With or without optimisation

	A	B	C	D	E	F	G	H	I	J
1	Suppose we sell baskets of 100, with 33% COGS.
2	Data-less	Stop at avg CAC = 33% of basket, to arrive at 33% margin
3	With data	Stop at max CAC < avgbasket Profit, to arrive at a 30% profit increase
4	color coding: price at which buying stops
5	CAC	Channel 1 users	Channel 2 users	Ch1 avg CAC	Ch2 Avg CAC
6	10	5	200	10.0	10.0
7	20	10	150	16.7	14.3
8	30	40	30	26.4	15.5
9	40	60	5	33.5	15.8
10	50	90	5	40.7	16.3
11	60	100	5	47.0	16.8
12	70	80	5	51.8	17.5
13	80	60	5	55.6	18.3
14	90	20	10	57.1	20.0
15	100	10	10	58.0	21.9
16	110	0	10	58.0	23.9
17	120	0	10	58.0	26.1
18	130	0	10	58.0	28.4
19	140	0	10	58.0	30.8
20	150	0	10	58.0	33.3
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22		Customers c1	Customers c2	Total customers	Profit C1	Profit C2	Total profit	Cost	Revenue	margin
23	Unoptimised	115	475	590	3815.9	15863.5	19679.4	19650	59000	33.35%
24	Optimised for profit	305	395	700	5981.3	19680.7	25662	21000	70000	36.66%
25			Total customers	118.64%		Profit	130.40%
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34	Optimised
35	Strategy	Profit maximisation through marketing optimisation
36	Data goal	Max CAC = Basket - COGS (buy customers as long as they still bring us profit)
37	Action	2nd auction bid = Max CAC * Conversion rate
38		waterfall bid -> estimate demand curve from past data and infer optimal profit point, requires aditional step
39	Result	If the max bid was above optimal, we stop selling to unprofitable customers
40		If the max bid was below optimal, we sell to more customers that are still profitable
41		typically, there are more cheaper customers and fewer expensive customers. By redistributing the channel spend, we achieve growth and profit increases
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