Evolution of Referrals over Customers’ Life Cycle: Evidence from a Ride-Sharing Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses how referral generation and referral value evolve throughout the customer's life cycle as a function of service usage, experience level, and past referral behavior. We look at the referral behavior of 400,000 users of a large ride-sharing platform over the duration of a year. The upshot is that users make more and higher value referrals as they become more experienced with the service and when they are using the service intensively. However, as users make referrals, they are more likely to run out of friends to refer, leading to fewer (and lower value) referrals in the future. Based on these results, we suggest how digital platforms can improve their referral programs by tailoring them to how referral generation and referral value evolve over time. The richness of our data set allows us to address two shortcomings from previous studies: modeling dynamic behavior, such as the relationship between past and future referrals, and accounting for unobserved heterogeneity across users.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 it