<b>Reading the clearing-price distribution in log-level data</b>

<b>Reading the clearing-price distribution in log-level data</b>

Log-level data (the raw per-impression record of bids, wins, and prices) is usually mined for fraud or viewability. Its richest signal is the shape of the clearing-price distribution, and most buyers never plot it.

What to extract per segment:
— Your bid, your win/loss flag, and the price paid on wins.
— Where available, the minimum-to-win or floor the SSP reveals on losses.

Now build the histogram of clearing prices for a single segment. The shape tells you the auction's character:
— A tight, single-peaked distribution means stable competition — your shading can be aggressive and precise.
— A bimodal distribution usually means two distinct demand sources (e.g., a PMP floor cluster plus open-exchange bids) sharing the same segment — average-based bidding is wrong here; you should bid against each mode separately.
— A long right tail means occasional high-value competitors spike the price — chasing those wins destroys your average surplus.

The actionable read: set your bid near the body of the distribution you can profitably win, and deliberately concede the tail. Buyers who bid to the mean of a skewed distribution systematically overpay, because the mean sits above the price that wins most impressions.

Why it matters: the clearing price isn't a number, it's a distribution with structure. Log-level data lets you see that structure and bid to its shape instead of its average.