Using thirteen years of data representing more than 80,000 deliveries, I find that the company is extremely adept at determining how many bagels and donuts to deliver to a particular customer on a given day. In stark contrast, the company appears to price on the inelastic portion of the demand curve for the entire period, thereby foregoing a substantial share of available profits. I argue that these results generalize well beyond this particular case study: firms are likely to be close to the efficient frontier on dimensions for which there is frequent and informative feedback regarding profits, but absent that feedback, systematic deviations from profit maximization are more likely.
This is a fantastic paper, as the data set is really well suited for seeing how well a firm (Feldman) does in maximizing profits. Although the discussion of optimal pricing in the paper is simplistic (Levitt has to ignore dynamic concerns and customer attitudes towards him, as well as any real analysis of how higher prices would affect the payment rate), it really highlights the way people price in real-life. And it's hard to refute Levitt's argument that pricing is below what would be optimal. (The main argument here is that revenues and profits go up after the price increases that do occur.)
I had a student once that owned a coffee shop, and he told me that he had really only raised his prices once. That price increase was in response to an increase in his rent (a fixed cost, that theoretically shouldn't affect pricing at all, let alone be the only reason for it). I was able to get him to admit eventually that the price increase also corresponded to a price increase (and rent increase) for a nearby competitor too, which allowed me to salvage the lesson on fixed prices not affecting prices, but I think it's pretty clear: firms often times lack (or ignore) the data to make "optimal" pricing decisions.