Customer reviews are the leading driver of purchasing decisions, influencing sales and product iterations. Amazon has expanded these capabilities by launching AI-generated review summaries. These tools collect and summarize the most relevant and frequent product feedback. The initial tests focused exclusively on electronics and small appliances before evolving to include hundreds of products across various categories.
As Amazon breaks into the AI space, how can you leverage and optimize these review summaries to enhance the shopping experience and guide crucial business decisions?
Reviews and ratings provide consumers with feedback and measurements of a product’s value. However, traditional reviews are limited since they emphasize quantitative rather than qualitative value. With review summaries, consumers gain insight into the reasons behind ratings and the products’ positive and negative qualities, allowing them to compare similar items. By sorting through relevant product attributes, these AI summaries compile buyer guides to present shoppers with quality options, personalizing and streamlining their experiences.
While Amazon’s AI-powered review summaries have gained traction in the market, only about 30% of brands have activated the tool. Consequently, many are unsure of how to analyze and predict summary trends. These review assessments have a broad and consistent structure and pattern beginning with the most common positive attribute. This is followed by a secondary favorable quality appearing in a list or sentence form. The final portion of review summaries consists of a key negative element and impartial sentiments. How can you segment these details to identify core product themes?
According to Spencer Kelty, the Head of Marketing at Yogi, conducting a thorough review analysis requires “sorting through your reviews by most recent, pulling out the attributes or themes that are mentioned, and recording the frequency of the mentions and whether they’re positive or negative.” This allows you to rank the most positive and negative features by percentage. Review summaries contain approximately 7-10 elements, so by categorizing them, you can correlate the percentages with your star ratings.
Once you’ve evaluated and structured your review summaries, you must apply the data to your business. Reviews are the most accessible form of consumer and competitor data, creating a feedback loop between your brand, the market, and the shopper. You can leverage an AI analysis tool to benchmark your review summaries against competitors and gauge consumer responses and behavior attributed to these aggregations. Then, use the data and insights to iterate products, update PDPs, refocus your marketing messages and claims, and replenish gaps in the market with innovations.
Yogi’s Co-founder and CEO, Gautam Kanumuru, shares a tip for brands to consider when applying review data, stating, “What you emphasize in your PDPs ends up in reviews. Because review summaries are based on what people are writing about, especially in the most recent reviews, it will show up in your review summaries.” This provides the opportunity to govern the narrative of your feedback notes. Ultimately, improving products with AI-powered review data boosts star ratings, sales, and growth.
