidence.