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We use Statistical Modeling to position and adjust the number of positive reviews based on the number of unnatural reviews, so you can make your decision based on high-quality data. The algorithm is not perfect, but has high accuracy rates. Amazon has done a good job of gathering hundreds of millions of feedbacks, but many brands have chosen to abuse it, flooding it with low quality and biased reviews to try and boost their own profits.

An Amazon bestseller is one that has at least 150 sales per month and the more sales it has, the easier it becomes to rely on other buyers' feedback.

The conclusion is based on: 1) To derive an overall view of product recommendations, we applied various statistical analysis and artificial intelligence techniques. 2) Reviews and ratings were analyzed, and we consider a rating of 4 and above to be an indication of a great product. 3) A meaningful result requires at least 100 orders, so the more orders we analyze, the more we trust the product rating conclusion more.

A product's price history graph - This can help you spot real discounts from fake ones. We keep a record of the price and then monitor changes on a daily basis, making it possible to take advantage of sales and limited-time offers.

We check the overall store feedback score, the higher the score, the better the chances of getting a good service. Over 91% feedback is considered positive feedback.