Growing Existing Customers’ Revenue Streams through Customer Referral Programs
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
Customer referral programs are an effective means of customer acquisition. By assessing a large-scale customer data set from a global cellular telecommunications provider, the authors show that participation in a referral program also increases existing customers’ loyalty. In a field experiment, recommenders’ defection rates fell from 19% to 7% within a year, and their average monthly revenue grew by 11.4% compared with a matched control group. A negative interaction between referral program participation and customer tenure reveals that the loyalty effect of voicing a recommendation is particularly pronounced for newer customer–firm relationships. A laboratory experiment further demonstrates that referral programs with larger rewards strengthen attitudinal and behavioral loyalty, whereas smaller rewards affect only the behavioral dimension. This article contributes to our theoretical understanding of the roles played by the commitment–consistency principle and positive reinforcement theory as mechanisms underlying the effectiveness of customer referral programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| 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 | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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