Discrimination … charging different customers different amounts

Degrees

1st degree: individual prices

charging each customer the maximum they are willing to pay
perfect requires the ability to distinguish the maximum ability to pay → very hard
- lower prices the longer in cart does not work → customer can pretend to be low willingness to pay and then wait for the price reduction
- easier with large information density of customers (e.g. Amazon)
imperfect is more doable
- e.g. doctors, accountants can partially figure out the customers willingness to pay

depending on when and how much you buy there are different prices
- customer base is cut into blocks, each block has maximum price
surge pricing, Peak Load Pricing as well
- also includes Economy of Scale → higher volume, lower price
e.g. electricity price (normally not consumer price, but actual grid price)
e.g. 20 GB data included, after that each GB costs 5$
- not every unit sold (consumed) is priced equally
e.g. flight tickets → tickets at Christmas are more expensive

different groups of customers pay different price
e.g. lower prices for students, seniors
e.g. private vs business clients
e.g. branded vs no-name products
- also includes non-real product differences
e.g. more expensive drinks around 24:00 → highest demand then
e.g. selling for different amounts in different countries (before transport costs, tax, etc)

within all price regions the Marginal Revenue will equal the Marginal Cost since we can adjust the quantity sold in all markets/groups
- all groups are linked by the same production costs
- $M R_{1} = M R_{2} = MC$
- otherwise not maximized profits
rule of thumb: $M R_{1} = p (1 + \frac{1}{ε _{D_{i}}})$ $M R_{1} = M R_{2}$ $\frac{p _{1}}{p _{2}} = \frac{( 1 + \frac{1}{ε _{D_{2}}} )}{( 1 + \frac{1}{ε _{D_{2}}} )}$

distinguish willingness to pay by time of purchase
- impatient consumers pay more
e.g. devices become cheaper after some team
- highest price right after launch