On the CDNOW panel, with a real 39 week holdout as ground truth, a Pareto/NBD model flags 32 percent of the top decile by historical spend as probably inactive. The median flagged customer then buys nothing at all, against a median of 130 for the rest of the decile. Extrapolating repeat spending overstates the holdout by 35 percent and the model undershoots it by 16, so the level is not where the argument is strongest. Fitted in R with CLVTools, with a bootstrap confidence interval computed in Python.

LLM synthetic respondents are known to produce response distributions that are too narrow. Nobody writes down what that costs you. Running a conjoint through a mixed logit and into a market simulator, a panel with correct mean price sensitivity and compressed heterogeneity reproduced the human holdout choice shares almost exactly, then set the revenue-maximizing price 26% too low and sized the premium segment at less than half its true value. Simulated in R with logitr, cross-checked in Python by Gauss-Hermite quadrature.