Behavior-based pricing: an analysis of the impact of anticipated regret
Bibliographic record
Abstract
Traditional behavior-based pricing (BBP) literature suggests that firms should offer lower prices to incentivize new customers to switch. However, at the time of switch, customers are often uncertain about their true needs or valuations of the product. Accordingly, they may experience repeat-purchase or switch-purchase regret, depending on whether they have bought a product from the same brand or switched to another brand. This paper investigates the impact of customers’ anticipated regret on firms’ BBP strategy and profits. Contrary to prior research which generally shows that firms performing BBP yield lower profits, we find that firms’ profits can increase or decrease in the presence of anticipated regret. When customers’ anticipated regret is sufficiently strong, firms can benefit from performing BBP. In addition, we find that firms have to change their traditional BBP strategy from rewarding new customers to rewarding repeat customers when repeat-purchase regret is sufficiently high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".