Start by framing the problem as modeling a count distribution with excess zeros and a heavy right tail, then justify a zero-inflated negative binomial or hurdle model. Sketch the relative positions of the mode, median, mean, and 95th percentile, and explain how retention and fraud suppression shift the distribution by day 60. Finally, recommend robust executive metrics (e.g., median and 90th percentile) and a diagnostic like a zero-inflation test or QQ plot.
Pro tip: Emphasize that heavy tails make the mean misleading; executives should focus on percentiles and the proportion of users with zero transfers. Also, mention that fraud suppression may truncate the tail, so monitor both the tail and the zero mass over time.
Justify a zero-inflated negative binomial (ZINB) or hurdle model because transfer counts are overdispersed, have excess zeros (many users never transfer), and a heavy right tail (power users).
Explain that the mode is at zero, median is low (e.g., 1-2), mean is pulled right by the tail, and the 95th percentile is much larger (e.g., 10-20). The mean exceeds the median due to right skew.
Retention effects: surviving users may transfer more, increasing the mean and tail, but fraud suppression may truncate extreme values and increase zeros if fraudulent accounts are removed.
Suggest median transfers per user (robust to outliers) and the 90th percentile (captures heavy users) as summary statistics. Avoid the mean due to heavy tails.
Validate assumptions with a zero-inflation test (e.g., Vuong test) or a QQ plot of the fitted distribution against observed counts, and check for overdispersion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.