Frustration-Based Promotions: Field Experiments in Ride-Sharing
Bibliographic record
Abstract
The service industry has become increasingly competitive. One of the main drivers for increasing profits and market share is service quality. When consumers encounter a bad experience, or a frustration, they may be tempted to stop using the service. In collaboration with the ride-sharing platform Via, our goal is to understand the benefits of proactively compensating customers who have experienced a frustration. Motivated by historical data, we consider two types of frustrations: long waiting times and long travel times. We design and run three field experiments to investigate how different types of compensation affect the engagement of riders who experienced a frustration. We find that sending proactive compensation to frustrated riders (i) is profitable and boosts their engagement behavior, (ii) works well for long waiting times but not for long travel times, (iii) seems more effective than sending the same offer to nonfrustrated riders, and (iv) has an impact moderated by past usage frequency. We also observe that the best strategy is to send credit for future usage (as opposed to waiving the charge or sending an apologetic message). This paper was accepted by Vishal Gaur, operations management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".