To Skim or not to Skim: Studying the Optimal Pricing Strategy for Technology Products
Bibliographic record
Abstract
Pricing of technology products often follows a skimming strategy in which a firm charges the highest price at product launch then reduces the price over time to attract additional buyers. Price-skimming has not been adequately examined in the literature related to technology products. Most pricing models proposed in the literature consider only initial product purchases while ignoring repeat purchases. However, for technology products, frequent upgrades often result in substantial repeat purchases. To fill this void, we develop an optimization model that accounts for both initial and repeat purchases. We find that the effectiveness of price skimming is highly dependent on the rate of repeat purchases. When the repeat purchase rate is low, firms may be better off delaying price reduction to accumulate more profit earlier in the product lifecycle, thus compensating for the decline in sales that occurs later in the lifecycle. For example, price skimming may not be the optimal pricing strategy for fad products with low repeat-purchase rates due to the product losing popularity in a short time period. Furthermore, contrary to common expectations, in markets highly sensitive to price changes firms may get away with charging a higher introductory price but must decrease the price over time to motivate sales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".