What is auc (area under the curve)?
AUC measures how well a model separates converters from non-converters across all possible thresholds. 0.5 means no better than chance; 1.0 means perfect separation.
Concretely, AUC is the probability that a randomly chosen converter receives a higher score than a randomly chosen non-converter. It describes ranking quality independent of where you set a cutoff, which makes it useful for comparing models.
Context determines what counts as good. In domains with strong signal, 0.85 might be unremarkable. In lead conversion prediction, where outcomes depend heavily on factors absent from the data — whether an agent reached the person, what was said, what competitors offered — values in the 0.6 to 0.7 range can represent genuinely useful models.
AUC alone is not sufficient. A model can have respectable AUC and still be poorly calibrated, meaning its probability estimates are systematically too high or too low. For bidding, where the number is used directly to set a price, calibration matters as much as ranking.
Related terms
- Decile lift — Decile lift compares how often leads in the best-scoring tenth of a population convert versus the worst-scoring tenth. A lift of 10x means the top decile converts ten times as often as the bottom.
- Conversion propensity — Conversion propensity is a model's estimate of how likely a specific lead is to result in a completed sale or enrollment, usually expressed as a probability or a score derived from one.
- Retrospective validation — Retrospective validation tests a model against historical data where the real outcomes are already known, scoring past leads as if they were new and comparing predictions to what actually happened.