Customer reviews are one of the oldest and most important features on Amazon. When we first launched reviews in the U.S. in 1995, the idea was radical. People scratched their heads when we said we wanted to give customers the opportunity to voice their honest opinions on products—the good, the bad, and everything in between. While the idea wasn’t universally embraced, it was embraced by our customers, and customers in Australia have been using reviews since the launch of our retail offering here in 2017.

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Customers loved learning from each other and sharing feedback within the Amazon community. They wanted to hear from people who spent their own money, got the product, and used it. Other companies across retail began to adopt customer reviews on their own sites, and soon reviews became synonymous with online shopping. Today, it’s hard to imagine making a purchase decision without first understanding how other customers feel about a product.

Over the years, we’ve made many updates to create a more useful experience and increase participation with two distinct yet interconnected audiences—those leaving reviews and those leveraging reviews to decide what to purchase next. We continued to invent features that now seem commonplace, such as the ability to include a review title, photos, and videos. To increase the range and diversity of reviewers, in 2019, we began enabling customers who purchased an item on Amazon to provide feedback by leaving a quick star rating without having to write a full text review.

As the value and importance of reviews grew along with the size of our catalogue, we got a lot more intentional about how we helped customers create reviews, as well as how we presented them back to customers. We made it easier for customers to submit their opinions on their purchases by proactively asking customers for feedback in the app. We also enabled reviews written in one country to surface on a product page in another country. Today, if a product is the same in every region, customers can benefit from opinions and insights of customers around the world. To help customers shop for apparel with confidence on Amazon, we created a feature that allows customers to filter certain clothing reviews to show only those written for people who are a similar height and weight to get a better idea of fit. We added a similar feature to help customers shop for children’s books by asking reviewers to share the age of the person who read the book when submitting their review.

We obsess over helping customers feel confident in their purchase decisions—even if that decision is not to make the purchase. Last year alone, 125 million customers contributed nearly 1.5 billion reviews and ratings to Amazon stores—that’s 45 reviews every second, making reviews at Amazon an incredible resource for customers.

New AI-generated customer review highlights

We want to make it even easier for customers to understand the common themes across reviews, and with the recent advancements in generative AI, we believe we have the technical means to address this long-standing customer need. Want to quickly determine what other customers are saying about a product before reading through the reviews? The new AI-powered feature provides a short paragraph right on the product detail page that highlights the product features and customer sentiment frequently mentioned across written reviews to help customers determine at a glance whether a product is right for them.

A smart phone with an Amazon product review on the screen.

Now available to a subset of mobile shoppers in Australia across a broad selection of products, the AI-generated review highlights also feature key product insights and allow customers to more easily surface reviews that mention certain product attributes. For example, a customer looking to understand whether a product is easy to use can easily surface reviews mentioning “ease of use” by tapping on that product attribute under the review highlights.

A smart phone with an Amazon product review on the screen.

We are always testing, learning, and fine-tuning our AI models to improve the customer experience and, based on customer feedback, may expand our review highlights feature to additional categories and customers in the coming months.

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How we keep the Amazon community safe and reviews authentic

As we continuously improve the reviews experience, we’re also working to ensure customers continue to see the content and opinions that will be the most valuable to them. Our Community Guidelines help set the parameters for both our machine learning models and our human moderators to keep the community safe and the reviews relevant, while allowing customers to express themselves and their opinions with as much personal expression as possible. We believe this leads to a richer, better, and more trustworthy set of reviews. Customers who share their opinions appreciate this, and those that read them do too.

We welcome authentic reviews—whether positive or negative—but strictly prohibit fake reviews that intentionally mislead customers by providing information that is not impartial, authentic, or intended for that product or service. Honest reviews provide customers with the information they need to make confident purchase decisions.

We continue to invest significant resources to proactively stop fake reviews. This includes machine learning models that analyse thousands of data points to detect risk, including relations to other accounts, sign-in activity, review history, and other indications of unusual behaviour, as well as expert investigators that use sophisticated fraud-detection tools to analyse and prevent fake reviews from ever appearing in our store. The new AI-generated review highlights use only our trusted review corpus from verified purchases, ensuring that customers can easily understand the community’s opinions at a glance.

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We’ll continue to make it easier for customers to submit reviews and add new content types while leveraging AI to help customers see the highlights of reviews. Should I order this, that, both, neither? As long as reviews can help make the path clearer for our customers, we are happy with the outcome.

Learn more about how Amazon is using AI.