First let's apply Bayesian adjustment to this and see what happens. In order to use Bayesian adjustment as we discussed, we need to be able to compute first the overall average. Doing that is pretty straightforward in this case. We're just going to take the average rating overall all the products. Here it's assumed to be the top 20, right? And, that's not necessarily the valid assumption to make, because it, you know, including the top 30, top 40 could change it up, but we'll assume it's the top 20 here. And that gives an average rating of 4.59 When we add up the total number of stars and find the total number of reviews submitted and do the average rate and computation, for the overall average. The second thing, which is a little more difficult, to, to choose what overall num is. Alright, because as we saw before, we don't necessarily just want to use, the total number of reviews that were submitted for that. We may want to use, something instead, like, what Beer Advocate does, which is, to use the minimum number of reviews required to submit on the site. So we will try out a few different cases. One is the lowest number of reviews entered for any product in the list, which is 19. Second is the highest number of reviews for any product in the list, which is 1,064. Third is the average number of reviews per product right, so if you just take the average, total I have a number of reviews entered, that's 235. And fourth is the total number of reviews which would just be using the standard definition of overall num. Which is 4,704. So, if we do that out in each case, usually the four cases, and, we rank the lists accordingly. The first case we'll get, 17 followed by 1, 2, 15 and so forth. So, this is the rank order list. The second case will have 1, 2, 1, 3, 4, 2, 17, 15, and so forth. Third case something very similar to that. And in the fourth case something a little different again. So if you look at these quantitatively and compare them they, the first and the last ones seem to be pretty off. I mean they, you know, a lot of these are pretty random in terms of what our objective is and, to remind you our objective is to get to the rank-order list that's put on Amazon. And that would be 1,2,3,4,5,6, blah, blah, blah down to 20. However the second two right here, the highest number of reviews for any product, 1064, and the average number of reviews, 235, So you could do a little better, you know 1,3,4,2. In terms of at least of having, similar clusters of products grouped together. And as we said, the highest and the average seem to give better results, even though there are still clear exceptions in each case. And, to try to understand this we'll focus on products 8, 15, 17 and 20. Try to see where the ranks where they are. But for products 15, 17 and 20, the question is why are they put so low on the list and, additionally, why are they scattered from one another given that they're put so low on the list. Well they each of these have really the lowest number of ratings. So these, these are the products that had the lowest number of ratings entered for them. So that could explain why they're placed lower and demoted lower onto the list. And with Amazon, this case really demotes products for being lower on the list. But that still doesn't explain why they're scattered from one another because if they have the lowest number of ratings then why aren't they just placed at 18, 19 and 20? So if we look at product 15 however one thing we find is that 15's most helpful review had 60 of 61 people find it helpful. Which is good. And the most helpful review is also very positive. So, they've got five star and 60 out of 61 found that review helpful. On the other hand product 17 and 20 only had seven and six people find their top reviews helpful respectively, which could explain why 15 is ranked higher in this list. So this could explain why 15 is pushed up in the ranking. Now the question is, why are 17 and 20 placed far apart? Well, if we look at product 17, product 17's most helpful review was more recent, occurring in 2012. Whereas, product 20's most helpful review was less, recent. So this should be, more recent. And so that could be at saving grace is the fact that, it had a very helpful review occurring, recently. And notice also that despite being ranked so low, product 17 is actually the one that has the highest average rating. So in addition to what we just discussed, there are two more factors that could help explain this. First is that only half the reviews are from 2012, and second is that the average review reputation is fairly low, with all but two having less than ten helpful votes overall. And finally we should point out that product 15 had an extremely high quality review and its descriptive listings in the pros and cons of each case and this also could have pushed its rank higher. The next question we want to understand is why was product eight placed so high, given that its Bayesian ranking puts it around the 16th position? Well it turns that item 8's most helpful review had 79 of 79 people find it helpful. Additionally, over 10 of the top 5000 reviewers on Amazon had actually reviewed this product. And, rankings on Amazon extend to around 3 million people over 3 million people. Finally, one of the reviewers was a Hall of Fame Reviewer and he had given it five stars. So, when you're on Amazon, you can actually tell if the person a Hall of Fame Reviewer or not, meaning they're in top ten. And that person had given this five stars. So each of these may have caused product 8 to be severely promoted in the rankings because quality of reviews can override the quantity of reviews. [BLANK_AUDIO]
