Analyzing Customer Conversion Patterns: A Survival Analysis Approach to Multi-Channel Attribution
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 report explores the dynamics of customer conversion by examining the relationship between visit behaviour and conversion outcomes across various marketing channels.By condensing customer visit data into a singular representation for each customer, we capture the time intervals from their first visit to either a conversion or their last recorded visit, thereby categorizing customers as converters or non-converters.The analysis utilizes survival analysis techniques to estimate conversion probabilities over time for different marketing channels, allowing for insights into the effectiveness of each channel in driving conversions.Furthermore, the report introduces a causal inference framework to assess the impact of offline marketing interventions, specifically television advertising, on web traffic.By employing Bayesian structural time-series models, we generate counterfactual predictions to isolate the uplift attributable to marketing efforts.This comprehensive approach highlights the interplay between digital and offline marketing strategies and provides actionable insights for optimizing customer engagement and conversion.
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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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.025 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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