Live demo
15 series in. 4 alerts out.
One property, four content clusters, 60 days, 15 series. On day 41 something reranked. Total site sessions never left its weekly band, direct and paid never moved, and conversion rate held. Find the cluster and the day. Watch average position: lower is better in ranking, so its regression is a rise.
Every number on this page is computed in your browser right now, by the same CUSUM and Bayesian changepoint code that runs in production. Nothing here is a screenshot.
The comparison that matters
Against the rule most teams actually ship
The standard configuration is a percentage or sigma threshold against a trailing mean, evaluated per metric. Run it over the exact same 15 series and count what lands in the channel.
The false-alarm count is not a judgement call. The incident was injected at a known index, so every naive alert before that index is wrong by construction.
What the engine found
Detection run
| Metric | Trend | Baseline | Now | Change | Confidence | Method |
|---|---|---|---|---|---|---|
Organic sessions cluster=product-comparisons | 9342.53 | 6272.56 | −32.9% | 99.9% | CUSUM | |
Average position cluster=product-comparisons | 6.341 | 10.737 | +69.3% | 99.9% | CUSUM | |
Search impressions cluster=product-comparisons | 215.1k | 157.0k | −27.0% | 99.9% | CUSUM | |
Click-through rate cluster=product-comparisons | 4.34% | 3.11% | −28.2% | 99.9% | CUSUM |
Show your work
Organic sessions · cluster=product-comparisons
Baseline mean μ = 0.0011 and σ = 0.0361, both computed from the first two thirds of the day-over-day percentage changes. The slack k = 0.0181 is half a sigma, and the decision boundary h = 0.1445 is four. The accumulator runs on percentage changes rather than raw levels so a healthy growing series cannot drift across the boundary on its own.
alert if |Δ| > 2.5σ vs 7-day mean fired 5 times across this one metric, including 1 before anything was actually wrong. The statistical pipeline sent one message, on the day the regime actually changed.
What lands in Slack
One message, with the work already done
- 1.Pull the 412 comparison URLs and diff serving position by query before and after Aug 9.
- 2.Split impressions by surface to see whether the loss is classic search, the answer-engine panel, or both.
- 3.Check whether the lost queries are now answered inline without a click, which is a different problem from a ranking loss.
The written cause is generated only after the statistics confirm the change. The model never decides whether something is an anomaly — it explains one that has already been established. Getting that order backwards is how these products hallucinate.