This content originally appeared on NN/g latest articles and announcements and was authored by Katie Sherwin, Taylor Dykes
Summary: AI-generated review summaries extract qualitative key themes from customer feedback, helping shoppers quickly assess what purchasers think about the product.
When designed well, AI summaries of ecommerce reviews helped our study participants quickly gauge product quality and fit for their needs. But when AI summaries of customer reviews were vague, poorly formatted, or blocked actual reviews, they wasted time. To truly improve upon the traditional review-reading experience, designers should leverage what AI is good at without forgetting why customers rely on reviews in the first place.
Traditional Summarization of Customer Reviews
In our decades of ecommerce research — including the past few years with AI features emerging on the scene — reviews have always played a major role in customer decision making. But the volume of reviews has presented a design challenge. While customers rely heavily on reviews, they typically don’t want to read dozens of them. When there are hundreds or thousands of reviews for a single product, it’s difficult for customers to get a quick understanding of reviewers’ conclusions.
Over the years, ecommerce designers introduced clever tactics to give users a quantitative sense of how other customers rated products. To help people navigate an ocean of opinions, retailers have implemented features like:
Read Full Article
This content originally appeared on NN/g latest articles and announcements and was authored by Katie Sherwin, Taylor Dykes
Katie Sherwin, Taylor Dykes | Sciencx (2025-06-13T17:00:00+00:00) AI Summaries of Reviews. Retrieved from https://www.scien.cx/2025/06/13/ai-summaries-of-reviews/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.