AMAZON CUSTOMER REVIEW CHANGES
Amazon is implementing changes to the customer review system in the US.
News of this change was recently announced in this c/net article.
The new customer review system uses a machine learning platform.
What does a machine learning customer review platform mean?
- The system will gauge which customer reviews are most helpful.
- The customer review system will be dynamic.
- The average star rating will be weighted by helpfulness.
- Verified reviews will have an edge toward helpfulness.
- Newer reviews will also count more toward helpfulness.
- Customer voting still impacts helpfulness.
So, exactly, how is this different?
- Average star rating may change, since it will be weighted by helpfulness. The most helpful reviews will carry more influence.
- Customer voting isn’t the only factor that affects helpfulness. Newer reviews and Verified reviews will carry more weight. There are probably other factors entailed in the “machine-learning.”
- Reviews deemed most helpful will have greater visibility on Amazon.
- The placement of reviews and average star ratings may change more frequently with the new system.
It will probably take time for the new Amazon customer review system to fully roll out and for the machine-learning to make an impact.
Amazon hopes to make the customer review system more useful through these changes.
The emphasis on newer reviews is to keep the information up-to-date. For example, if a product is improved to reflect criticism, newer reviews may reflect those changes, and thus should be more visible.
The emphasis on Verified reviews is in-line with Amazon’s recent lawsuit against alleged fake review websites. Amazon is striving to sustain customer trust in the review system.
KINDLE REVIEWS ARE SPECIAL
Machine-learning may have far-reaching consequences with regard to Kindle book reviews.
That’s because Amazon has more data to analyze:
- How many pages did the customer read?
- How much time did the customer spend reading the book?
- How does the customer’s behavior with this book compare to the customer’s behavior with other books?
- How many customers return this book?
Changes to customer reviews and to sales rank may both be related to recent changes announced for Kindle Unlimited.
Kindle Unlimited will now pay royalties based on how many pages customers read, effective July 1, 2015.
Royalty reports for Kindle Unlimited will show the number of pages read instead of the number of borrows.
Kindle sales rank may soon be impacted by the number of pages that customers read.
Similarly, machine-learning may look at the number of pages read and related data to help judge which reviews are more helpful.
If so, this would affect all Kindle e-books (not just those in Kindle Unlimited).
Write happy, be happy. 🙂
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Chris McMullen, Author of A Detailed Guide to Self-Publishing with Amazon and Other Online Booksellers
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I don’t know what to think about this. I can already see how one could easily abuse this system. Both to hurt and help.
Voting yes/no for helpfulness and its impact on review placement isn’t new, and Amazon already has ways of fighting the more obvious ways to abuse that. It’s a hot issue for top reviewers. So I don’t think it will be abused as much as people fear, and as the system learns, such abuse may even backfire. Amazon clearly has possible abuse in mind with its recent lawsuits and emphasis on verified reviews. I bet they have ideas in place already. But we’ll see.
Hope we do. The hard part is proving that abuse is happening. In the past, it seems it’s easier to prove people are trying to pad an author’s numbers than it is to prove somebody is trying to sink a book. Hopefully they do have something in mind. Love for a human to be involved too.
Yes, the Amazon reviewer forums are all a-buzz about this latest change from Amazon. It will sure be interesting to see how this all unfolds.
Amazon is very good at building anticipation. 🙂
This may help to reduce the impact of those 1 star reviews left by people who didn’t really read the book, or who like to spend their time leaving horrible and inaccurate reviews just for fun. Maybe?
One could hope..
I got worried at machine learning. I do not like it when machines get involved in stuff like that. I’m scared they are poised for world domination.
Customer review apocalypse? J/K 😉
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Thank you. 🙂