Amazon listened to Sellers’ feedbacks related to the Customer Reviews Experience. Now brands can answer to negative reviews on Amazon which have a rating score from 1 to 3.

This is what Amazon said:

“We’re now offering a brand-only benefit that allows you to reach out to buyers who purchased your branded product directly from you, who left critical (1-3 star reviews) via templated emails that allow you to communicate with buyers via buyer-seller messaging.”

Answer to negative reviews on Amazon: How does it work?

With the goal of building trustful, established stronger relationships between brands and customers, Amazon developed a tool sellers can use to respond to negative reviews.

The tool is limited, for now, to sellers enrolled in the Amazon Brand Registry, that is to say for those sellers who produce their own unique products. Giving sellers the chance to respond to negative reviews is a strategic move for Amazon, in order to prevent fake reviews and keep sellers’ star ratings high without turning to paid reviews. If sellers can clear up misunderstandings and make consumers change their negative reviews, they might, in theory, be discouraged to buy fake positive reviews.

However it isn’t yet clear how useful this tool will be for sellers in real-world scenarios. Amazon provides sellers with a series of template emails which don’t give sellers much room to improvise. If they decide to contact negative reviewers they can only communicate in the way Amazon decided through these template emails. A seller showed Modern Retail examples of the templates, and they included opportunities to “offer a full, courtesy refund or replacement item” to customers or to “check with your buyer to clarify any product issues based on their review.”

These series of template emails give some sellers astonished and perplexed. If, on a one hand, it improves the communication among brands and consumers, on the other hand it is ot so powerful in discouraging the purchase of fake positive reviews. the fact is that as long as Amazon’s algorithm places heavy emphasis on both the number of reviews on a product and the star rating of those reviews, it’s hard to see a real end to the fake review problem.

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