Some of you really amuse me with your X% numbers. First of all the uniqueness % provided by any spinning software is INCORRECT/BAD. The article you get is not 100/50/15/1000% unique it is the seed that is % unique compared to the original. That means if you have 100 words and for each you add a synonym you get something like 100% unique. BUT if you spin that seed 10000 your average uniqueness will be very very low because you will get lots of articles that are not that unique compared to some other article that was generated. In order to correctly compute uniqueness you have to actually spin the articles X times and then compare each spin/article with every other generated. While some will be 100% unique, most will be much lower. The more spins you generate the lower the uniqueness for the whole batch is.
Most people overspin. They add just one-level spinning for words and then generate hundreds of spins. That produces low uniqueness. That's why I spend a whole week to spin one article, so I can spin it 1000-2000 times and still get high uniqueness. And that's why I spin it on multiple levels. The more you spin it the more unique spins you can get out of it, but it is not linear. The more you spin the higher the return for your effort is.
Then there's the problem of redundant spinning. Lots of people that offer spinning services (actually I think all of them) produce redundant spinning. That is, they spin it on multiple levels but the result (spun article) ends up similar to original or other spun article because it had lots of redundant spinning. What is redundant spinning? Let's say you have a sentence:
I was going to the market to buy some fruits.
At sentence level you add an equivalent for the above as:
I will go to the store because I want to buy a few fruits.
Now, when you do word-level spinning, you will use "will go" as a synonym for "was going", "store" for "market" etc. So the result is like you didn't even done the sentence level spinning. You might even change from active voice to passive voice or reverse the order of the sub-sentences (I bet I lost you here... google "english active passive voice" and also find out about how a sentence can be comprised from multiple shorter sentences, structurally - read on linguistics). The sentence ends up ONLY LOOKING different but at n-gram level (google n-gram too) they remain very similar. Text comparison algorithms (google, copyscape, etc) are based on n-gram comparison and n-gram statistical analysis. That's why even with a super spun article done at top quality by my spinners I still get 10-15% similarity though mathematically/statistically it should be zero for a number of spins below 1000.
OK, I'll stop here, I probably lost you completely.