This is shady as hell, because I am 100% sure I wrote this review. I even wrote it twice, once on amazon.com and once translated on a local amazon site. This is slightly infuriating.
And it encourages and even facilitates shady shit.
A bit hyperbolic, but also applies to pretty much every culture which has ever existed.
They said that they wanted their review to appear in the "top negative review" column since it didn't have a shot at the "top positive review" column.
I was dumbfounded.
Really makes you question what is going on in the heads of people around you.
Not her fault. What choice do you have but to assign your own arbitrary meanings to each star, and how aren't those going to vary from person to person?
I never looked at 5-star rating systems the same.
On the other hand, I don't think it's really a problem as long as users are consistent. It doesn't matter how "harsh" they are as long as everyone has similar odds of getting a harsh reviewer.
I do tend to leave slightly below-average ratings because I like leaving room for truly exceptional products/experiences to get a higher rating.
Many times a (good faith) vendor will make small, incremental changes to a product that addresses concerns that low-star reviewers point out. However, the product page remains the same - it does not distinguish between "Seattle Coffee Beans - Batch A" and "Seattle Coffee Beans - Batch C". It simply says "Seattle Coffee Beans".
So if you leave a review for Batch A, but the product being sold is now Batch C, should the review still be displayed?
A bad book is a bad book. Batch B of the said book is mostly a new "product" listing on Amazon with its own set of reviews and ratings.
Also if Batch C is being sold or something similar (to batches), a better idea would be (IMO) to have the product owner comment on the review, saying we have fixed this in batch 2 or batch B or something
That is a good idea, but that still does not fix the aggregate data. When someone searches for "Coffee", the aggregate number of reviews is still considering that low-star review. So the "Seattle Coffee Beans" overall rating for Batch C could be a 4.5 / 5, but for all batches is a 2.5 / 5.
The easiest way to fix this is to remove old, out-of-date reviews. This should apply to positive reviews too, but I have no idea if Amazon is doing this.
Another approach is what Steam did - have a long-term rating and a "recent" rating. See "No Man's Sky" as an example [1] - a product that started poor, but improved over time. This helps prevent product brigading and gives vendors an opportunity to bounce back from negative reviews.
Tastes change. That said, you can still find those old reviews. Not so in all cases on Amazon...
Or make a new listing, and in the description mention what has been fixed.
However, I am quite sure I have seen elsewhere this feature. It's not bad. It allows to have more recent opinions about the item.
Here, it creates the impression that my comment was swept under the rug for some dubious reasons. And this is probably the last time I ever comment on Amazon. That does not really create a great trust in the system.
That said, video games these days can change a lot from one year to another, whereas almost all of the products on Amazon will not do so.
I believe that got added to help people recognize and filter out review-bombing.
Books for example. Some print runs are terrible. So even if the content is great, terrible print quality or construction can lead to bad reviews.
Some books have numbered editions. But if the content is unchanged, should they be different products?
For example, No Man's Sky was one of the biggest letdowns in recent gaming releases. It was later patched to be much better.
Check out the reviews here: https://store.steampowered.com/app/275850/No_Mans_Sky/
I was doing this for free, so I just shrugged my shoulders and moved on. I'm not gonna fight the system, but there is money involved I could see how someone would.
"Amazon calculates a product’s star ratings using a machine learned model instead of a raw data average. The machine learned model takes into account factors including: the age of a review, helpfulness votes by customers and whether the reviews are from verified purchases."
Selling a marketing tool for businesses and products.