Apple is the textbook example. A new iPhone launches at a premium, early adopters pay it, and the price drifts down (or the model gets discounted) as the next launch approaches. Each phase of the price curve targets a different customer segment willing to pay a different amount.
Skimming trains your customer base to wait. If buyers learn that the price always drops 30% within six months, the smart ones never buy at launch. You end up with a high-price phase that nobody participates in and a low-price phase where you sell most of the volume at thinner margins.
It can also damage brand trust. Customers who paid full price feel punished when the same product appears at a meaningful discount soon after. Some brands manage this with explicit "new generation" framing; others get burned by it.
For most e-commerce stores, skimming is less about a single launch and more about a rolling strategy across new arrivals. New stock launches at full price, gets gentle markdowns at predictable intervals, and clears at end-of-season. Repricing tools handle the markdown phase, applying time-based rules that move prices through the curve automatically.
Example: A consumer electronics retailer launches a flagship headphone at $349. After 60 days, an automated scheduled rule drops it to $299. After 120 days, $269. The store captures the early-adopter premium without any manual price management, and the rule stops the moment a competitor's behaviour would push the price below the floor.