Time-Aware Subscription Prediction Model for User Acquisition in Digital News Media
Bibliographic record
Abstract
User acquisition is one of the most challenging problems for online news providers. In fact, due to availability of different news media, users have a lot of choices in selecting the news source. To date, most of digital news portals have tried to approach the solution indirectly by targeting the user satisfaction through the recommendation systems. In contrast, we address the problem directly by identifying valuable visitors who are likely potential subscribers in the future. First, we suggest that the decision for subscription is not a sudden, instantaneous action, but is the informed decision based on positive experience with digital medium. As such, we propose effective engagement measures and show that they are effective in building the predictive model for subscription. We design a model that not only predicts the potential subscribers but also answers queries about the subscription occurrence time. The proposed model can be used to predict the subscription time and recommend accurately the “potential users” to the current marketing campaign. We evaluate the proposed model using a real dataset from The Globe and Mail which is a major newspaper in Canada. The experimental results show that the proposed model outperforms the traditional state-of-the-art approaches significantly.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".