The Future of Reviews: Amazon AI Summaries

First, a quick catch up in case you’ve missed the news. 

Amazon has rolled out a new feature that uses generative AI to analyze a product’s reviews and provide a brief synopsis in plain language, highlighting the most common features and themes mentioned. 

These summaries are keyword driven, focusing on terms that appear the most across reviews and correlating them with positive or negative review outcomes in the form of a paragraph describing the experience consumers have with that product. 

At the time of writing, this feature is only available on Amazon’s mobile app and is limited to just a few products. But that won’t be the case for much longer.

Amazon AI Review Summaries: How it Works

How do Amazon review summaries impact brands?

The internet is changing. Consumer habits are evolving, and the AI goldrush is making its way into mainstream consumer experiences quickly. AI review summaries pipe review data straight to customers, giving them instant access to data from hundreds or thousands of reviews without spending the time to read them all. 

Review summaries that align with consumer needs and trends will be the big winners, driving more sales and creating happier customers. Products that have traditionally relied on high ratings and large numbers of reviews may discover their advantage is diminished, with critical negative themes highlighted in their summaries. 

Amazon on the other hand only cares about driving sales. Here are a few of their driving motivations for this new feature: 

  • Leveling the review playing field. 

With this tool, Amazon significantly reduces consumers’ need to scroll through reviews looking for relevant information. It even reduces the importance of overall rating by focusing on themes over a qualitative score. Often, products with more reviews win even if the product with fewer reviews is better overall. This puts new products at a disadvantage against incumbents, and slows product release momentum. 

  • Reducing time to purchase. 

For ecommerce retailers, time to purchase is a key metric. Less time to purchase means fewer abandoned carts and more sales overall. By showing consumers review summaries, Amazon gets the key information in front of people much faster, leading to faster decision making. No more scrolling through dozens or hundreds of reviews to find the info you are looking for. 

  • Personalized shopping. 

Amazon is already at work to create ChatGPT-style shopping experiences, where recommendations can be given organically and conversationally. AI review summaries identify the core positive and negative attributes of a product, which can easily be connected to other personalized recommendation engines to provide truly curated suggestions that lead to better consumer outcomes. 

How should brands react?

It’s time to get serious about reviews. It's as simple as that. 

The world’s largest eCommerce retailer’s first foray into generative AI is leveraging review data. This illustrates just how central reviews will be in the future of ecommerce. The number, quality, and the content of product reviews will be exponentially more important in the coming years. 

This trend isn’t going to reverse, and Amazon is just the first to use AI and review data to inform customer experience. Other retailers like Walmart will not be far behind. Review data is the most plentiful, timely, and accurate consumer data available, and it will be one of the foundations for AI-based personalization engines.

Smart brands have the chance to embrace this opportunity to not only leverage this exciting new feature, but lean into the new direction of ecommerce and prepare for an AI-first future where data powers purchasing decisions. 

By getting ahead of the curve and analyzing reviews for consumer insights, you can take control of your brand’s future and ensure that you actually know exactly what Amazon’s summary will say about your product before your customers do, giving you the chance to make adjustments and make course corrections if need be (via PDP updates, marketing iteration, and more). 

Turning Reviews into Results

Start thinking about reviews like Amazon: As a predictive data source. 

At the most basic level, better reviews lead to more conversions. But now we are going beyond that. It’s not about simply better or worse, and it can’t be distilled down into a simple star rating anymore. It’s more experiential than that. 

Star ratings have always been an abstraction to make qualitative feedback quantitative. But reviews are like consumers, complicated. Any given review may contain positive, negative, or even neutral themes. A consumer experience with a theme or feature isn’t necessarily negative if it's mentioned in a negative review, and Amazon is starting to acknowledge this nuance and transcend the need for (or at least the importance of) star ratings, and get at the core question: what might you like and dislike about this product.

Retailers like Amazon want to get as close to perfect recommendations as possible, where every product they show a consumer is a great fit and results in a purchase. And they will use every data source they can to move closer to that goal.  

To get ahead of this, brands need to think like Amazon and try to understand what consumers like and dislike about their products. In real time. Proactively and on an ongoing basis, using review data. 

Large incumbent brands should see this as a wake up call to get deeper into review data analysis at an enterprise level, and understand exactly where their consumers’ experiences exceed or miss expectations and adjust messaging and PDPs accordingly. By aligning positioning in marketing and PDPs, review scores and quality can be increased significantly without any product changes. Here is an example where Tylenol was able to reduce negative themes in their reviews dramatically with PDP and marketing changes. 

Challenger brands can iterate their products more quickly to adjust and change based on the customer feedback they receive. This new feature goes a long way to leveling the playing field for upstart products that don’t have years of reviews. By integrating review analysis, these brands can use their agile nature to their advantage and adjust PDPs, marketing, and even their products to align with consumer trends and feedback quickly, giving them huge opportunities to shape their Amazon review summaries as they see fit. 

If Amazon added review summaries to your brand’s products tomorrow, what would they say?

If you don’t have a clear, data-backed answer directly from your Amazon reviews, you are at a disadvantage. Manual review analysis is a great place to start, but if you are ready to start getting the most out of your product reviews, Yogi is here to help. 

At Yogi, we help consumer brands answer questions straight from product reviews and ratings. Our platform aggregates reviews from across the web, uses AI to analyze it, and shows you exactly what consumers like and dislike about your products. And the best part? It shows you what consumers like and dislike about your competitors too, letting you know exactly how your brand stacks up.

In March, Yogi rolled out a new feature called Summaries that uses generative AI to summarize the top themes for a product instantly, showing you top positive and negative themes. Yogi offers full data visualization and lets you drill down and compare data sets to answer complex questions like:

  • How have consumers felt about our products’ value compared to the competition over time? 
  • What themes should our marketing and PDP focus on to improve reviews (and review summaries) while capitalizing on competitor weaknesses? 
  • What impact did a recent packaging change have on Amazon order arriving damaged? 
  • What product changes should we prioritize for sales impact?

Looking to make the most of your product reviews? Click here to speak with a category expert.