What is the revenue impact of expanding capacity? The solution is not obvious, but the author's approximation is more accuare than you suggest. You obviously can't solve this without a stochastic ticket demand model, but 4.8 is much closer to the right answer than 0.
Also if the airline wants to maintain an 80% capacity, having an extra x% of seats means they add to revenue if they can maintain that occupancy percentage. In this case we don't even need to factor in 100% occupancy, which if anything would help the numbers as I'm sure last seats on a fully booked flight aren't cheap.
They target around 100% full. That being said, having more seats allows them to adjust their pricing model so that they have more seats to attempt to maximize revenue.
Depending on the demand structure of routes, those 6 seats may have wildly different values and could potentially be worth more than the average ticket price, on average for the airline.
Since this is counter-intuitive, I'll explain why briefly:
Airlines attempt to price discriminate by selling low priced tickets to leisure travelers and high priced tickets to business customers.
The problem airlines face is that business travelers book at the last minute.
Since business travelers book at the last minute, they need to estimate the number of business travelers who will be flying.
If the average leisure passenger ticket sells for $100, and the average business ticket sells for $500, just having 1 extra seat can add 5 leisure tickets worth of revenue assuming they sell the same number of leisure tickets.
Realistically, their model will readjust the optimal pricing changes overtime to capture the most value. Because the additional seats give the model more flexibility, I would not be surprised if the additional seats added more than an average of 80% of 6 seats multiplied by the average ticket price.
$100 for first 50% of capacity
$120 for next 20%
$170 for next 20%
$300 for next 5%
$600 for final 5%
What is the revenue impact of expanding capacity? The solution is not obvious, but the author's approximation is more accuare than you suggest. You obviously can't solve this without a stochastic ticket demand model, but 4.8 is much closer to the right answer than 0.