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
Relationship marketing assumes that firms can be more profitable if they identify the most profitable customers and invest disproportionate marketing resources in them. While intuitive, such strategies presume that a firm can accurately predict the future profitability of customers. In particular, we argue that the feasibility of such strategies depends on the probabilities and costs of misclassifying customers. This paper presents a detailed empirical evaluation of how accurately the future profitability of customers can be estimated. We evaluate a firm's ability to estimate the future value of customers using four data sets from different industries. Out-of-sample estimates of predictive accuracy are provided. We examine (1) the accuracy of predictions, (2) how accuracy depends on the length of time over which estimates are made, and (3) the predictors of the firm's best customers. We propose the 20–55 and 80–15 rules. Of the top 20%, approximately 55% will be misclassified (and not receive special treatment). Of the future bottom 80%, approximately 15% will be misclassified (and receive special treatment). Thus, a firm cannot assume that high-profit customers in the past will be profitable in the future nor can they assume that historically low-profit will be low-profit customers in the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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