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Suppose you have a sausage maker. He buys batches of ground meat, then makes and sells sausages. Suppose each batch of ground meat makes N sausages, and each batch has specific level of quality that dictates the price of the sausage, so each batch of sausages is priced differently. Let's also assume there isn't any shortage of ground meat so the sausage maker can always make more sausage.

The sausages are also priced dynamically in that the price you pay per sausage decreases as you increase the size of your order (i.e. buy 1 for \$2 and 3 for \$5).

Let's also assume that consumers have perfect information about the quality of the sausage. The sausage maker doesn't know the best way to price the sausage based on the quality, so the sausage maker wants to evaluate the performance of each batch of ground meat to try to model the consumer's demand as a function of quality. What would be the best metric?

Naively one could look at the lifetime value of the batch (i.e. how much total revenue was generated from sales from this batch). But this seems wrong, because it fails to take into account the time it took to sell it, and it rewards batches that consumers buy less of in bulk because the price per unit is higher (when in reality consumers buying more of a batch in bulk is an indication of high quality).

So revenue per day (or some time period) seems like the next logical metric, but this also seems naive.

I am certain that this sort of problem is in the Economic literature, but I am not familiar enough to find it. Any guidance would be greatly appreciated.

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