A low alert fire rate can mean your threshold is too strict—or that the event you are watching rarely happens. In a 2025 case study, Juan Camilo Auriti reported that his score-drop alert fired seven times across 1,963 consecutive-report pairs, a 0.36% rate. Lowering its thresholds could help, but only 6.2% of those pairs showed any score decline at all. That underlying frequency limited the upside.
Why seven alerts did not prove the thresholds were the only problem
A score-drop alert is governed by two different things: how often the monitored score actually falls, and how much it must fall before the alert fires. A strict rule can suppress alerts even when declines occur. But changing the rule cannot produce more alerts than the data contains qualifying events.
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Auriti described an alert that required both a total-score decline of at least five points and a decline of at least three points in one or more categories. He said those cutoffs were chosen by intuition, not derived from historical data. In his account, the shipped alert had been running for four months before he checked its behavior. Across 1,963 consecutive-report pairs, it fired seven times: a reported rate of 0.36%.
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Backtest the alert on the same unit it evaluates
To understand a low fire rate, examine historical observations using the same comparison the live alert makes. In this case, the unit was a pair of consecutive reports for the same domain. Comparing unrelated reports, or reports belonging to different domains, would produce a distribution that does not reflect the alert’s actual decision.
Auriti’s SQL approach uses LAG to place each report beside the preceding report for its domain:
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LAG(score) OVER (PARTITION BY domain_id ORDER BY created_at)The partition keeps each domain’s history separate, while the ordering identifies its previous report. From those paired values, the analysis counts score declines of at least one, three, and five points, then calculates the share with any decline. Those measurements answer two separate questions:
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- How often does the current rule fire? Auriti reported seven fires across 1,963 pairs, or 0.36%.
- How often does the signal move in the monitored direction at all? He reported a decline in 6.2% of pairs.
The first number describes the deployed rule’s output; the second describes the opportunity available to a score-degradation alert. The case study reports these figures but does not provide independent verification or an audited dataset.
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Use the signal’s frequency to judge the upside of tuning
If the monitored event is common but the alert rarely fires, the threshold may be excluding many useful cases. If the event itself is rare, relaxing the threshold may raise the rate only modestly. The 6.2% figure in this case is not a prediction of what any particular revised rule would achieve: a new rule might still miss some declines. It does show why changing thresholds alone could not turn the alert into a frequent one on those report pairs.
That distinction prevents a common diagnostic mistake: treating every low fire rate as evidence that a threshold needs to be lowered. As Auriti put it, “Fire rate alone always says ‘tune me’ — it’s low, it looks fixable, and it will absorb as much time as you give it.” The quote captures the temptation, not a universal rule against tuning. Thresholds still matter; they simply cannot compensate for a signal that seldom moves in the target direction.
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Decide what to change after the backtest
Relax the threshold when the data supports it
If the historical pairs contain many declines that fall short of the current cutoff, test candidate thresholds against those same pairs. Compare the resulting number of alerts and the declines each rule would catch. Choose based on whether those additional alerts are useful, not on a target firing frequency alone.
Choose a more variable signal when the ceiling is too low
If a score barely changes from report to report, a score-drop alert will naturally have limited opportunities to fire. Auriti suggested considering signals with more week-to-week variation, including competitor position, citation presence, or crawler arrivals in logs. These are examples from his case study, not guaranteed substitutes: each measures a different condition and should match the problem the product needs to surface.
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Make “no change” an understandable result
When a monitored signal often stays stable, the product can present no change as a clear, reassuring outcome rather than making silence feel like a broken alert. This does not increase the signal’s frequency; it gives users a useful interpretation of what the monitoring found.
What this case study does—and does not—show
Auriti’s September 24, 2025, DEV Community article is a first-person account of one system. Its reported 0.36% firing rate and 6.2% decline ceiling illustrate how to separate threshold effects from event frequency, but they do not establish typical rates for score alerts, websites, or monitoring products. The practical lesson is to measure both the deployed rule and the underlying signal on the alert’s true comparison unit before deciding whether threshold tuning can solve the problem.
Read Juan Camilo Auriti’s case study on DEV Community.
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