Amazon reviews are very important to consumers. The review star rating, the number of negative reviews on the homepage, and the quality of negative reviews are all important information for buyers to evaluate when placing an order. To a certain extent, it will directly affect the sales of products. Now that the peak seasons such as Christmas, Black Friday, and Cyber ​​Monday are approaching, if there are too many negative reviews at this time, the subsequent conversion of the peak season will have a great impact.

Today, Xiaohu uses this article to analyze which factors affect the star rating of the review, and how to optimize and adjust through effective methods. In Xiaohu's opinion, the stars that affect the review will have the following points, which are for reference only.

1. The retention time of the review and the number of likes

The retention time of a review refers to the time between a review and the present. Because Amazon will regularly scan reviews, the longer a review is retained, the more true it is, and some old reviews will have a much higher weight. The more likes a Review has, the more it will be ranked first, so that you can perform operations to like and stick high-quality comments to the top.

2. Comment word count and content

The artificial intelligence system has introduced the Amazon A9 algorithm. On the one hand, it can detect fake reviews, and on the other hand, it can help reviews with star ratings. Therefore, I tend to leave reviews naturally. Reviews with good text volume and quality will inevitably have some advantages in star rating.

3. The number of included and adopted comments

The number of review acceptances refers to the number of reviews that are clicked by other buyers; the more acceptances, the greater the impact on the star rating of the review. When a low-star negative review is liked too many times, it may have a negative impact, so be careful.

4. The number of VP evaluations

VP review refers to when the product review is marked as Verified Purchase, indicating that the customer who wrote the product review has purchased the product on Amazon; only customers who confirm that they have purchased the product on Amazon can add this label to the review. Customers who identify reviews can use this information to decide which reviews are helpful to their purchasing decisions.

How we should use effective methods to obtain reviews is very important. Sellers can use Saihu ERP, the Request a Review function of Saihu ERP, which can filter orders according to the rules set by users, and automatically request reviews in batches for Amazon's official "Request a Review". And you don't need to install any plug-ins, you can operate it directly in the Saihu ERP system, and new users can get 3,000 orders for free when they register. Come and experience it.

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