streaming data set example

	A	B	C	D	E	F	G
1	Example #1: Royalty distribution before top 10% increases their streaming
2	Deciles	# of streams	% of royalties
3	1st	100	0.97%
4	2nd	150	1.46%
5	3rd	200	1.94%		Mode	200	<- the most comon number
6	4th	200	1.94%		Average	1030	<- the "average" which determines the price per stream
7	5th	250	2.43%		Median	275	<- The point at which half of the users are above and half are below
8	6th	300	2.91%		Revenue	100	<- Total of all subscription fees
9	7th	500	4.85%		Royalty Per Stream	0.0068	<- Royalty rate as calculated by the big pool method
10	8th	1,000	9.71%
11	9th	2,000	19.42%
12	10th	5,600	54.37%
13	TOTALS:	10,300	100.00%
14
15	Example #2: Royalty distribution after the top 10% increases their streaming
16	Deciles	# of streams	% of royalties
17	1st	100	0.92%
18	2nd	150	1.38%
19	3rd	200	1.84%		Mode	200	<- the most comon number
20	4th	200	1.84%		Average	1086	<- the "average" which determines the price per stream
21	5th	250	2.30%		Median	275	<- The point at which half of the users are above and half are below
22	6th	300	2.76%		Revenue	100	<- Total of all subscription fees
23	7th	500	4.60%		Royalty Per Stream	0.0064	<- Royalty rate as calculated by the big pool method
24	8th	1,000	9.21%
25	9th	2,000	18.42%
26	10th	6,160	56.72%	<- increased usage by 10%
27	TOTALS:	10,860	100.00%
28
29		NOTES:
30		1. In both data sets 80% of users are "below average"
31		2. In both data sets the top 10% of users with the highest streaming consumption control more than half of the royalties
32
33	Example #3: Royalty distribution in subscriber share
34	Deciles	# of streams	% of royalties
35	1st	100	10.00%
36	2nd	150	10.00%
37	3rd	200	10.00%		GREEN = Their choices make more money under subscriber share
38	4th	200	10.00%		RED = Their choices make less money under subscriber share
39	5th	250	10.00%
40	6th	300	10.00%
41	7th	500	10.00%
42	8th	1,000	10.00%
43	9th	2,000	10.00%
44	10th	6,160	10.00%
45	TOTALS:	10,860	100.00%
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